Editorial Type:
Article
Article Type:
Research Article

Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). Part II: CGCM Experiments

Feiyu Lu Nelson Institute Center for Climatic Research, and Department of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, Madison, Wisconsin

Search for other papers by Feiyu Lu in
Current site
Google Scholar
PubMed Close
,
Zhengyu Liu Nelson Institute Center for Climatic Research, and Department of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, Madison, Wisconsin, and Laboratory for Climate, Ocean and Atmosphere Studies, Peking University, Beijing, China

Search for other papers by Zhengyu Liu in
Current site
Google Scholar
PubMed Close
,
Shaoqing Zhang NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

Search for other papers by Shaoqing Zhang in
Current site
Google Scholar
PubMed Close
,
Yun Liu Nelson Institute Center for Climatic Research, and Department of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, Madison, Wisconsin

Search for other papers by Yun Liu in
Current site
Google Scholar
PubMed Close
, and
Robert Jacob Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, Illinois

Search for other papers by Robert Jacob in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Nov 2015
DOI:
https://doi.org/10.1175/MWR-D-15-0088.1
Page(s):
4645–4659
A related article is available
Restricted access

Abstract

This paper uses a fully coupled general circulation model (CGCM) to study the leading averaged coupled covariance (LACC) method in a strongly coupled data assimilation (SCDA) system. The previous study in a simple coupled climate model has shown that, by calculating the coupled covariance using the leading averaged atmospheric states, the LACC method enhances the signal-to-noise ratio and improves the analysis quality of the slow model component compared to both the traditional weakly coupled data assimilation without cross-component adjustments (WCDA) and the regular SCDA using the simultaneous coupled covariance (SimCC).

Here in Part II, the LACC method is tested with a CGCM in a perfect-model framework. By adding the observational adjustments from the low-level atmosphere temperature to the sea surface temperature (SST), the SCDA using LACC significantly reduces the SST error compared to WCDA over the globe; it also improves from the SCDA using SimCC, which performs better than the WCDA only in the deep tropics. The improvement in SST analysis is a result of the enhanced signal-to-noise ratio in the LACC method, especially in the extratropical regions. The improved SST analysis also benefits the subsurface ocean temperature and low-level atmosphere temperature analyses through dynamic and statistical processes.

Center for Climatic Research Contribution Number 1211.

Corresponding author address: Feiyu Lu, Center for Climatic Research, 1225 W. Dayton St., Madison, WI 53706. E-mail: flu7@wisc.edu

Abstract

This paper uses a fully coupled general circulation model (CGCM) to study the leading averaged coupled covariance (LACC) method in a strongly coupled data assimilation (SCDA) system. The previous study in a simple coupled climate model has shown that, by calculating the coupled covariance using the leading averaged atmospheric states, the LACC method enhances the signal-to-noise ratio and improves the analysis quality of the slow model component compared to both the traditional weakly coupled data assimilation without cross-component adjustments (WCDA) and the regular SCDA using the simultaneous coupled covariance (SimCC).

Here in Part II, the LACC method is tested with a CGCM in a perfect-model framework. By adding the observational adjustments from the low-level atmosphere temperature to the sea surface temperature (SST), the SCDA using LACC significantly reduces the SST error compared to WCDA over the globe; it also improves from the SCDA using SimCC, which performs better than the WCDA only in the deep tropics. The improvement in SST analysis is a result of the enhanced signal-to-noise ratio in the LACC method, especially in the extratropical regions. The improved SST analysis also benefits the subsurface ocean temperature and low-level atmosphere temperature analyses through dynamic and statistical processes.

Center for Climatic Research Contribution Number 1211.

Corresponding author address: Feiyu Lu, Center for Climatic Research, 1225 W. Dayton St., Madison, WI 53706. E-mail: flu7@wisc.edu
Save
Cover Monthly Weather Review
Monthly Weather Review

  • Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884–2903, doi:10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Anderson, J. L., 2003: A local least squares framework for ensemble filtering. Mon. Wea. Rev., 131, 634–642, doi:10.1175/1520-0493(2003)131<0634:ALLSFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Barsugli, J. J., and D. S. Battisti, 1998: The basic effects of atmosphere–ocean thermal coupling on midlatitude variability. J. Atmos. Sci., 55, 477–493, doi:10.1175/1520-0469(1998)055<0477:TBEOAO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Bloom, S. C., L. L. Takacs, A. M. da Silva, and D. Ledvina, 1996: Data assimilation using incremental analysis updates. Mon. Wea. Rev., 124, 1256–1271, doi:10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Burgers, G., P. J. van Leeuwen, and G. Evensen, 1998: Analysis scheme in the ensemble Kalman filter. Mon. Wea. Rev., 126, 1719–1724, doi:10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Cox, M. D., 1984: A primitive equation three-dimensional model of the ocean. Tech. Rep. GFDL Ocean Group Tech. Rep. 1, GFDL, Princeton, NJ, 143 pp.

