Impact of Lateral Boundary Errors on the Simulation of Clouds with a Nonhydrostatic Regional Climate Model

Junya Uchida Atmosphere and Ocean Research Institute, University of Tokyo, Chiba, Japan

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Masato Mori Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan

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Masayuki Hara Center for Environmental Science in Saitama, Saitama, Japan

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Masaki Satoh Atmosphere and Ocean Research Institute, University of Tokyo, Chiba, Japan

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Daisuke Goto National Institute for Environmental Studies, Ibaraki, Japan

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Takahito Kataoka Atmosphere and Ocean Research Institute, University of Tokyo, Chiba, Japan

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Kentaroh Suzuki Atmosphere and Ocean Research Institute, University of Tokyo, Chiba, Japan

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Teruyuki Nakajima Earth Observation Research Center, Japan Aerospace Exploration Agency (JAXA), Ibaraki, Japan

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Abstract

A nonhydrostatic, regional climate limited-area model (LAM) was used to analyze lateral boundary condition (LBC) errors and their influence on the uncertainties of regional models. Simulations using the fully compressible nonhydrostatic LAM (D-NICAM) were compared against the corresponding global quasi-uniform-grid Nonhydrostatic Icosahedral Atmospheric Model (NICAM) and a stretched-grid counterpart (S-NICAM). By this approach of sharing the same dynamical core and physical schemes, possible causes of model bias and LBC errors are isolated. The simulations were performed for a 395-day period from March 2011 through March 2012 with horizontal grid intervals of 14, 28, and 56 km in the region of interest. The resulting temporal mean statistics of the temperatures at 500 hPa were generally well correlated between the global and regional simulations, indicating that LBC errors had a minor impact on the large-scale flows. However, the time-varying statistics of the surface precipitation showed that the LBC errors lead to the unpredictability of convective precipitation, which affected the mean statistics of the precipitation distributions but induced only minor influences on the large-scale systems. Specifically, extratropical cyclones and orographic precipitation are not severely affected. It was concluded that the errors of the precipitation distribution are not due to the difference of the model configurations but rather to the uncertainty of the system itself. This study suggests that applications of ensemble runs, internal nudging, or simulations with longer time scales are needed to obtain more statistically significant results of the precipitation distribution in regional climate models.

© 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: Junya Uchida, junya@aori.u-tokyo.ac.jp

Abstract

A nonhydrostatic, regional climate limited-area model (LAM) was used to analyze lateral boundary condition (LBC) errors and their influence on the uncertainties of regional models. Simulations using the fully compressible nonhydrostatic LAM (D-NICAM) were compared against the corresponding global quasi-uniform-grid Nonhydrostatic Icosahedral Atmospheric Model (NICAM) and a stretched-grid counterpart (S-NICAM). By this approach of sharing the same dynamical core and physical schemes, possible causes of model bias and LBC errors are isolated. The simulations were performed for a 395-day period from March 2011 through March 2012 with horizontal grid intervals of 14, 28, and 56 km in the region of interest. The resulting temporal mean statistics of the temperatures at 500 hPa were generally well correlated between the global and regional simulations, indicating that LBC errors had a minor impact on the large-scale flows. However, the time-varying statistics of the surface precipitation showed that the LBC errors lead to the unpredictability of convective precipitation, which affected the mean statistics of the precipitation distributions but induced only minor influences on the large-scale systems. Specifically, extratropical cyclones and orographic precipitation are not severely affected. It was concluded that the errors of the precipitation distribution are not due to the difference of the model configurations but rather to the uncertainty of the system itself. This study suggests that applications of ensemble runs, internal nudging, or simulations with longer time scales are needed to obtain more statistically significant results of the precipitation distribution in regional climate models.

