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 Carbon Cycle And Ecosystems (329G): People
Junjie  Liu's Picture
Address:
Jet Propulsion Laboratory
M/S 233-200
4800 Oak Grove Drive
Pasadena, CA 91109
Phone:
818.354.0059
Email Contact:
Curriculum Vitae:

Junjie Liu

Dr. Junjie Liu's research interests broadly cover the development of data assimilation technique to better use satellite and ground-based observations to constrain model state variables, surface forcing and model parameters. Her current research focuses on understanding surface carbon budget from the "top-down" constraints, and the terrestrial biosphere dynamics by improving the process representation with the use of satellite observations and data assimilation technique. This will ultimately lead to the predictability of carbon cycle and climate variability on seasonal to decadal time scales.


Education
  • Ph. D, December 2007: University of Maryland-College Park.
  • M. S., Spring 2003: Nanjing Institute of Meteorology, China
  • B. S., 2000: Nanjing Institute of Meteorology, China.

Research Interests
  • CO2 source and sink estimation based on advanced data assimilation technique (i.e. ensemble Kalman filter, 4D-Var)
  • Surface CO2 flux attribution problem (how much CO2 is from anthropogenic activity and where it is from)
  • Observational constrains on model parameters of carbon climate model
  • Observation impact study to monitor the quality of observations; The decadal climate predictability of carbon cycle

Projects

CMS Flux (Carbon Monitoring System Flux) Icon CMS Flux (Carbon Monitoring System Flux)
Carbon Monitoring System Flux (CMS Flux) incorporates the full suite of NASA observational, modeling, and assimilation capabilities to attribute CO2 climate forcing to spatially resolved emissions.

NASA Carbon Cycle Science (CARBON) Icon NASA Carbon Cycle Science (CARBON)
Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) Phase II

OCO-2 - Orbiting Carbon Observatory Icon OCO-2 - Orbiting Carbon Observatory
The Orbiting Carbon Observatory-2 (OCO-2) is a mission designed to make precise, time-dependent global measurements of atmospheric carbon dioxide (CO2) from an Earth orbiting satellite.


Professional Experience
  • Research Scientist, Feb 2011-: Jet Propulsion Laboratory, Caltech
  • Assistant Researcher, Feb 2010-Feb2011: University of California, Berkeley
  • Research associate, Feb 2008-Feb 2010: University of California, Berkeley
  • Research associate, Dec 2007-Feb 2008: University of Maryland-College Park

Community Service
  • Reviewer for Scientific Report, Monthly Weather Review, Quarterly Journal of, Tellus, Climate Dynamics, Journal of Climate, Atmospheric Chemistry and Physics, and Geophysical Model Development
  • Reviewer for NASA, NOAA and NSF proposals

Selected Awards
  • JPL Voyager Award (2017)
  • NASA early career achievement award (2015)
  • NASA Group Achievement Award, Carbon Monitoring System Flux Pilot Project Team (2013)
  • Best Ph. D thesis award in Atmospheric and Oceanic Science department, University of Maryland, 2007
  • Second place student paper award for "Application of Local Ensemble Transform Kalman Filter: Perfect model experiments with NASA fvGCM" in AMS 86th annual meeting held in Atlanta, GA, Jan. 28-Feb. 3, 2006

