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Title: Estimating surface carbon fluxes based on a local ensemble transform Kalman filter with a short assimilation window and a long observation window: an observing system simulation experiment test in GEOS-Chem 10.1

Abstract

We developed a carbon data assimilation system to estimate surface carbon fluxes using the local ensemble transform Kalman filter (LETKF) and atmospheric transport model GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological field based on the Goddard Earth Observing System model,version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. (2011, 2012), who estimated the surface carbon fluxes in an observing system simulation experiment (OSSE) as evolving parameters in the assimilation of the atmospheric CO 2, using a short assimilation window of 6 h. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO 2 transport. After proposing new techniques such as “variablelocalization”, and increased observation weights near the surface, they obtained accurate surface carbon fluxes at grid-point resolution. We developed a new version of the local ensemble transform Kalman filter related to the “running-in-place”(RIP) method used to accelerate the spin-up of ensemble Kalman filter (EnKF) data assimilation(Kalnay and Yang, 2010; Wang et al., 2013; Yang et al., 2012). Like RIP, the new assimilation system uses the “no cost smoothing” algorithm for the LETKF (Kalnay et al., 2007b), which allows shifting the Kalmanmore » filter solution forward or backward within an assimilation window at no cost. In the new scheme a long “observation window” (e.g., 7 d or longer) is used to design a LETKF ensemble at 7 d. Then, the RIP smoother is used to obtain an accurate final analysis at 1 d. This new approach has the advantage of being based on a short assimilation window, which makes it more accurate,and of having been exposed to the future 7 d observations, which improves the analysis and accelerates the spin-up. The assimilation and observation windows are then shifted forward by 1 d, and the process is repeated.This reduces significantly the analysis error, suggesting that the newly developed assimilation method can be used with other Earth system models,especially in order to make greater use of observations in conjunction with models.« less

Authors:
 [1]; ORCiD logo [2]; ORCiD logo [2];  [3];  [4]; ORCiD logo [5]
  1. Univ. of Maryland, College Park, MD (United States); Texas A & M Univ., College Station, TX (United States)
  2. Univ. of Maryland, College Park, MD (United States)
  3. Joint Global Change Research Institute/PNNL, College Park, MD (United States)
  4. Univ. of East Anglia, Norwich (United Kingdom)
  5. Chinese Academy of Sciences (CAS), Beijing (China)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1548274
Report Number(s):
PNNL-SA-129693
Journal ID: ISSN 1991-9603
Grant/Contract Number:  
AC05-76RL01830
Resource Type:
Accepted Manuscript
Journal Name:
Geoscientific Model Development (Online)
Additional Journal Information:
Journal Name: Geoscientific Model Development (Online); Journal Volume: 12; Journal Issue: 7; Journal ID: ISSN 1991-9603
Publisher:
European Geosciences Union
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; Carbon Data Assimilation; Surface Carbon Flux; LETKF

