Background error covariance estimation for atmospheric CO 2 data assimilation
dc.contributor.author | Chatterjee, Abhishek | en_US |
dc.contributor.author | Engelen, Richard J. | en_US |
dc.contributor.author | Kawa, Stephan R. | en_US |
dc.contributor.author | Sweeney, Colm | en_US |
dc.contributor.author | Michalak, Anna M. | en_US |
dc.date.accessioned | 2013-11-01T19:01:02Z | |
dc.date.available | 2014-10-06T19:17:43Z | en_US |
dc.date.issued | 2013-09-16 | en_US |
dc.identifier.citation | Chatterjee, Abhishek; Engelen, Richard J.; Kawa, Stephan R.; Sweeney, Colm; Michalak, Anna M. (2013). "Background error covariance estimation for atmospheric CO 2 data assimilation." Journal of Geophysical Research: Atmospheres 118(17): 10,140-10,154. <http://hdl.handle.net/2027.42/100305> | en_US |
dc.identifier.issn | 2169-897X | en_US |
dc.identifier.issn | 2169-8996 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/100305 | |
dc.description.abstract | In any data assimilation framework, the background error covariance statistics play the critical role of filtering the observed information and determining the quality of the analysis. For atmospheric CO 2 data assimilation, however, the background errors cannot be prescribed via traditional forecast or ensemble‐based techniques as these fail to account for the uncertainties in the carbon emissions and uptake, or for the errors associated with the CO 2 transport model. We propose an approach where the differences between two modeled CO 2 concentration fields, based on different but plausible CO 2 flux distributions and atmospheric transport models, are used as a proxy for the statistics of the background errors. The resulting error statistics: (1) vary regionally and seasonally to better capture the uncertainty in the background CO 2 field, and (2) have a positive impact on the analysis estimates by allowing observations to adjust predictions over large areas. A state‐of‐the‐art four‐dimensional variational (4D‐VAR) system developed at the European Centre for Medium‐Range Weather Forecasts (ECMWF) is used to illustrate the impact of the proposed approach for characterizing background error statistics on atmospheric CO 2 concentration estimates. Observations from the Greenhouse gases Observing SATellite “IBUKI” (GOSAT) are assimilated into the ECMWF 4D‐VAR system along with meteorological variables, using both the new error statistics and those based on a traditional forecast‐based technique. Evaluation of the four‐dimensional CO 2 fields against independent CO 2 observations confirms that the performance of the data assimilation system improves substantially in the summer, when significant variability and uncertainty in the fluxes are present. Key Points Difference in modeled CO2 fields is used to define background errors in CO2‐DA Both atmospheric transport & flux pattern differences impact background errors Evaluation using independent data shows positive impact on analysis estimates | en_US |
dc.publisher | John Wiley & Sons, Inc. | en_US |
dc.subject.other | Variational Data Assimilation | en_US |
dc.subject.other | GOSAT CO2 | en_US |
dc.subject.other | NMC Method | en_US |
dc.subject.other | Background Error Covariance Matrix | en_US |
dc.subject.other | Atmospheric CO2 | en_US |
dc.subject.other | Spatial and Temporal CO2 Variations | en_US |
dc.title | Background error covariance estimation for atmospheric CO 2 data assimilation | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Atmospheric and Oceanic Sciences | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/100305/1/jgrd50654.pdf | |
dc.identifier.doi | 10.1002/jgrd.50654 | en_US |
dc.identifier.source | Journal of Geophysical Research: Atmospheres | en_US |
