Editorial Type:
Article
Article Type:
Research Article

Impact of Lateral Boundary Conditions on Regional Analyses

Kamel Chikhar Étude et Simulation du Climat à l’Échelle Régionale Centre, University of Québec at Montréal, Montréal, Québec, Canada

Search for other papers by Kamel Chikhar in
Current site
Google Scholar
PubMed Close
and
Pierre Gauthier Étude et Simulation du Climat à l’Échelle Régionale Centre, University of Québec at Montréal, Montréal, Québec, Canada

Search for other papers by Pierre Gauthier in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Apr 2017
DOI:
https://doi.org/10.1175/MWR-D-16-0245.1
Page(s):
1361–1379
Restricted access

Abstract

Regional and global climate models are usually validated by comparison to derived observations or reanalyses. Using a model in data assimilation results in a direct comparison to observations to produce its own analyses that may reveal systematic errors. In this study, regional analyses over North America are produced based on the fifth-generation Canadian Regional Climate Model (CRCM5) combined with the variational data assimilation system of the Meteorological Service of Canada (MSC). CRCM5 is driven at its boundaries by global analyses from ERA-Interim or produced with the global configuration of the CRCM5. Assimilation cycles for the months of January and July 2011 revealed systematic errors in winter through large values in the mean analysis increments. This bias is attributed to the coupling of the lateral boundary conditions of the regional model with the driving data particularly over the northern boundary where a rapidly changing large-scale circulation created significant cross-boundary flows. Increasing the time frequency of the lateral driving and applying a large-scale spectral nudging significantly improved the circulation through the lateral boundaries, which translated in a much better agreement with observations.

Denotes content that is immediately available upon publication as open access.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Kamel Chikhar, chikhar@sca.uqam.ca

Abstract

Regional and global climate models are usually validated by comparison to derived observations or reanalyses. Using a model in data assimilation results in a direct comparison to observations to produce its own analyses that may reveal systematic errors. In this study, regional analyses over North America are produced based on the fifth-generation Canadian Regional Climate Model (CRCM5) combined with the variational data assimilation system of the Meteorological Service of Canada (MSC). CRCM5 is driven at its boundaries by global analyses from ERA-Interim or produced with the global configuration of the CRCM5. Assimilation cycles for the months of January and July 2011 revealed systematic errors in winter through large values in the mean analysis increments. This bias is attributed to the coupling of the lateral boundary conditions of the regional model with the driving data particularly over the northern boundary where a rapidly changing large-scale circulation created significant cross-boundary flows. Increasing the time frequency of the lateral driving and applying a large-scale spectral nudging significantly improved the circulation through the lateral boundaries, which translated in a much better agreement with observations.

Denotes content that is immediately available upon publication as open access.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Kamel Chikhar, chikhar@sca.uqam.ca
Save
Cover Monthly Weather Review
Monthly Weather Review

  • Alexandru, A., R. De Elia, R. Laprise, L. Šeparović, and S. Biner, 2009: Sensitivity study of regional climate model simulations to large-scale nudging parameters. Mon. Wea. Rev., 137, 1666–1686, doi:10.1175/2008MWR2620.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Baumhefner, D. P., and D. J. Perkey, 1982: Evaluation of lateral boundary errors in a limited-domain model. Tellus, 34A, 409–428, doi:10.1111/j.2153-3490.1982.tb01831.x.

    • Search Google Scholar
    • Export Citation

  • Buehner, M., and Coauthors, 2015: Implementation of deterministic weather forecasting systems based on ensemble–variational data assimilation at Environment Canada. Part I: The global system. Mon. Wea. Rev., 143, 2532–2559, doi:10.1175/MWR-D-14-00354.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Caron, J.-F., T. Milewski, M. Buehner, L. Fillion, M. Reszka, S. Macpherson, and J. St-James, 2015: Implementation of deterministic weather forecasting systems based on ensemble–variational data assimilation at Environment Canada. Part II: The regional system. Mon. Wea. Rev., 143, 2560–2580, doi:10.1175/MWR-D-14-00353.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chikhar, K., and P. Gauthier, 2014: Impact of analyses on the dynamical balance of global and limited-area atmospheric models. Quart. J. Roy. Meteor. Soc., 140, 2535–2545, doi:10.1002/qj.2319.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chikhar, K., and P. Gauthier, 2015: On the effect of boundary conditions on the Canadian Regional Climate Model: Use of process tendencies. Climate Dyn., 45, 2515–2526, doi:10.1007/s00382-015-2488-2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Côté, J., S. Gravel, A. Méthot, A. Patoine, M. Roch, and A. Staniforth, 1998: The operational CMC-MRB Global Environmental Multiscale (GEM) model. Part I: Design considerations and formulation. Mon. Wea. Rev., 126, 1373–1395, doi:10.1175/1520-0493(1998)126<1373:TOCMGE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Courtier, P., J.-N. Thépaut, and A. Hollingsworth, 1994: A strategy for operational implementation of 4D-Var, using an incremental approach. Quart. J. Roy. Meteor. Soc., 120, 1367–1387, doi:10.1002/qj.49712051912.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Davies, H., 1976: A lateral boundary formulation for multi-level prediction models. Quart. J. Roy. Meteor. Soc., 102, 405–418, doi:10.1002/qj.49710243210.

    • Search Google Scholar
    • Export Citation

  • Davies, T., 2014: Lateral boundary conditions for limited area models. Quart. J. Roy. Meteor. Soc., 140, 185–196, doi:10.1002/qj.2127.

  • Dee, D. P., 2005: Bias and data assimilation. Quart. J. Roy. Meteor. Soc., 131, 3323–3343, doi:10.1256/qj.05.137.

  • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Denis, B., R. Laprise, D. Caya, and J. Côté, 2002: Downscaling ability of one-way nested regional climate models: The Big-Brother experiment. Climate Dyn., 18, 627–646, doi:10.1007/s00382-001-0201-0.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Fillion, L., and Coauthors, 2010: The Canadian Regional Data Assimilation and Forecasting System. Wea. Forecasting, 25, 1645–1669, doi:10.1175/2010WAF2222401.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gauthier, P., C. Chouinard, and B. Brasnett, 2003: Quality control: Methodology and applications. Data Assimilation for the Earth System, R. Swinbank, V. Shutyaev, and W. A. Lahoz, Eds., Springer, 177–187, doi:10.1007/978-94-010-0029-1_15.

    • Crossref
    • Export Citation

  • Gauthier, P., M. Tanguay, S. Laroche, S. Pellerin, and J. Morneau, 2007: Extension of 3DVAR to 4DVAR: Implementation of 4DVAR at the Meteorological Service of Canada. Mon. Wea. Rev., 135, 2339–2354, doi:10.1175/MWR3394.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gilbert, J. C., and C. Lemaréchal, 1989: Some numerical experiments with variable-storage quasi-Newton algorithms. Math. Programm., 45, 407–435, doi:10.1007/BF01589113.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Glisan, J. M., W. J. Gutowski Jr., J. J. Cassano, and M. E. Higgins, 2013: Effects of spectral nudging in WRF on arctic temperature and precipitation simulations. J. Climate, 26, 3985–3999, doi:10.1175/JCLI-D-12-00318.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gustafsson, N., E. Källén, and S. Thorsteinsson, 1998: Sensitivity of forecast errors to initial and lateral boundary conditions. Tellus, 50A, 167–185, doi:10.1034/j.1600-0870.1998.t01-1-00002.x.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Houtekamer, P. L., X. Deng, H. L. Mitchell, S.-J. Baek, and N. Gagnon, 2014: Higher resolution in an operational ensemble Kalman filter. Mon. Wea. Rev., 142, 1143–1162, doi:10.1175/MWR-D-13-00138.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kanamaru, H., and M. Kanamitsu, 2007: Scale-selective bias correction in a downscaling of global analysis using a regional model. Mon. Wea. Rev., 135, 334–350, doi:10.1175/MWR3294.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Laprise, R., 1992: The Euler equations of motion with hydrostatic pressure as an independent variable. Mon. Wea. Rev., 120, 197–207, doi:10.1175/1520-0493(1992)120<0197:TEEOMW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Laroche, S., P. Gauthier, J. St-James, and J. Morneau, 1999: Implementation of a 3D variational data assimilation system at the Canadian Meteorological Centre. Part II: The regional analysis. Atmos.–Ocean, 37, 281–307, doi:10.1080/07055900.1999.9649630.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Laroche, S., P. Gauthier, M. Tanguay, S. Pellerin, and J. Morneau, 2007: Impact of the different components of 4DVAR on the Global Forecast System of the Meteorological Service of Canada. Mon. Wea. Rev., 135, 2355–2364, doi:10.1175/MWR3408.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Mailhot, J., and Coauthors, 2006: The 15-km version of the Canadian Regional Forecast System. Atmos.–Ocean, 44, 133–149, doi:10.3137/ao.440202.

  • Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343–360, doi:10.1175/BAMS-87-3-343.

  • Miguez-Macho, G., G. L. Stenchikov, and A. Robock, 2004: Spectral nudging to eliminate the effects of domain position and geometry in regional climate model simulations. J. Geophys. Res., 109, D13104, doi:10.1029/2003JD004495.

    • Search Google Scholar
    • Export Citation

  • Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center’s spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120, 1747–1763, doi:10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rodwell, M. J., and T. N. Palmer, 2007: Using numerical weather prediction to assess climate models. Quart. J. Roy. Meteor. Soc., 133, 129–146, doi:10.1002/qj.23.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rodwell, M. J., and T. Jung, 2008: Understanding the local and global impacts of model physics changes: An aerosol example. Quart. J. Roy. Meteor. Soc., 134, 1479–1497, doi:10.1002/qj.298.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Scinocca, J., and Coauthors, 2016: Coordinated global and regional climate modeling. J. Climate, 29, 17–35, doi:10.1175/JCLI-D-15-0161.1.

  • Šeparović, L., A. Alexandru, R. Laprise, A. Martynov, L. Sushama, K. Winger, K. Tete, and M. Valin, 2013: Present climate and climate change over North America as simulated by the fifth-generation Canadian regional climate model. Climate Dyn., 41, 3167–3201, doi:10.1007/s00382-013-1737-5.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • von Storch, H., H. Langenberg, and F. Feser, 2000: A spectral nudging technique for dynamical downscaling purposes. Mon. Wea. Rev., 128, 3664–3673, doi:10.1175/1520-0493(2000)128<3664:ASNTFD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Warner, T. T., R. A. Peterson, and R. E. Treadon, 1997: A tutorial on lateral boundary conditions as a basic and potentially serious limitation to regional numerical weather prediction. Bull. Amer. Meteor. Soc., 78, 2599–2617, doi:10.1175/1520-0477(1997)078<2599:ATOLBC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zadra, A., D. Caya, J. Côté, B. Dugas, C. Jones, R. Laprise, K. Winger, and L.-P. Caron, 2008: The next Canadian regional climate model. Phys. Can., 64, 75–83.

    • Search Google Scholar
    • Export Citation

  • Zhong, Z., X. Wang, W. Lu, and Y. Hu, 2010: Further study on the effect of buffer zone size on regional climate modeling. Climate Dyn., 35, 1027–1038, doi:10.1007/s00382-009-0662-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1664 556 61
PDF Downloads 846 214 16