Citation Formats

Liu, Yun, Kalnay, Eugenia, Zeng, Ning, Asrar, Ghassem, Chen, Zhaohui, and Jia, Binghao. Estimating surface carbon fluxes based on a local ensemble transform Kalman filter with a short assimilation window and a long observation window: an observing system simulation experiment test in GEOS-Chem 10.1. United States: N. p., 2019. Web. doi:10.5194/gmd-12-2899-2019.
Liu, Yun, Kalnay, Eugenia, Zeng, Ning, Asrar, Ghassem, Chen, Zhaohui, & Jia, Binghao. Estimating surface carbon fluxes based on a local ensemble transform Kalman filter with a short assimilation window and a long observation window: an observing system simulation experiment test in GEOS-Chem 10.1. United States. doi:10.5194/gmd-12-2899-2019.
Liu, Yun, Kalnay, Eugenia, Zeng, Ning, Asrar, Ghassem, Chen, Zhaohui, and Jia, Binghao. Fri . "Estimating surface carbon fluxes based on a local ensemble transform Kalman filter with a short assimilation window and a long observation window: an observing system simulation experiment test in GEOS-Chem 10.1". United States. doi:10.5194/gmd-12-2899-2019. https://www.osti.gov/servlets/purl/1548274.
@article{osti_1548274,
title = {Estimating surface carbon fluxes based on a local ensemble transform Kalman filter with a short assimilation window and a long observation window: an observing system simulation experiment test in GEOS-Chem 10.1},
author = {Liu, Yun and Kalnay, Eugenia and Zeng, Ning and Asrar, Ghassem and Chen, Zhaohui and Jia, Binghao},
abstractNote = {We developed a carbon data assimilation system to estimate surface carbon fluxes using the local ensemble transform Kalman filter (LETKF) and atmospheric transport model GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological field based on the Goddard Earth Observing System model,version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. (2011, 2012), who estimated the surface carbon fluxes in an observing system simulation experiment (OSSE) as evolving parameters in the assimilation of the atmospheric CO2, using a short assimilation window of 6 h. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO2 transport. After proposing new techniques such as “variablelocalization”, and increased observation weights near the surface, they obtained accurate surface carbon fluxes at grid-point resolution. We developed a new version of the local ensemble transform Kalman filter related to the “running-in-place”(RIP) method used to accelerate the spin-up of ensemble Kalman filter (EnKF) data assimilation(Kalnay and Yang, 2010; Wang et al., 2013; Yang et al., 2012). Like RIP, the new assimilation system uses the “no cost smoothing” algorithm for the LETKF (Kalnay et al., 2007b), which allows shifting the Kalman filter solution forward or backward within an assimilation window at no cost. In the new scheme a long “observation window” (e.g., 7 d or longer) is used to design a LETKF ensemble at 7 d. Then, the RIP smoother is used to obtain an accurate final analysis at 1 d. This new approach has the advantage of being based on a short assimilation window, which makes it more accurate,and of having been exposed to the future 7 d observations, which improves the analysis and accelerates the spin-up. The assimilation and observation windows are then shifted forward by 1 d, and the process is repeated.This reduces significantly the analysis error, suggesting that the newly developed assimilation method can be used with other Earth system models,especially in order to make greater use of observations in conjunction with models.},
doi = {10.5194/gmd-12-2899-2019},
journal = {Geoscientific Model Development (Online)},
number = 7,
volume = 12,
place = {United States},
year = {2019},
month = {7}
}

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Works referenced in this record:

Accelerating the EnKF Spinup for Typhoon Assimilation and Prediction
journal, August 2012

  • Yang, Shu-Chih; Kalnay, Eugenia; Miyoshi, Takemasa
  • Weather and Forecasting, Vol. 27, Issue 4
  • DOI: 10.1175/WAF-D-11-00153.1

Inverse modeling of annual atmospheric CO 2 sources and sinks: 1. Method and control inversion
journal, November 1999

  • Bousquet, P.; Ciais, P.; Peylin, P.
  • Journal of Geophysical Research: Atmospheres, Vol. 104, Issue D21
  • DOI: 10.1029/1999JD900342

“Variable localization” in an ensemble Kalman filter: Application to the carbon cycle data assimilation
journal, January 2011

  • Kang, Ji-Sun; Kalnay, Eugenia; Liu, Junjie
  • Journal of Geophysical Research, Vol. 116, Issue D9
  • DOI: 10.1029/2010JD014673

Analysis Scheme in the Ensemble Kalman Filter
journal, June 1998


Global Carbon Budget 2016
journal, January 2016

  • Le Quéré, Corinne; Andrew, Robbie M.; Canadell, Josep G.
  • Earth System Science Data, Vol. 8, Issue 2
  • DOI: 10.5194/essd-8-605-2016

Transcom 3 inversion intercomparison: Model mean results for the estimation of seasonal carbon sources and sinks: T3 SEASONAL RESULTS
journal, January 2004

  • Gurney, Kevin Robert; Law, Rachel M.; Denning, A. Scott
  • Global Biogeochemical Cycles, Vol. 18, Issue 1
  • DOI: 10.1029/2003GB002111

A local ensemble Kalman filter for atmospheric data assimilation
journal, October 2004


AIRS-based versus flask-based estimation of carbon surface fluxes
journal, January 2009

  • Chevallier, Frédéric; Engelen, Richard J.; Carouge, Claire
  • Journal of Geophysical Research, Vol. 114, Issue D20
  • DOI: 10.1029/2009JD012311