© 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: Junya Uchida, junya@aori.u-tokyo.ac.jp
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  • Antic, S., R. Laprise, B. Denis, and R. de Elía, 2006: Testing the downscaling ability of a one-way nested regional climate model in regions of complex topography. Climate Dyn., 26, 305325, https://doi.org/10.1007/s00382-005-0046-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ban, N., J. Schmidli, and C. Schär, 2014: Evaluation of the convection-resolving regional climate modeling approach in decade-long simulations. J. Geophys. Res. Atmos., 119, 78897907, doi:10.1002/2014JD021478.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ban, N., J. Schmidli, and C. Schär, 2015: Heavy precipitation in a changing climate: Does short-term summer precipitation increase faster? Geophys. Res. Lett., 42, 11651172, https://doi.org/10.1002/2014GL062588.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beniston, M., and Coauthors, 2007: Current and future extreme climatic events in Europe: Observations and modeling studies conducted within the EU PRUDENCE project. Climatic Change, 81, 7195, https://doi.org/10.1007/s10584-006-9226-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Caian, M., and J.-F. Geleyn, 1997: Some limits to the variable-mesh solution and comparison with the nested-LAM solution. Quart. J. Roy. Meteor. Soc., 123, 743766, https://doi.org/10.1002/qj.49712353911.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chapman, E. G., W. I. Gustafson Jr., J. C. Barnard, S. J. Ghan, M. S. Pekour, and J. D. Fast, 2009: Coupling aerosol-cloud-radiative processes in the WRF-Chem model: Investigating the radiative impact of large point sources. Atmos. Chem. Phys., 9, 945964, https://doi.org/10.5194/acp-9-945-2009.

    • 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, 627646, https://doi.org/10.1007/s00382-001-0201-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Denis, B., R. Laprise, and D. Caya, 2003: Sensitivity of a regional climate model to the spatial resolution and temporal updating frequency of lateral boundary conditions. Climate Dyn., 20, 107126, https://doi.org/10.1007/s00382-002-0264-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Déqué, M., and J. Ph. Piedelievre, 1995: High-resolution climate simulation over Europe. Climate Dyn., 11, 321339, https://doi.org/10.1007/BF00215735.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Déqué, M., and Coauthors, 2007: An intercomparison of regional climate simulations for Europe: Assessing uncertainties in model projections. Climatic Change, 81 (Suppl. 1), 5370, https://doi.org/10.1007/s10584-006-9228-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dimitrijevic, M., and R. Laprise, 2005: Validation of the nesting technique in a regional climate model and sensitivity tests to the resolution of the lateral boundary conditions during summer. Climate Dyn., 25, 555580, https://doi.org/10.1007/s00382-005-0023-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feser, F., B. Rockel, H. Von Storch, J. Winterfeldt, and M. Zahn, 2011: Regional climate models add value to global model data: A review and selected examples. Bull. Amer. Meteor. Soc., 92, 11811192, https://doi.org/10.1175/2011BAMS3061.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Flato, G., and Coauthors, 2013: Evaluation of climate models. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 741–866.

  • Flesch, T. K., and G. W. Reuter, 2012: WRF Model simulation of two Alberta flooding events and the impact of topography. J. Hydrometeor., 13, 695708, https://doi.org/10.1175/JHM-D-11-035.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Foley, A. M., 2010: Uncertainty in regional climate modelling: A review. Prog. Phys. Geogr., 34, 647670, https://doi.org/10.1177/0309133310375654.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fosser, G., S. Khodayar, and P. Berg, 2015: Benefit of convection permitting climate model simulations in the representation of convective precipitation. Climate Dyn., 44, 45, https://doi.org/10.1007/s00382-014-2242-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fox-Rabinovitz, M. S., J. Cote, M. Deque, B. Dugas, and J. McGregor, 2006: Variable resolution general circulation models: Stretched-Grid Model Intercomparison Project (SGMIP). J. Geophys. Res., 111, D16104, https://doi.org/10.1029/2005JD006520.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giorgi, F., and L. O. Mearns, 1991: Approaches to the simulation of regional climate change: A review. Rev. Geophys., 29, 191216, https://doi.org/10.1029/90RG02636.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giorgi, F., and L. O. Mearns, 1999: Introduction to special section: Regional Climate Modeling revisited. J. Geophys. Res., 104, 63356352, https://doi.org/10.1029/98JD02072.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giorgi, F., and Coauthors, 2001: Regional climate information—Evaluation and projections. Climate Change 2001: The Scientific Basis, J. T. Houghton et al., Eds., Cambridge University Press, 583–638, http://works.bepress.com/william-gutowski/56/.