dc.identifier.citedreference | Nichols, D. ( 2010 ), Mathematical concepts of data assimilation, in Part I. Theory, Data Assimilation, Making Sense of Observations, edited by W. Lahoz, B. Khattatov, and R. Menard, pp. 13 – 40, Springer‐Verlag, Berlin. | en_US |
dc.identifier.citedreference | Le Quere, C., et al. ( 2009 ), Trends in the sources and sinks of carbon dioxide, Nat. Geosci., 2 ( 12 ), 831 – 836, doi: 10.1038/ngeo689. | en_US |
dc.identifier.citedreference | Liu, J., I. Fung, E. Kalnay, J.‐S. Kang, E. T. Olsen, and L. Chen ( 2012 ), Simultaneous assimilation of AIRS XCO 2 and meteorological observations in a carbon climate model with an ensemble Kalman filter, J. Geophys. Res., 117, D05309, doi: 10.1029/2011JD016642. | en_US |
dc.identifier.citedreference | Machida, T., N. Vivo, N. de Noblet‐Ducoudre, J. Ogee, J. Polcher, P. Friedlingstein, P. Ciais, S. Stephen, and I. C. Prentice ( 2008 ), Worldwide measurements of atmospheric CO 2 and other trace gas species using commercial airlines, J. Atmos. Oceanic Technol., 25 ( 10 ), 1744 – 1754, doi: 10.1175/2008JTECHA1082.1. | en_US |
dc.identifier.citedreference | Massart, S., A. Piacentini, and O. Pannekoucke ( 2012 ), Importance of using ensemble estimated background error covariances for the quality of atmospheric ozone analyses, Q. J. R. Meteorol. Soc., 138 ( 665 ), 889 – 905, doi: 10.1002/qj.971. | en_US |
dc.identifier.citedreference | Nocedal, J., and S. J. Wright ( 2006 ), Numerical Optimization, Springer Ser. Oper. Res., pp. 224 – 229, Springer‐Verlag, Berlin. | en_US |
dc.identifier.citedreference | O'Dell, C. W., et al. ( 2012 ), The ACOS CO 2 retrieval algorithm. Part 1: Description and validation against synthetic observations, Atmos. Meas. Tech., 5 ( 1 ), 99 – 121, doi: 10.5194/amt‐5‐99‐2012. | en_US |
dc.identifier.citedreference | Pannekoucke, O., L. Berre, and G. Desroziers ( 2007 ), Filtering properties of wavelets for local background‐error correlations, Q. J. R. Meteorol. Soc., 133 ( 623 ), 363 – 379, doi: 10.1002/qj.33. | en_US |
dc.identifier.citedreference | Parrish, D. F., and J. C. Derber ( 1992 ), The National Meteorological Centre's spectral statistical–interpolation analysis system, Mon. Weather Rev., 120 ( 8 ), 1747 – 1763, doi: 10.1175/1520‐0493(1992)120<1747:tnmcss>2.0.co;2. | en_US |
dc.identifier.citedreference | Randerson, J. T., M. V. Thomps, T. J. Conw, I. Y. Fun, and C. B. Field ( 1997 ), The contribution of terrestrial sources and sinks to trends in the seasonal cycle of atmospheric carbon dioxide, Global Biogeochem. Cycles, 11 ( 4 ), 535 – 560, doi: 10.1029/97GB02268. | en_US |
dc.identifier.citedreference | Raynaud, L., L. Berre, and G. Desroziers ( 2009 ), Objective filtering of ensemble‐based background‐error variances, Q. J. R. Meteorol. Soc., 135 ( 642 ), 1177 – 1199, doi: 10.1002/qj.438. | en_US |
dc.identifier.citedreference | Scholes, R. J., P. M. S. Monteiro, C. L. Sabine, and J. G. Canadell ( 2009 ). Systematic long‐term observations of the global carbon cycle, Trends Ecol. Evol., 24 ( 8 ), 427 – 430, doi: 10.1016/j.tree.2009.03.006. | en_US |
dc.identifier.citedreference | Simeoni, D., C. Singer, and G. Chalon ( 1997 ), Infrared atmospheric sounding interferometer, Acta Astronaut., 40 ( 2–8 ), doi: 10.1016/s0094‐5765(97)00098‐2. | en_US |
dc.identifier.citedreference | Singh, K., M. Jardak, A. Sandu, K. Bowman, M. Lee, and D. Jones ( 2011 ), Construction of non‐diagonal background error covariance matrices for global chemical data assimilation, Geoscientific Model Dev., 4 ( 2 ), 299 – 316, doi: 10.5194/gmd‐4‐299‐2011. | en_US |