Selected Publications
  1. Liu, J. et al 2017 Contrasting carbon cycle responses of the tropical continents to the 2015–2016 El Nino Science 358 eaam5690
  2. Eldering, A., Wennberg, P. O., Crisp, D., Schimel, D. S., Gunson, M. R., Chatterjee, A.,J. Liu, et al.(2017). The Orbiting Carbon Observatory‐2 early science investigations of regional carbon dioxide fluxes. Science, 358, eaam5745.
  3. Shi, M., Liu, J., Zhao, M., Yu, Y., & Saatchi, S. (2017). Mechanistic processes controlling persistent changes of forest canopy structure after 2005 Amazon drought. Journal of Geophysical Research: Biogeosciences, 122, 3378–3390. https://doi.org/10.1002/2017JG003966
  4. Mueller, K.J., J. Liu, W. McCarty, and R. Gelaro, 2017: An Adjoint-Based Forecast Impact from Assimilating MISR Winds into the GEOS-5 Data Assimilation and Forecasting System. Mon. Wea. Rev., 145, 4937–4947, https://doi.org/10.1175/MWR-D-17-0047.1
  5. Bowman, K. W., Liu, J., Bloom, A. A., Parazoo, N. C., Lee, M., Jiang, Z., … Wunch, D. (2017). Global and Brazilian carbon response to El Niño Modoki 2011–2010. Earth and Space Science, 4, 637–660. https://doi.org/10.1002/2016EA000204
  6. Basu, S., Baker, D. F., Chevallier, F., Patra, P. K., Liu, J., and Miller, J. B.: The Impact of Transport Model Differences on CO2 Surface Flux Estimates from OCO-2 Retrievals of Column Average CO2, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-1158, in review, 2017.
  7. Souri, A. H., Choi, Y., Pan, S., Curci, G., Nowlan, C. R., Janz, S. J., M. K. Kowalewski, J. Liu et al.(2018). First Top‐Down Estimates of Anthropogenic NOx Emissions Using High‐Resolution Airborne Remote Sensing Observations. Journal of Geophysical Research: Atmospheres, 123. https://doi.org/10.1002/2017JD028009
  8. Byrne, B., D. B. A. Jones, K. Strong, Z.‐C. Zeng, F. Deng, and J. Liu(2017), Sensitivity of CO2 surface flux constraints to observational coverage, J. Geophys. Res. Atmos., 122, 6672–6694, doi:10.1002/2016JD026164.
  9. Fischer, M. L., N. Parazoo, K. Brophy, X Cui, S. Jeong, J. Liu et al. (2017), Simulating estimation of California fossil fuel and biosphere carbon dioxide exchanges combining in situ tower and satellite column observations, J. Geophys. Res. Atmos., 122, doi:10.1002/2016JD025617.
  10. Fisher, J.B., Sikka, M., Huntzinger, D.N., Schwalm, C., Liu, J., 2016. 3-hourly temporal downscaling of monthly global terrestrial biosphere model net ecosystem exchange. Biogeosciences 13(14): 4271-4277.
  11. Liu, J., K. W. Bowman, and M. Lee (2016), Comparison between the Local Ensemble Transform Kalman Filter (LETKF) and 4D-Var in atmospheric CO2 flux inversion with the Goddard Earth Observing System-Chem model and the observation impact diagnostics from the LETKF, J. Geophys. Res. Atmos., 121, 13,066–13,087, doi:10.1002/2016JD025100.
  12. Liu, J., and K. Bowman (2016), A method for independent validation of surface fluxes from atmospheric inversion: Application to CO2, Geophys. Res. Lett., 43, doi:10.1002/2016GL067828.
  13. Liu, J., K. W. Bowman, and D. K. Henze (2015), Source-receptor relationships of column-average CO2 and implications for the impact of observations on flux inversions. J. Geophys. Res. Atmos., 120, 5214–5236. doi: 10.1002/2014JD022914.
  14. Worden, J. R., Turner, A. J., Bloom, A., Kulawik, S. S., Liu, J., Lee, M., Weidner, R.,
  15. Bowman, K., Frankenberg, C., Parker, R., and Payne, V. H.: Quantifying lower tropospheric methane concentrations using GOSAT near-IR and TES thermal IR measurements, Atmos. Meas. Tech., 8, 3433-3445, doi:10.5194/amt-8-3433-2015, 2015.
  16. Bousserez, N., D. K. Henze, A. Perkins, K. W. Bowman, M.Lee, J.Liu, D.B.A. Jones, F. Deng (2015), Improved analysis error covariance matrix estimates for variational inverse problems, Q. J. R. Meteorol. Soc., 141: 1906--1921, do:10.1002/qj.2495,
  17. Kuai, L., J. Worden, S. S. Kulawik, S. A. Montzka, and J. Liu (2014): Characterization of aura tropospheric emissions spectrometer carbonyl sulfide retrievals over ocean, Atmos. Meas. Tech., 7, 163-172, doi:10.5194/amt-7-163-2014.
  18. Miller, S. M., Hayek, M. N., Andrews, A. E., Fung, I., and Liu, J.: Biases in atmospheric CO2 estimates from correlated meteorology modeling errors, Atmos. Chem. Phys., 15, 2903-2914, doi:10.5194/acp-15-2903-2015, 2015.
