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앙상블 칼만 필터 기반 탄소추적시스템의 아시아 지역 탄소 순환 진단에의 적용

Application of Carbon Tracking System based on Ensemble Kalman Filter on the Diagnosis of Carbon Cycle in Asia

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초록

CO2 is the most important trace gas related to climate change. Therefore,understanding surface carbon sources and sinks is important when seeking to estimate the impact of CO2 on the environment and climate. CarbonTracker, developed by NOAA, is an inverse modeling system that estimates surface carbon fluxes using an ensemble Kalman filter with atmospheric CO2 measurements as a constraint. In this study, to investigate the capability of CarbonTracker as an analysis tool for estimating surface carbon fluxes in Asia, an experiment with a nesting domain centered in Asia is performed. In general, the results show that setting a nesting domain centered in Asia region enables detailed estimations of surface carbon fluxes in Asia. From a rank histogram, the prior ensemble spread verified at observational sites located in Asia is well represented with a relatively flat rank histogram. The posterior flux in the Eurasian Boreal and Eurasian Temperate regions is well analyzed with proper seasonal cycles and amplitudes. On the other hand, in tropical regions of Asia, the posterior flux does not differ greatly from the prior flux due to fewer CO2 observations. The root mean square error of the model CO2 calculated by the posterior flux is less than the model CO2 calculated by the prior flux, implying that CarbonTracker based on the ensemble Kalman filter works appropriately for the Asia region.

Abstract

CO2 is the most important trace gas related to climate change. Therefore,understanding surface carbon sources and sinks is important when seeking to estimate the impact of CO2 on the environment and climate. CarbonTracker, developed by NOAA, is an inverse modeling system that estimates surface carbon fluxes using an ensemble Kalman filter with atmospheric CO2 measurements as a constraint. In this study, to investigate the capability of CarbonTracker as an analysis tool for estimating surface carbon fluxes in Asia, an experiment with a nesting domain centered in Asia is performed. In general, the results show that setting a nesting domain centered in Asia region enables detailed estimations of surface carbon fluxes in Asia. From a rank histogram, the prior ensemble spread verified at observational sites located in Asia is well represented with a relatively flat rank histogram. The posterior flux in the Eurasian Boreal and Eurasian Temperate regions is well analyzed with proper seasonal cycles and amplitudes. On the other hand, in tropical regions of Asia, the posterior flux does not differ greatly from the prior flux due to fewer CO2 observations. The root mean square error of the model CO2 calculated by the posterior flux is less than the model CO2 calculated by the prior flux, implying that CarbonTracker based on the ensemble Kalman filter works appropriately for the Asia region.

참고문헌 (32)