dc.identifier.citedreference | Siroka, M., C. Fischer, V. Casse, R. Brozkova, and J. F. Geleyn ( 2003 ), The definition of mesoscale selective forecast error covariances for a limited area variational analysis, Meteorol. Atmos. Phys., 82 ( 1–4 ), 227 – 244, doi: 10.1007/s00703‐001‐0588‐5. | en_US |
dc.identifier.citedreference | Stohl, A., S. Eckhardt, C. Forster, P. James, and N. Spichtinger ( 2002 ), On the pathways and timescales of intercontinental air pollution transport, J. Geophys. Res., 107 ( D23 ), 4684, doi: 10.1029/2001JD001396. | en_US |
dc.identifier.citedreference | Storto, A., and R. Randriamampianina ( 2010 ), Ensemble variational assimilation for the representation of background error covariances in a high‐latitude regional model, J. Geophys. Res., 115, D17204, doi: 10.1029/2009JD013111. | en_US |
dc.identifier.citedreference | Tans, P. P. ( 1996 ), Carbon cycle (group report), in Summary Report 1994–1995, vol. 23, edited by D. J. Hoffman, J. Peterson, and R. M. Rosson, pp. 29 – 49, U. S. Dep. of Commer, Boulder, Colorado. | en_US |
dc.identifier.citedreference | Tiwari, Y. K., M. Gloor, R. J. Engelen, F. Chevallier, C. Rödenbeck, S. Körner, P. Peylin, B. H. Braswell, and M. Heimann ( 2006 ), Comparing CO 2 retrieved from Atmospheric Infrared Sounder with model predictions: Implications for constraining surface fluxes and lower‐to‐upper troposphere transport, J. Geophys. Res., 111, D17106, doi: 10.1029/2005JD006681. | en_US |
dc.identifier.citedreference | Willmott, C. J., and K. Matsuura ( 2005 ), Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance, Clim. Res., 30 ( 1 ), 79 – 82, doi: 10.3354/cr030079. | en_US |
dc.identifier.citedreference | Wofsy, S. C. ( 2011 ), HIAPER Pole‐to‐Pole Observations (HIPPO): Fine‐grained, global‐scale measurements of climatically important atmospheric gases and aerosols, Proc. R. Soc. A, 369, 2073 – 2086, doi: 10.1098/rsta.2010.0313. | en_US |
dc.identifier.citedreference | Wunch, D., et al. ( 2011a ), A method for evaluating bias in global measurements of CO 2 total columns from space, Atmos. Chem. Phys., 11, 12,317 – 12,337, doi: 10.5194/acp‐11‐12317‐2011. | en_US |
dc.identifier.citedreference | Wunch, D., G. C. Toon, J.‐F. L. Blavier, R. A. Washenfelder, J. Notholt, B. J. Connor, D. W. T. Griffith, V. Sherlock, and P. O. Wennberg ( 2011b ), The Total Carbon Column Observing Network, Phil. Trans. R. Soc. A‐Math. Phys. Eng. Sci., 369 ( 1943 ), 2087 – 2112, doi: 10.1098/rsta.2010.0240. | en_US |
dc.identifier.citedreference | Xueref‐Remy, I., P. Bousquet, C. Carouge, L. Rivier, and P. Ciais ( 2011 ), Variability and budget of CO 2 in Europe: Analysis of the CAATER airborne campaigns. Part 2: Comparison of CO2 vertical variability and fluxes between observations and a modeling framework, Atmos. Chem. Phys., 11 ( 12 ), doi: 10.5194/acp‐11‐5673‐2011. | en_US |
dc.identifier.citedreference | Yokota, T., Y. Yoshida, N. Eguchi, Y. Ota, T. Tanaka, H. Watanabe, and S. Maksyutov ( 2009 ), Global concentrations of CO 2 and CH 4 retrieved from GOSAT, SOLA, 5, 160 – 163, doi: 10.2151/sola.2009‐041. | en_US |
dc.identifier.citedreference | Alkhaled, A. A., A. M. Michalak, S. Randolph Kawa, S. C. Olsen, and J.‐W. Wang ( 2008 ), A global evaluation of the regional spatial variability of column integrated CO 2 distributions, J. Geophys. Res., 113, D20303, doi: 10.1029/2007JD009693. | en_US |
dc.identifier.citedreference | Andrews, A. E., K. A. Boering, S. C. Wofsy, B. C. Daube, D. B. Jones, S. Alex, M. Loewenstein, J. R. Podolske, and S. E. Strahan ( 2001 ), Empirical age spectra for the midlatitude lower stratosphere from in situ observations of CO 2: Quantitative evidence for a subtropical “barrier” to horizontal transport, J. Geophys. Res., 106 ( D10 ), 10,257 – 10,274, doi: 10.1029/2000JD900703. | en_US |
