Atmosphere (대기)

Korean Meteorological Society (한국기상학회)
권호 표지
DOI QR코드

DOI QR Code

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

앙상블 칼만 필터 기반 탄소추적시스템의 아시아 지역 탄소 순환 진단에의 적용

  • Kim, JinWoong (Atmospheric Predictability and Data Assimilation Laboratory, Department of Atmospheric Sciences, Yonsei University) ;
  • Kim, Hyun Mee (Atmospheric Predictability and Data Assimilation Laboratory, Department of Atmospheric Sciences, Yonsei University) ;
  • Cho, Chun-Ho (Climate Research Division, National Institute of Meteorological Research, Korea Meteorological Administration)
  • 김진웅 (연세대학교 대기과학과, 대기예측성 및 자료동화 연구실) ;
  • 김현미 (연세대학교 대기과학과, 대기예측성 및 자료동화 연구실) ;
  • 조천호 (국립기상연구소 기후연구과)
  • Received : 2012.10.12
  • Accepted : 2012.11.20
  • Published : 2012.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

$CO_2$ 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 $CO_2$ 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 $CO_2$ 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 $CO_2$ observations. The root mean square error of the model $CO_2$ calculated by the posterior flux is less than the model $CO_2$ calculated by the prior flux, implying that CarbonTracker based on the ensemble Kalman filter works appropriately for the Asia region.