  1. Anderson, J. L., "An ensemble adjustment Kalman filter for data assimilation", Mon. Wea. Rev, 129, pp.2884~2903 (2001)
  2. Baker, D. F., "Variational data assimilation for atmospheric CO2", Tellus, Ser. B, 58, pp.359~365 (2006)
  3. Bishop, C. H., "Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects", Mon. Wea. Rev, 129, pp.420~436 (2001)
  4. Chevallier, F., "AIRS-based versus flaskbased estimation of carbon surface fluxes", J. Geophys. Res, 114, pp.D20303~ (2009)[DOI]
  5. Chevallier, F, F., "On the accuracy of the CO2 surface fluxes to be estimated from the GOSAT observations", Geophys. Res. Lett., 36, pp.L19807~ (2009)[DOI]
  6. Ciais, P, "Europe-wide reduction in primary productivity caused by the heat and drought in 2003", Nature, 437, pp.529~533 (2003)
  7. Dee, D. P., "On-line estimation of error covariance parameters for atmospheric data assimilation", Mon. Wea. Rev, 123, pp.1128~1144 (1995)
  8. Engelen, R. J., "Fourdimensional data assimilation of atmospheric CO2 using AIRS observations", J. Geophys. Res, 114, pp.D03303~ (2009)[DOI]
  9. Enting, I., "Inverse Problems in Atmospheric Constituent Transport" (2002), Cambridege Univ. Press
  10. Evensen, G., "Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics", J. Geophys. Res, 99, pp.10143~10162 (1994)
  11. Feng, L., "Estimating surface CO2 fluxes from space-born CO2 dry air mole fraction observations using an ensemble Kalman Filter", Atmos. Chem. Phys, 9, pp.2619~2633 (2009)
  12. Gurney, K. R., "Towards robust regional estimates of CO2 sources and sinks using atmospheric transport models", Nature, 415, pp.626~630 (2002)
  13. Hamill, T. M., "Interpretation of rank histograms for verifying ensemble forecasts", Mon. Wea. Rev, 129, pp.550~560 (2001)
  14. Hou, D., "Objective verification of the SAMEX'98 ensemble forecasts", Mon. Wea. Rev, 128, pp.73~91 (2001)
  15. Houtekamer, P. L., "A sequential ensemble Kalman filter for atmospheric data assimilation", Mon. Wea. Rev., 129, pp.123~137 (2001)
  16. Houtekamer, P. L, "Atmospheric data assimilation with an ensemble Kalman filter: Results with real observations", Mon. Wea. Rev, 133, pp.604~620 (2005)
  17. Hunt, B. R., "Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter", Physica D, 230, pp.112~126 (2007)
  18. Kang, J.-S, "“Variable localization” in an ensemble Kalman filter: Application to the carbon cycle data assimilation", J. Geophys. Res, 116, pp.D09110,~ (2011)[DOI]
  19. Krol, M., "The two-way nested global chemistry-transport zoom model TM5: Algorithm and applications", Atmos. Chem. Phys, 5, pp.417~432 (2005)
  20. Masarie, K. A., "Impact of CO2 measurement bias on CarbonTracker surface flux estimates", J. Geophys. Res., 116, pp.D17305~ (2011)[DOI]
  21. Meng, Z, "Test of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part IV: performance over a warm-season month of June 2003", Mon. Wea. Rev, 136, pp.3671~3682 (2003)
  22. Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation: A Database, "Olson, S. C.", Carbon Dioxide Info. Analy. Cent., Oak Ridge Nat. Lab., Oak Rideger, Tenn, ~, pp.NDP017 (1985)
  23. Ott, E., "Estimating the state of large spatiotemporally chaotic systems", Phys. Lett. A, 330, pp.365~370 (2004)
  24. 박종임, "앙상블 칼만 필터를 이용한 태풍 우쿵 (200610) 예측과 앙상블 민감도 분석", 대기, 20(3), pp.287~306 (2010), 한국기상학회
  25. Peters, W., "Toward regional-scale modeling using the two-way nested global model TM5: Characterizatino of transport using SF6", J. Geophys. Res, 109, pp.D19314~ (2004)[DOI]
  26. Peters, W, "Seven years of recent European net terrestrial carbon dioxide exchange constrained by atmospheric observations", Glob. Change Bio, 16, pp.1317~1337 (2010)[DOI]
  27. Peters, W, "An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker", Proc. Nat. Acad. Sci. U.S.A., 104, pp.18925~18930 (2007)
  28. Peters, W, "An ensemble data assimilation system to estimate CO2 surface fluxes from atmospheric trace gas observations", J. Geophys. Res, 110, pp.D24304~ (2005)[DOI]
  29. van der Werf, G. R, "Interannual variability of global biomass burning emissions from 1997 to 2004", Atmos. Chem. Phys, 6, pp.3423~3441 (2004)
  30. Whitaker, J. S, "Ensemble Data Assimilation without Perturbed Observations", Mon. Wea. Rev., 130, pp.1913~1924 (2002)
  31. Whitaker, J. S, "Ensemble data assimilation with the NCEP global forecast system", Mon. Wea. Rev., 136, pp.463~482 (2008)
  32. Zupanski, M., "Maximum likelihood ensemble filter: Theoretical aspects", Mon. Wea. Rev, 133, pp.1710~1726 (2005)

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