dc.identifier.citedreference | Aumann, H. H., et al. ( 2003 ), AIRS/AMSU/HSB on the aqua mission: Design, science objectives, data products, and processing systems, IEEE Trans. Geosci. Remote Sens., 41 ( 2 ), doi: 10.1109/tgrs.2002.808356. | en_US |
dc.identifier.citedreference | Baker, D. F., H. Bösch, S. C. Doney, D. O'Brien, and D. S. Schimel ( 2010 ), Carbon source/sink information provided by column CO 2 measurements from the Oribiting Carbon Observatory, Atmos. Chem. Phys., 10, 4145 – 4165, doi: 10.5194/acp‐10‐4145‐2010. | en_US |
dc.identifier.citedreference | Bannister, R. N. ( 2008a ), A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances, Q. J. R. Meteorol. Soc., 134 ( 637 ), 1951 – 1970, doi: 10.1002/qj.339. | en_US |
dc.identifier.citedreference | Bannister, R. N. ( 2008b ), A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics, Q. J. R. Meteorol. Soc., 134 ( 637 ), 1971 – 1996, doi: 10.1002/qj.340. | en_US |
dc.identifier.citedreference | Belo Pereira, M., and L. Berre ( 2006 ), The use of an ensemble approach to study the background error covariances in a global NWP model, Mon. Weather Rev., 134 ( 9 ), 2466 – 2489, doi: 10.1175/mwr3189.1. | en_US |
dc.identifier.citedreference | Benedetti, A., and M. Fisher ( 2007 ), Background error statistics for aerosols, Q. J. R. Meteorol. Soc., 133 ( 623 ), 391 – 405, doi: 10.1002/qj.37. | en_US |
dc.identifier.citedreference | Berre, L., and G. Desroziers ( 2010 ), Filtering of background error variances and correlations by local spatial averaging: A review, Mon. Weather Rev., 138 ( 10 ), 3693 – 3720, doi: 10.1175/2010mwr3111.1. | en_US |
dc.identifier.citedreference | Berre, L., S. E. Stefanescu, and M. B. Pereira ( 2006 ), The representation of the analysis effect in three error simulation techniques, Tellus Ser. A. Dyn. Meteorol. Oceanogr., 58 ( 2 ), 196 – 209, doi: 10.1111/j.1600‐0870.2006.00165.x. | en_US |
dc.identifier.citedreference | Bonavita, M., L. Raynaud, and L. Isaksen ( 2011 ), Estimating background‐error variances with the ECMWF Ensemble of Data Assimilations system: Some effects of ensemble size and day‐to‐day variability, Q. J. R. Meteorol. Soc., 137 ( 655 ), 423 – 434, doi: 10.1002/qj.756. | en_US |
dc.identifier.citedreference | Bönisch, H., A. Engel, J. Curtius, T. Birner, and P. Hoor ( 2009 ), Quantifying transport into the lowermost stratosphere using simultaneous in‐situ measurements of SF 6 and CO 2, Atmos. Chem. Phys., 9 ( 16 ), 5905 – 5919, doi: 10.5194/acp‐9‐5905‐2009. | en_US |
dc.identifier.citedreference | Bösch, H., et al. ( 2006 ), Space‐based near‐infrared CO 2 measurements: Testing the orbiting carbon observatory retrieval algorithm and validation concept using SCIAMACHY observations over Park Falls, Wisconsin, J. Geophys. Res., 111, D23302, doi: 10.1029/2006JD007080. | en_US |
dc.identifier.citedreference | Brenninkmeijer, C. A. M., et al. ( 2007 ), Civil aircraft for the regular investigation of the atmosphere based on an instrumented container: The new CARIBIC system, Atmos. Chem. Phys., 7 ( 18 ), 4953 – 4976, doi: 10.5194/acp‐7‐4953‐2007. | en_US |
dc.identifier.citedreference | Brousseau, P., L. Berre, F. Bouttier, and G. Desroziers ( 2012 ), Flow‐dependent background‐error covariances for a convective‐scale data assimilation system, Q. J. R. Meteorol. Soc., 138 ( 663 ), 310 – 322, doi: 10.1002/qj.920. | en_US |
dc.identifier.citedreference | Buehner, M., and M. Charron ( 2007 ), Spectral and spatial localization of background‐error correlations for data assimilation, Q. J. R. Meteorol. Soc., 133 ( 624 ), 615 – 630, doi: 10.1002/qj.50. | en_US |
dc.identifier.citedreference | Cardinali, C., S. Pezzulli, and E. Andersson ( 2004 ), Influence‐matrix diagnostic of a data assimilation system, Q. J. R. Meteorol. Soc., 130 ( 603 ), 2767 – 2786, doi: 10.1256/qj.03.205. | en_US |