Keywords

References

  1. Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884-2903. https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2
  2. Baker, D. F., S. C. Doney, and D. S. Schimel, 2006: Variational data assimilation for atmospheric $CO_2$. Tellus, Ser. B, 58, 359-365. https://doi.org/10.1111/j.1600-0889.2006.00218.x
  3. Bishop, C. H., B. J. Etherton, and S. J. Majumdar, 2001: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev., 129, 420-436. https://doi.org/10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2
  4. Chevallier, F., R. J. Engelen, C. Carouge, T. J. Conway, P. Peylin, C. Pickett-Heaps, M. Ramonet, P. J. Rayner, and I. Xueref-Remy, 2009a: AIRS-based versus flask-based estimation of carbon surface fluxes. J. Geophys. Res., 114, D20303, doi:10.1029/2009JD012311.
  5. Chevallier, F., F., S. Maksyutov, P. Bousquet, F.-M. Breon, R. Saito, Y. Yoshida, and T. Yokota, 2009b: On the accuracy of the $CO_2$ surface fluxes to be estimated from the GOSAT observations. Geophys. Res. Lett., 36, L19807, doi:10.1029/2009GL040108.
  6. Ciais, P. and Coauthors, 2005: Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature, 437, 529-533. https://doi.org/10.1038/nature03972
  7. Dee, D. P., 1995: On-line estimation of error covariance parameters for atmospheric data assimilation. Mon. Wea. Rev., 123, 1128-1144. https://doi.org/10.1175/1520-0493(1995)123<1128:OLEOEC>2.0.CO;2
  8. Engelen, R. J., S. Serrar, and F. Chevallier, 2009: Fourdimensional data assimilation of atmospheric $CO_2$ using AIRS observations. J. Geophys. Res., 114, D03303, doi:10.1029/2008JD010739.
  9. Enting, I., 2002: Inverse Problems in Atmospheric Constituent Transport. Cambridege Univ. Press, New York, doi:10.1017/CBO9780511535741.
  10. Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10143-10162. https://doi.org/10.1029/94JC00572
  11. Feng, L., P. I. Palmer, H. Bosch, and S. Dance, 2009: Estimating surface $CO_2$ fluxes from space-born $CO_2$ dry air mole fraction observations using an ensemble Kalman Filter. Atmos. Chem. Phys., 9, 2619-2633. https://doi.org/10.5194/acp-9-2619-2009
  12. Gurney, K. R. and Coauthors, 2002: Towards robust regional estimates of $CO_2$ sources and sinks using atmospheric transport models. Nature, 415, 626-630. https://doi.org/10.1038/415626a
  13. Hamill, T. M. 2001: Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev., 129, 550-560. https://doi.org/10.1175/1520-0493(2001)129<0550:IORHFV>2.0.CO;2
  14. Hou, D., E. Kalnay, and K. K. Droegemeier, 2001: Objective verification of the SAMEX'98 ensemble forecasts. Mon. Wea. Rev., 128, 73-91.
  15. Houtekamer, P. L. and H. L. Mitchell, 2001: A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev., 129, 123-137. https://doi.org/10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;2
  16. Houtekamer, P. L., H. L. Mitchell, G. Pellerin, M. Buehner, M. Charron, L. Speak, and B. Hansen, 2005: Atmospheric data assimilation with an ensemble Kalman filter: Results with real observations. Mon. Wea. Rev., 133, 604-620. https://doi.org/10.1175/MWR-2864.1
  17. Hunt, B. R., E. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D, 230, 112-126. https://doi.org/10.1016/j.physd.2006.11.008
  18. Kang, J.-S., E. Kalnay, J. Liu, I. Fung, T. Miyoshi, and K. Ide, 2011: "Variable localization" in an ensemble Kalman filter: Application to the carbon cycle data assimilation. J. Geophys. Res., 116, D09110, doi:10.1029/2010JD014673.
  19. Krol, M., S. Houweling, B. Bregman, M. Van Den Broek, A. Segers, P. Van Velthoven, W. Peters, F. Dentener, and P. Bergamaschi, 2005: The two-way nested global chemistry-transport zoom model TM5: Algorithm and applications. Atmos. Chem. Phys., 5, 417-432. https://doi.org/10.5194/acp-5-417-2005
  20. Masarie, K. A., G. Pétron, A. Andrews, L. Bruhwiler, T. J. Conway, A. R. Jacobson, J. B. Miller, P. P. Tans, D. E. Worthy, and W. Peters, 2011: Impact of $CO_2$ measurement bias on CarbonTracker surface flux estimates. J. Geophys. Res., 116, D17305, doi:10.1029/2011JD016270.
  21. Meng, Z. and F. Zhang, 2008: 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, 3671-3682. https://doi.org/10.1175/2008MWR2270.1
  22. Olson, S. C., J. A. Watts, and L. J. Allison: Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation: A Database. NDP017, Carbon Dioxide Info. Analy. Cent., Oak Ridge Nat. Lab., Oak Rideger, Tenn., 1985.
  23. Ott, E., B. R. Hunt, I. Szunyogh, A. V. Zimin, E. J. Kostelich, M. Corazza, E. Kalnay, D. J. Patil, and J. A. Yorke, 2004: Estimating the state of large spatiotemporally chaotic systems. Phys. Lett. A, 330, 365-370. https://doi.org/10.1016/j.physleta.2004.08.004
  24. Park, J. I. and H. M. Kim, 2010: Typhoon Wukong, 2006: Prediction Based on The Ensemble Kalman Filter and Ensemble Sensitivity Analysis (in Korean with English abstract). Atmosphere, 20, 287-306.
  25. Peters, W. and Coauthors, 2004: Toward regional-scale modeling using the two-way nested global model TM5: Characterizatino of transport using $SF_6$. J. Geophys. Res., 109, D19314, doi:10.1029/2004JD005020.
  26. Peters, W. and Coauthors, 2010: Seven years of recent European net terrestrial carbon dioxide exchange constrained by atmospheric observations. Glob. Change Bio., 16, 1317-1337, doi:10.1111/j.1365-2486.2009.02078.x
  27. Peters, W. and Coauthors, 2007: An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker. Proc. Nat. Acad. Sci. U.S.A., 104, 18925-18930. https://doi.org/10.1073/pnas.0708986104
  28. Peters, W., J. B. Miller, J. S. Whitaker, A. S. Denning, A. Hirsch, M. C. Krol, D. Zupanski, L. Bruhwiler, and P. P. Tans, 2005: An ensemble data assimilation system to estimate $CO_2$ surface fluxes from atmospheric trace gas observations. J. Geophys. Res., 110, D24304, doi:10.1029/2005JD006157.
  29. van der Werf, G. R., J. T. Randerson, L. Giglio, G. J. Collatz, P. S. Kasibhatla, and A. F. Arellano Jr. 2006: Interannual variability of global biomass burning emissions from 1997 to 2004. Atmos. Chem. Phys., 6, 3423-3441. https://doi.org/10.5194/acp-6-3423-2006
  30. Whitaker, J. S. and T.M. Hamill, 2002: Ensemble Data Assimilation without Perturbed Observations. Mon. Wea. Rev., 130, 1913-1924. https://doi.org/10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2
  31. Whitaker, J. S., T.M. Hamill, X. Wei, Y. Song, and Z. Toth, 2008: Ensemble data assimilation with the NCEP global forecast system. Mon. Wea. Rev., 136, 463-482. https://doi.org/10.1175/2007MWR2018.1
  32. Zupanski, M., 2005: Maximum likelihood ensemble filter: Theoretical aspects. Mon. Wea. Rev., 133, 1710-1726. https://doi.org/10.1175/MWR2946.1

Cited by

  1. flux in an ensemble Kalman filter vol.14, pp.9, 2014, https://doi.org/10.5194/acpd-14-13561-2014
  2. flux in an ensemble Kalman filter vol.14, pp.24, 2014, https://doi.org/10.5194/acp-14-13515-2014
  3. The effect of optimization and the nesting domain on carbon flux analyses in Asia using a carbon tracking system based on the ensemble Kalman filter vol.50, pp.3, 2014, https://doi.org/10.1007/s13143-014-0020-y
  4. Impact of Siberian observations on the optimization of surface CO<sub>2</sub> flux vol.17, pp.4, 2017, https://doi.org/10.5194/acp-17-2881-2017
  5. Effect of Data Assimilation Parameters on The Optimized Surface CO2 Flux in Asia vol.54, pp.1, 2018, https://doi.org/10.1007/s13143-017-0049-9