Ensemble Data Assimilation with the NCEP Global Forecast System
journal, February 2008

  • Whitaker, Jeffrey S.; Hamill, Thomas M.; Wei, Xue
  • Monthly Weather Review, Vol. 136, Issue 2
  • DOI: 10.1175/2007MWR2018.1

4-D-Var or ensemble Kalman filter?
journal, January 2007


Response to the discussion on “4-D-Var or EnKF?” by Nils Gustafsson
journal, January 2007


EnKF and Hybrid Gain Ensemble Data Assimilation. Part II: EnKF and Hybrid Gain Results
journal, December 2015

  • Bonavita, Massimo; Hamrud, Mats; Isaksen, Lars
  • Monthly Weather Review, Vol. 143, Issue 12
  • DOI: 10.1175/MWR-D-15-0071.1

Technical Note: Adapting a fixed-lag Kalman smoother to a geostatistical atmospheric inversion framework
journal, January 2008


An ensemble data assimilation system to estimate CO2 surface fluxes from atmospheric trace gas observations
journal, January 2005

  • Peters, W.; Miller, J. B.; Whitaker, J.
  • Journal of Geophysical Research, Vol. 110, Issue D24
  • DOI: 10.1029/2005JD006157

How strong is carbon cycle-climate feedback under global warming?
journal, January 2004


An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker
journal, November 2007

  • Peters, W.; Jacobson, A. R.; Sweeney, C.
  • Proceedings of the National Academy of Sciences, Vol. 104, Issue 48
  • DOI: 10.1073/pnas.0708986104

Estimating surface CO 2 fluxes from space-borne CO 2 dry air mole fraction observations using an ensemble Kalman Filter
journal, January 2009

  • Feng, L.; Palmer, P. I.; Bösch, H.
  • Atmospheric Chemistry and Physics, Vol. 9, Issue 8
  • DOI: 10.5194/acp-9-2619-2009

Variational data assimilation for atmospheric CO 2
journal, January 2006


Estimation of global CO 2 fluxes at regional scale using the maximum likelihood ensemble filter
journal, January 2008

  • Lokupitiya, R. S.; Zupanski, D.; Denning, A. S.
  • Journal of Geophysical Research, Vol. 113, Issue D20
  • DOI: 10.1029/2007JD009679

Estimation of surface carbon fluxes with an advanced data assimilation methodology: SURFACE CO
journal, December 2012

  • Kang, Ji-Sun; Kalnay, Eugenia; Miyoshi, Takemasa
  • Journal of Geophysical Research: Atmospheres, Vol. 117, Issue D24
  • DOI: 10.1029/2012JD018259

Accelerating the spin-up of Ensemble Kalman Filtering
journal, July 2010

  • Kalnay, Eugenia; Yang, Shu-Chih
  • Quarterly Journal of the Royal Meteorological Society, Vol. 136, Issue 651
  • DOI: 10.1002/qj.652

Terrestrial mechanisms of interannual CO 2 variability : INTERANNUAL CO
journal, March 2005

  • Zeng, N.; Mariotti, A.; Wetzel, P.
  • Global Biogeochemical Cycles, Vol. 19, Issue 1
  • DOI: 10.1029/2004GB002273

Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter
journal, June 2007

  • Hunt, Brian R.; Kostelich, Eric J.; Szunyogh, Istvan
  • Physica D: Nonlinear Phenomena, Vol. 230, Issue 1-2
  • DOI: 10.1016/j.physd.2006.11.008

Improving the temporal and spatial distribution of CO 2 emissions from global fossil fuel emission data sets : SCALING OF FOSSIL FUEL CO
journal, January 2013

  • Nassar, Ray; Napier-Linton, Louis; Gurney, Kevin R.
  • Journal of Geophysical Research: Atmospheres, Vol. 118, Issue 2
  • DOI: 10.1029/2012JD018196

Global sea–air CO2 flux based on climatological surface ocean pCO2, and seasonal biological and temperature effects
journal, January 2002

  • Takahashi, Taro; Sutherland, Stewart C.; Sweeney, Colm
  • Deep Sea Research Part II: Topical Studies in Oceanography, Vol. 49, Issue 9-10
  • DOI: 10.1016/S0967-0645(02)00003-6