dc.identifier.citedreference | Chai, T. F., et al. ( 2007 ), Four‐dimensional data assimilation experiments with International Consortium for Atmospheric Research on Transport and Transformation ozone measurements, J. Geophys. Res., 112, D12S15, doi: 10.1029/2006JD007763. | en_US |
dc.identifier.citedreference | 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 estimation of carbon surface fluxes, J. Geophys. Res., 114, D20303, doi: 10.1029/2009JD012311. | en_US |
dc.identifier.citedreference | Chevallier, F., S. Maksyutov, P. Bousquet, F.‐M. Bréon, 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. | en_US |
dc.identifier.citedreference | Chiles, J.‐P., and P. Delfiner ( 2012 ), Geostatistics: Modeling Spatial Uncertainty (Wiley Series in Probability and Statistics), 734 pp., John Wiley & Sons, Inc., Hoboken, New Jersey. | en_US |
dc.identifier.citedreference | Constantinescu, E. M., T. Chai, A. Sandu, and G. R. Carmichael ( 2007 ), Autoregressive models of background errors for chemical data assimilation, J. Geophys. Res., 112, D12309, doi: 10.1029/2006JD008103. | en_US |
dc.identifier.citedreference | Crevoisier, C., A. Chedin, H. Matsueda, T. Machida, R. Armante, and N. A. Scott ( 2009 ), First year of upper tropospheric integrated content of CO 2 from IASI hyperspectral infrared observations, Atmos. Chem. Phys., 9 ( 14 ), 4797 – 4810, doi: 10.5194/acp‐9‐4797‐2009. | en_US |
dc.identifier.citedreference | Crevoisier, C., C. Sweeney, M. Gloor, J. L. Sarmiento, and P. P. Tans ( 2010 ), Regional US carbon sinks from three‐dimensional atmospheric CO 2 sampling, Proc. Natl. Acad. Sci. U. S. A., 107 ( 43 ), 18,348 – 18,353, doi: 10.1073/pnas.0900062107. | en_US |
dc.identifier.citedreference | Crisp, D., et al. ( 2012 ), The ACOS CO 2 retrieval algorithm. Part II: Global XCO 2 data characterization, Atmos. Meas. Tech., 5, 687 – 707, doi: 10.5194/amt‐5‐687‐2012. | en_US |
dc.identifier.citedreference | Denning, A. S., T. Takahashi, and P. Friedlingstein ( 1999 ), Can a strong atmospheric CO 2 rectifier effect be reconciled with a “reasonable” carbon budget?, Tellus Ser. B‐Chem. Phys. Meteorol., 51 ( 2 ), doi: 10.1034/j.1600‐0889.1999.t01‐1‐00010.x. | en_US |
dc.identifier.citedreference | Derber, J., and F. Bouttier ( 1999 ), A reformulation of the background error covariance in the ECMWF global data assimilation system, Tellus Ser. A‐Dyn. Meteorol. Oceanogr., 51 ( 2 ), 195 – 221, doi: 10.1034/j.1600‐0870.1999.t01‐2‐00003.x. | en_US |
dc.identifier.citedreference | Engelen, R. J., and A. P. McNally ( 2005 ), Estimating atmospheric CO 2 from advanced infrared satellite radiances within an operational four‐dimensional variational (4D‐Var) data assimilation system: Results and validation, J. Geophys. Res., 110, D18305, doi: 10.1029/2005JD005982. | en_US |
dc.identifier.citedreference | Pannekoucke, O., L. Berre, and G. Desroziers ( 2008 ), Background‐error correlation length‐scale estimates and their sampling statistics, Q. J. R. Meteorol. Soc., 134 ( 631 ), 497 – 508, doi: 10.1002/qj.212. | en_US |
dc.identifier.citedreference | Engelen, R. J., E. Andersson, F. Chevallier, A. Hollingsworth, M. Matricardi, A. P. McNally, J.‐N. Thepaut, and P. D. Watts ( 2004 ), Estimating atmospheric CO 2 from advanced infrared satellite radiances within an operational 4D‐Var data assimilation system: Methodology and first results, J. Geophys. Res., 109, D19309, doi: 10.1029/2004JD004777. | en_US |
dc.identifier.citedreference | Engelen, R. J., S. Serrar, and F. Chevallier ( 2009 ), Four‐dimensional data assimilation of atmospheric CO 2 using AIRS observations, J. Geophys. Res., 114, D03303, doi: 10.1029/2008JD010739. | en_US |