  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, doi:10.1002/qj.828.

    • Search Google Scholar
    • Export Citation

  • Dirren, S., and G. J. Hakim, 2005: Toward the assimilation of time-averaged observations. Geophys. Res. Lett., 32, L04804, doi:10.1029/2004GL021444.

    • Search Google Scholar
    • Export Citation

  • Drake, J., I. Foster, J. Michalakes, B. Toonen, and P. Worley, 1995: Design and performance of a scalable parallel community climate model. Parallel Comput., 21, 1571–1591, doi:10.1016/0167-8191(96)80001-9.

    • Search Google Scholar
    • Export Citation

  • Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte-Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10 143–10 162, doi:10.1029/94JC00572.

    • Search Google Scholar
    • Export Citation

  • Frankignoul, C., A. Czaja, and B. L’Heveder, 1998: Air–sea feedback in the North Atlantic and surface boundary conditions for ocean models. J. Climate, 11, 2310–2324, doi:10.1175/1520-0442(1998)011<2310:ASFITN>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Gaspari, G., and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723–757, doi:10.1002/qj.49712555417.

    • Search Google Scholar
    • Export Citation

  • Hack, J. J., B. A. Boville, B. P. Briegleb, J. T. Kiehl, P. J. Rasch, and D. L. Williamson, 1993: Description of the NCAR Community Climate Model (CCM2). NCAR Tech. Note NCAR/TN-382+STR, NCAR, Boulder, CO, 108 pp.

  • Han, G. J., X. R. Wu, S. Zhang, Z. Liu, and W. Li, 2013: Error covariance estimation for coupled data assimilation using a Lorenz atmosphere and a simple pycnocline ocean model. J. Climate, 26, 10 218–10 231, doi:10.1175/JCLI-D-13-00236.1.

    • Search Google Scholar
    • Export Citation

  • Hasselmann, K., 1976: Stochastic climate models. Part I. Theory. Tellus, 28A, 473–485, doi:10.1111/j.2153-3490.1976.tb00696.x.

  • Houtekamer, P. L., and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796–811, doi:10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Huntley, H. S., and G. J. Hakim, 2010: Assimilation of time-averaged observations in a quasi-geostrophic atmospheric jet model. Climate Dyn., 35, 995–1009, doi:10.1007/s00382-009-0714-5.

    • Search Google Scholar
    • Export Citation

  • Jacob, R., 1997: Low frequency variability in a simulated atmosphere ocean system. Ph.D. thesis, University of Wisconsin–Madison, 170 pp.

  • Liu, Z., J. Kutzbach, and L. X. Wu, 2000: Modeling climate shift of El Niño variability in the Holocene. Geophys. Res. Lett., 27, 2265–2268, doi:10.1029/2000GL011452.

    • Search Google Scholar
    • Export Citation

  • Liu, Z., Q. Zhang, and L. Wu, 2004: Remote impact on tropical Atlantic climate variability: Statistical assessment and dynamic assessment. J. Climate, 17, 1529–1549, doi:10.1175/1520-0442(2004)017<1529:RIOTAC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Liu, Z., and Coauthors, 2007a: Simulating the transient evolution and abrupt change of Northern Africa atmosphere–ocean–terrestrial ecosystem in the Holocene. Quat. Sci. Rev., 26, 1818–1837, doi:10.1016/j.quascirev.2007.03.002.

    • Search Google Scholar
    • Export Citation

  • Liu, Z., Y. Liu, L. Wu, and R. Jacob, 2007b: Seasonal and long-term atmospheric responses to reemerging North Pacific Ocean variability: A combined dynamical and statistical assessment. J. Climate, 20, 955–980, doi:10.1175/JCLI4041.1.

    • Search Google Scholar
    • Export Citation

  • Liu, Z., S. Wu, S. Zhang, Y. Liu, and X. Y. Rong, 2013: Ensemble data assimilation in a simple coupled climate model: The role of ocean-atmosphere interaction. Adv. Atmos. Sci., 30, 1235–1248, doi:10.1007/s00376-013-2268-z.