EnKF and Hybrid Gain Ensemble Data Assimilation. Part I: EnKF Implementation
journal, December 2015

  • Hamrud, Mats; Bonavita, Massimo; Isaksen, Lars
  • Monthly Weather Review, Vol. 143, Issue 12
  • DOI: 10.1175/MWR-D-14-00333.1

A multiyear, global gridded fossil fuel CO 2 emission data product: Evaluation and analysis of results : GLOBAL FOSSIL FUEL CO2 EMISSIONS
journal, September 2014

  • Asefi-Najafabady, S.; Rayner, P. J.; Gurney, K. R.
  • Journal of Geophysical Research: Atmospheres, Vol. 119, Issue 17
  • DOI: 10.1002/2013JD021296

The impact of transport model differences on CO 2 surface flux estimates from OCO-2 retrievals of column average CO 2
journal, January 2018

  • Basu, Sourish; Baker, David F.; Chevallier, Frédéric
  • Atmospheric Chemistry and Physics, Vol. 18, Issue 10
  • DOI: 10.5194/acp-18-7189-2018

Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation
journal, October 2001

  • Bey, Isabelle; Jacob, Daniel J.; Yantosca, Robert M.
  • Journal of Geophysical Research: Atmospheres, Vol. 106, Issue D19
  • DOI: 10.1029/2001JD000807

The Orbiting Carbon Observatory (OCO) mission
journal, January 2004


Global carbon budget 2014
journal, January 2015

  • Le Quéré, C.; Moriarty, R.; Andrew, R. M.
  • Earth System Science Data, Vol. 7, Issue 1
  • DOI: 10.5194/essd-7-47-2015

CO<sub>2</sub> flux history 1982–2001 inferred from atmospheric data using a global inversion of atmospheric transport
journal, January 2003

  • Rödenbeck, C.; Houweling, S.; Gloor, M.
  • Atmospheric Chemistry and Physics, Vol. 3, Issue 6
  • DOI: 10.5194/acp-3-1919-2003

Carbon flux bias estimation employing Maximum Likelihood Ensemble Filter (MLEF)
journal, January 2007

  • Zupanski, Dusanka; Denning, A. Scott; Uliasz, Marek
  • Journal of Geophysical Research, Vol. 112, Issue D17
  • DOI: 10.1029/2006JD008371

The ACOS CO 2 retrieval algorithm – Part 1: Description and validation against synthetic observations
journal, January 2012

  • O&apos;Dell, C. W.; Connor, B.; Bösch, H.
  • Atmospheric Measurement Techniques, Vol. 5, Issue 1
  • DOI: 10.5194/amt-5-99-2012

Carbon source/sink information provided by column CO 2 measurements from the Orbiting Carbon Observatory
journal, January 2010

  • Baker, D. F.; Bösch, H.; Doney, S. C.
  • Atmospheric Chemistry and Physics, Vol. 10, Issue 9
  • DOI: 10.5194/acp-10-4145-2010

An Ensemble Adjustment Kalman Filter for Data Assimilation
journal, December 2001


A Local Least Squares Framework for Ensemble Filtering
journal, April 2003


Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics
journal, January 1994

  • Evensen, Geir
  • Journal of Geophysical Research, Vol. 99, Issue C5
  • DOI: 10.1029/94JC00572

Ensemble Kalman Filtering
conference, September 2007


Data Assimilation Using an Ensemble Kalman Filter Technique
journal, March 1998


An Adaptive Ensemble Kalman Filter
journal, January 2000


The Hybrid Local Ensemble Transform Kalman Filter
journal, June 2014


Ensemble Square Root Filters*
journal, July 2003


An iterative ensemble square root filter and tests with simulated radar data for storm-scale data assimilation: Testing an Iterative Procedure for an Ensemble Square Root Filter
journal, January 2013

  • Wang, Shizhang; Xue, Ming; Schenkman, Alexander D.
  • Quarterly Journal of the Royal Meteorological Society, Vol. 139, Issue 676
  • DOI: 10.1002/qj.2077

Ensemble Data Assimilation without Perturbed Observations
journal, July 2002


    Works referencing / citing this record:

    The Orbiting Carbon Observatory (OCO) mission
    journal, January 2004


    Carbon source/sink information provided by column CO 2 measurements from the Orbiting Carbon Observatory
    journal, January 2010

    • Baker, D. F.; Bösch, H.; Doney, S. C.
    • Atmospheric Chemistry and Physics, Vol. 10, Issue 9
    • DOI: 10.5194/acp-10-4145-2010

    Ensemble Kalman Filtering
    conference, September 2007


    Accelerating the spin-up of Ensemble Kalman Filtering
    journal, July 2010

    • Kalnay, Eugenia; Yang, Shu-Chih
    • Quarterly Journal of the Royal Meteorological Society, Vol. 136, Issue 651
    • DOI: 10.1002/qj.652

    Estimating surface CO 2 fluxes from space-borne CO 2 dry air mole fraction observations using an ensemble Kalman Filter
    journal, January 2009

    • Feng, L.; Palmer, P. I.; Bösch, H.
    • Atmospheric Chemistry and Physics, Vol. 9, Issue 8
    • DOI: 10.5194/acp-9-2619-2009

    CO<sub>2</sub> flux history 1982–2001 inferred from atmospheric data using a global inversion of atmospheric transport
    journal, January 2003

    • Rödenbeck, C.; Houweling, S.; Gloor, M.
    • Atmospheric Chemistry and Physics, Vol. 3, Issue 6
    • DOI: 10.5194/acp-3-1919-2003

    The impact of transport model differences on CO 2 surface flux estimates from OCO-2 retrievals of column average CO 2
    journal, January 2018

    • Basu, Sourish; Baker, David F.; Chevallier, Frédéric
    • Atmospheric Chemistry and Physics, Vol. 18, Issue 10
    • DOI: 10.5194/acp-18-7189-2018

    Response to the discussion on “4-D-Var or EnKF?” by Nils Gustafsson
    journal, January 2007


    4-D-Var or ensemble Kalman filter?
    journal, January 2007


    Global Carbon Budget 2016
    journal, January 2016

    • Le Quéré, Corinne; Andrew, Robbie M.; Canadell, Josep G.
    • Earth System Science Data, Vol. 8, Issue 2
    • DOI: 10.5194/essd-8-605-2016

    A local ensemble Kalman filter for atmospheric data assimilation
    journal, October 2004


    An iterative ensemble square root filter and tests with simulated radar data for storm-scale data assimilation: Testing an Iterative Procedure for an Ensemble Square Root Filter
    journal, January 2013

    • Wang, Shizhang; Xue, Ming; Schenkman, Alexander D.
    • Quarterly Journal of the Royal Meteorological Society, Vol. 139, Issue 676
    • DOI: 10.1002/qj.2077

    The ACOS CO 2 retrieval algorithm – Part 1: Description and validation against synthetic observations
    journal, January 2012

    • O&apos;Dell, C. W.; Connor, B.; Bösch, H.
    • Atmospheric Measurement Techniques, Vol. 5, Issue 1
    • DOI: 10.5194/amt-5-99-2012

    An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker
    journal, November 2007

    • Peters, W.; Jacobson, A. R.; Sweeney, C.
    • Proceedings of the National Academy of Sciences, Vol. 104, Issue 48
    • DOI: 10.1073/pnas.0708986104

    Global carbon budget 2014
    journal, January 2015

    • Le Quéré, C.; Moriarty, R.; Andrew, R. M.
    • Earth System Science Data, Vol. 7, Issue 1
    • DOI: 10.5194/essd-7-47-2015

    Technical Note: Adapting a fixed-lag Kalman smoother to a geostatistical atmospheric inversion framework
    journal, January 2008


    Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter
    journal, June 2007

    • Hunt, Brian R.; Kostelich, Eric J.; Szunyogh, Istvan
    • Physica D: Nonlinear Phenomena, Vol. 230, Issue 1-2
    • DOI: 10.1016/j.physd.2006.11.008

    Variational data assimilation for atmospheric CO 2
    journal, January 2006


    Ensemble-Based Parameter Estimation in a Coupled General Circulation Model
    journal, September 2014