dc.identifier.citedreference | Fisher, M. ( 2004 ), Background error covariance modelling, Proc. of ECMWF Seminar on Recent Developments in Data Assimilation for Atmosphere and Ocean, Reading, UK, 45–64. | en_US |
dc.identifier.citedreference | Fisher, M. ( 2006 ), “Wavelet”Jb –A new way to model the statistics of background errors. ECMWF Newsletter, 106, 23–28, Society, 130 ( 601 ), 2253 – 2275, doi: 10.1256/qj.03.26. | en_US |
dc.identifier.citedreference | Gurk, C., H. Fischer, P. Hoor, M. G. Lawrence, J. Lelieveld, and H. Wernli ( 2008 ), Airborne in‐situ measurements of vertical, seasonal and latitudinal distributions of carbon dioxide over Europe, Atmos. Chem. Phys., 8 ( 21 ), 6395 – 6403, doi: 10.5194/acp‐8‐6395‐2008. | en_US |
dc.identifier.citedreference | Hammerling, D. M., A. M. Michalak, and S. R. Kawa ( 2012a ), Mapping of CO 2 at high spatiotemporal resolution using satellite observations: Global distributions from OCO‐2, J. Geophys. Res., 117, D06306, doi: 10.1029/2011JD017015. | en_US |
dc.identifier.citedreference | Hammerling, D. M., A. M. Michalak, C. O'Dell, and S. R. Kawa ( 2012b ), Global CO 2 distributions over land from the Greenhouse Gases Observing Satellite (GOSAT), Geophys. Res. Lett., 39, L08804, doi: 10.1029/2012GL051203. | en_US |
dc.identifier.citedreference | Hess, R. ( 2010 ), Flow dependence of background errors and their vertical correlations for radiance‐data assimilation, Q. J. R. Meteorol. Soc., 136 ( 647 ), 475 – 486, doi: 10.1002/qj.570. | en_US |
dc.identifier.citedreference | Hollingsworth, A., et al. ( 2008 ), Toward a monitoring and forecasting system for atmospheric composition: The GEMS project, Bull. Am. Meteorol. Soc., 89 ( 8 ), 1147 – 1164, doi: 10.1175/2008bams2355.1. | en_US |
dc.identifier.citedreference | Holton, J. R., P. H. Haynes, M. E. McIntyre, A. R. Douglass, R. B. Rood, and L. Pfister ( 1995 ), Stratosphere‐troposphere exchange, Rev. Geophys., 33 ( 4 ), 403 – 439, doi: 10.1029/95RG02097. | en_US |
dc.identifier.citedreference | Huntzinger, D. N., et al. ( 2012 ), North American Carbon Program (NACP) regional interim synthesis: Terrestrial biospheric model intercomparison, Ecol. Model., 232, doi: 10.1016/j.ecolmodel.2012.02.004. | en_US |
dc.identifier.citedreference | Kahnert, M. ( 2008 ), Variational data analysis of aerosol species in a regional CTM: Background error covariance constraint and aerosol optical observation operators, Tellus Ser. B‐Chem. Phys. Meteorol., 60 ( 5 ), 753 – 770, doi: 10.1111/j.1600‐0889.2008.00377.x. | en_US |
dc.identifier.citedreference | Kawa, S. R., D. J. Erickson, S. Pawson, and Z. Zhu ( 2004 ), Global CO 2 transport simulations using meteorological data from the NASA data assimilation system, J. Geophys. Res., 109, D18312, doi: 10.1029/2004JD004554. | en_US |
dc.identifier.citedreference | Krinner, G., et al. ( 2005 ), A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system, Global Biogeochem. Cycles, 19, GB1015, doi: 10.1029/2003GB002199. | en_US |
dc.identifier.citedreference | Kulawik, S. S., N. Viovy, N. de Noblet‐Ducoudre, J. Ogée, J. Polcher, P. Friedlingstein, P. Ciais, S. Sitch, and I. Colin Prentice ( 2010 ), Characterization of Tropospheric Emission Spectrometer (TES) CO 2 for carbon cycle science, Atmos. Chem. Phys., 10 ( 12 ), 5601 – 5623, doi: 10.5194/acp‐10‐5601‐2010. | en_US |
dc.identifier.citedreference | Kuze, A., H. Suto, M. Nakajima, and T. Hamazaki ( 2009 ), Thermal and near infrared sensor for carbon observation Fourier‐transform spectrometer on the Greenhouse Gases Observing Satellite for greenhouse gases monitoring, Appl. Opt., 48, 6716 – 6733, doi: 10.1364/AO.48.006716. | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.