    • Search Google Scholar
    • Export Citation

  • Liu, Y., Z. Liu, S. Zhang, R. Jacob, F. Lu, X. Rong, and S. Wu, 2014a: Ensemble-based parameter estimation in a coupled general circulation model. J. Climate, 27, 7151–7162, doi:10.1175/JCLI-D-13-00406.1.

    • Search Google Scholar
    • Export Citation

  • Liu, Y., Z. Liu, S. Zhang, X. Rong, R. Jacob, S. Wu, and F. Lu, 2014b: Ensemble-based parameter estimation in a coupled GCM using the adaptive spatial average method. J. Climate, 27, 4002–4014, doi:10.1175/JCLI-D-13-00091.1.

    • Search Google Scholar
    • Export Citation

  • Lu, F., Z. Liu, S. Zhang, and Y. Liu, 2015: Strongly coupled data assimilation using leading averaged coupled covariance (LACC). Part I: Simple model study. Mon. Wea. Rev., 143, 3823–3837, doi:10.1175/MWR-D-14-00322.1.

    • Search Google Scholar
    • Export Citation

  • Rienecker, M. M., and Coauthors, 2011: MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 3624–3648, doi:10.1175/JCLI-D-11-00015.1.

    • Search Google Scholar
    • Export Citation

  • Rosati, A., K. Miyakoda, and R. Gudgel, 1997: The impact of ocean initial conditions on ENSO forecasting with a coupled model. Mon. Wea. Rev., 125, 754–772, doi:10.1175/1520-0493(1997)125<0754:TIOOIC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 1015–1057, doi:10.1175/2010BAMS3001.1.

    • Search Google Scholar
    • Export Citation

  • Sugiura, N., T. Awaji, S. Masuda, T. Mochizuki, T. Toyoda, T. Miyama, H. Igarashi, and Y. Ishikawa, 2008: Development of a four-dimensional variational coupled data assimilation system for enhanced analysis and prediction of seasonal to interannual climate variations. J. Geophys. Res., 113, C10017, doi:10.1029/2008JC004741.

    • Search Google Scholar
    • Export Citation

  • Tardif, R., G. J. Hakim, and C. Snyder, 2014: Coupled atmosphere–ocean data assimilation experiments with a low-order climate model. Climate Dyn., 43, 1631–1643, doi:10.1007/s00382-013-1989-0.

    • Search Google Scholar
    • Export Citation

  • Tardif, R., G. J. Hakim, and C. Snyder, 2015: Coupled atmosphere–ocean data assimilation experiments with a low-order model and CMIP5 model data. Climate Dyn., 45, 1415–1427, doi:10.1007/s00382-014-2390-3.

    • Search Google Scholar
    • Export Citation

  • Wu, L., and Z. Liu, 2005: North Atlantic decadal variability: Air–sea coupling, oceanic memory, and potential Northern Hemisphere resonance. J. Climate, 18, 331–349, doi:10.1175/JCLI-3264.1.

    • Search Google Scholar
    • Export Citation

  • Wu, L., Z. Liu, R. G. Gallimore, R. Jacob, D. Lee, and Y. Zhong, 2003: Pacific decadal variability: The tropical Pacific mode and the North Pacific mode. J. Climate, 16, 1101–1120, doi:10.1175/1520-0442(2003)16<1101:PDVTTP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Yin, Y. H., O. Alves, and P. R. Oke, 2011: An ensemble ocean data assimilation system for seasonal prediction. Mon. Wea. Rev., 139, 786–808, doi:10.1175/2010MWR3419.1.

    • Search Google Scholar
    • Export Citation

  • Zhang, F. Q., C. Snyder, and J. Z. Sun, 2004: Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble Kalman filter. Mon. Wea. Rev., 132, 1238–1253, doi:10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Zhang, S., 2011: A study of impacts of coupled model initial shocks and state parameter optimization on climate predictions using a simple pycnocline prediction model. J. Climate, 24, 6210–6226, doi:10.1175/JCLI-D-10-05003.1.

    • Search Google Scholar
    • Export Citation

  • Zhang, S., M. J. Harrison, A. T. Wittenberg, A. Rosati, J. L. Anderson, and V. Balaji, 2005: Initialization of an ENSO forecast system using a parallelized ensemble filter. Mon. Wea. Rev., 133, 3176–3201, doi:10.1175/MWR3024.1.

    • Search Google Scholar
    • Export Citation

  • Zhang, S., M. J. Harrison, A. Rosati, and A. Wittenberg, 2007: System design and evaluation of coupled ensemble data assimilation for global oceanic climate studies. Mon. Wea. Rev., 135, 3541–3564, doi:10.1175/MWR3466.1.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 224 105 6
PDF Downloads 183 83 4