  19. Ott, L. E., Steven Pawson, George J. Collatz, Watson W. Gregg, Dimitris Menemenlis, Holger Brix, Cecile S. Rousseaux, Kevin W. Bowman, Junjie Liu, Annmarie Eldering, Michael R. Gunson, and Stephan R. Kawa, 2015, Assessing the magnitude of CO2 flux uncertainty in atmospheric CO2 records using products from NASA’s Carbon Monitoring Flux Pilot Project, J. Geophys. Res. Atmos., 120, doi:10.1002/2014JD022411.
  20. Liu, J., Bowman, K., Lee, M., Henze, D., Bousserez, N., Brix, H., Collatz, G., Menemenlis, D., Ott, L., Pawson, S., Jones, D., Nassar, R.. Carbon monitoring system flux estimation and attribution: impact of ACOS-GOSAT XCO2 sampling on the inference of terrestrial biospheric sources and sinks. Tellus B, North America, 66, may. 2014. Available at: <http://www.tellusb.net/index.php/tellusb/article/view/22486>
  21. Parazoo, N. C., et al. (including Liu, J.) (2013), Interpreting seasonal changes in the carbon balance of southern Amazonia using measurements of XCO2 and chlorophyll fluorescence from GOSAT, Geophys. Res. Lett., 40, 2829–2833, doi:10.1002/grl.50452.
  22. Worden, J., et al. (including Liu, J.) (2013), El Niño, the 2006 Indonesian peat fires, and the distribution of atmospheric methane, Geophys. Res. Lett., 40, 4938–4943, doi:10.1002/grl.50937
  23. Liu, J., I. Fung, E. Kalnay, J.-S. Kang, E. T. Olsen, and L. Chen (2012), Simultaneous assimilation of AIRS Xco2 and meteorological observations in a carbon climate model with an ensemble Kalman filter, J. Geophys. Res., 117, D05309, doi:10.1029/2011JD016642.
  24. Kalnay, E., Y.  Ota, T. Miyoshi, J. Liu (2012), A simpler formulation of forecast sensitivity to observations: application to ensemble Kalman filters. Tellus A.
  25. Kang, J.-S., E. Kalnay, T. Miyoshi, J. Liu, and I. Fung (2012), Estimation of surface carbon fluxes with an advanced data assimilation methodology, J. Geophys. Res., 117, D24101, doi:10.1029/2012JD018259.
  26. Liu, J., I. Fung, E. Kalnay, and J.-S. Kang (2011), CO2 transport uncertainties from the uncertainties in meteorological fields, Geophys. Res. Lett., 38, L12808, doi:10.1029/2011GL047213.
  27. 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.
  28. Li, H., J. Liu, E. J. Fertig, E. Kalnay, E. Kostelich, and I. Szunyogh (2011), Improved analyses and forecasts with AIRS temperature retrievals using the Local Ensemble Transform Kalman Filter. J. of Tropopical Meteorology. 17, 43-49.
  29. Li, H., J. Liu, and E. Kalnay, 2010: Correction of ‘Estimating observation impact without adjoint model in an ensemble Kalman filter’. Quart. J. Roy. Meteor. Soc. 136, 1652-1654
  30. Liu, J., E. Kalnay, T. Miyoshi, and C. Cardinali, 2009: Analysis sensitivity calculation within an ensemble Kalman filter. Quart. J. Roy. Meteor. Soc. 135, 1842-1851
  31. Liu, J., H. Li, E. Kalnay, E.J. Kostelich, and I. Szunyogh, 2009: Univariate and Multivariate Assimilation of AIRS Humidity Retrievals with the Local Ensemble Transform Kalman Filter. Mon. Wea. Rev., 137, 3918–3932.
  32. Fertig, E. J., S.-J. Baek, B. R. Hunt, E. Ott, I. Szunyogh, J. A. Aravequia, E. Kalnay, H. Li, and J. Liu, 2009: Observation bias correction with an ensemble Kalman filter. Tellus A, 61, 210-226.
  33. Liu, J. and E. Kalnay, 2008: Estimating observation impact study without adjoint model in an ensemble Kalman filter. Quart. J. Roy. Meteor. Soc, 134, 1327-1335.
  34. Liu, J., E. J. Fertig, H. Li, I. Szunyogh, B. Hunt, E. Kalnay, E. J. Kostelich, and R. Todling, 2008: Comparison between Local Ensemble Transform Kalman Filter and PSAS in the NASA finite volume GCM: perfect model experiments. Nonlin. Processes in Geophys., 15, 645-659.
  35. Liu, J. and E. Kalnay, 2007:  Simple Doppler Wind Lidar (DWL) adaptive observation experiments with 3D-Var and an ensemble Kalman filter in a global primitive equations model. Geophys. Res. Lett., 34, L19808, doi: 10.1029/2007GL030707.
  36. Liu, J., Y-H. Ding, and J-H. He, 2003:  Analysis of typical Meiyu front structure in 1999.Acta Meteorological Sinica. 61, 291-301.

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