  • Goto, D., and Coauthors, 2015: Application of a global nonhydrostatic model with a stretched-grid system to regional aerosol simulations around Japan. Geosci. Model Dev., 8, 235259, https://doi.org/10.5194/gmd-8-235-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hashimoto, A., J. M. Done, L. D. Fowler, and C. L. Bruyère, 2016: Tropical cyclone activity in nested regional and global grid-refined simulations. Climate Dyn., 47, 497508, https://doi.org/10.1007/s00382-015-2852-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Herceg, D., A. H. Sobel, L. Sun, and S. E. Zebiak, 2006: The Big Brother Experiment and seasonal predictability in the NCEP Regional Spectral Model. Climate Dyn., 27, 6982, https://doi.org/10.1007/s00382-006-0130-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., and J.-W. Lee, 2009: Assessment of the WRF model in reproducing a flash-flood heavy rainfall event over Korea. Atmos. Res., 93, 818831, https://doi.org/10.1016/j.atmosres.2009.03.015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, R. G., J. M. Murphy, and M. Noguer, 1995: Simulation of climate change over Europe using a nested regional-climate model. I: Assessment of control climate, including sensitivity to location of lateral boundaries. Quart. J. Roy. Meteor. Soc., 121, 14141449, doi:10.1002/qj.49712152610.

    • Search Google Scholar
    • Export Citation
  • Juang, H.-M. H., and S.-Y. Hong, 2001: Sensitivity of the NCEP Regional Spectral Model to domain size and nesting strategy. Mon. Wea. Rev., 129, 29042922, https://doi.org/10.1175/1520-0493(2001)129<2904:SOTNRS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Køltzow, M., T. Iversen, and J. E. Haugen, 2008: Extended big-brother experiments: The role of lateral boundary data quality and size of integration domain in regional climate modelling. Tellus, 60A, 398410, https://doi.org/10.1111/j.1600-0870.2008.00309.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Laprise, R., and Coauthors, 2008: Challenging some tenets of regional climate modelling. Meteor. Atmos. Phys., 100, 322, https://doi.org/10.1007/s00703-008-0292-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Larsen, M. A. D., P. Thejll, J. H. Christensen, J. C. Refsgaard, and K. H. Jensen, 2013: On the role of domain size and resolution in the simulations with the HIRHAM region climate model. Climate Dyn., 40, 29032918, https://doi.org/10.1007/s00382-012-1513-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leung, L. R., and S. J. Ghan, 1998: Parameterizing subgrid orographic precipitation and surface cover in climate models. Mon. Wea. Rev., 126, 32713291, https://doi.org/10.1175/1520-0493(1998)126<3271:PSOPAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leung, L. R., L. O. Mearns, F. Giorgi, and R. L. Wilby, 2003: Regional climate research: Needs and opportunity. Bull. Amer. Meteor. Soc., 84, 8995, https://doi.org/10.1175/BAMS-84-1-89.

    • Search Google Scholar
    • Export Citation
  • Lo, J. C., Z.-L. Yang, and R. A. Pielke Sr., 2008: Assessment of three dimensional dynamical climate downscaling methods using the Weather Research and Forecasting (WRF) model. J. Geophys. Res., 113, D09112, https://doi.org/10.1029/2007JD009216.

    • Search Google Scholar
    • Export Citation
  • Matte, D., R. Laprise, and J. Thériault, 2016: Comparison between high-resolution climate simulations using single- and double-nesting approaches within the Big-Brother experimental protocol. Climate Dyn., 47, 36133626, https://doi.org/10.1007/s00382-016-3031-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McGregor, J. L., 1997: Regional climate modelling. Meteor. Atmos. Phys., 63, 105117, https://doi.org/10.1007/BF01025367.

  • Mearns, L. O., F. Giorgi, P. Whetton, D. Pabon, M. Hulme, and M. Lal, 2003: Guidelines for use of climate scenarios developed from regional climate model experiments. Data Distribution Centre, Intergovernmental Panel on Climate Change, 38 pp., http://www.ipcc-data.org/guidelines/dgm_no1_v1_10-2003.pdf.

  • Mesinger, F., K. Veljovic, M. J. Fennessy, and E. L. Altshuler, 2012: Value added in regional climate modeling: Should one aim to improve on the large scales as well? Climate Change, A. Berger, F. Mesinger, and D. Sijacki, Eds., Springer, 201–214, https://doi.org/10.1007/978-3-7091-0973-1_15.

    • Crossref
    • Export Citation
  • Nakanishi, M., and H. Niino, 2004: An improved Mellor–Yamada level-3 model with condensation physics: Its design and verification. Bound-Layer Meteor., 112, 131, doi:10.1023/B:BOUN.0000020164.04146.98.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakanishi, M., and H. Niino, 2009: Development of an improved turbulence closure model for the atmospheric boundary layer. J. Meteor. Soc. Japan, 87, 895912, https://doi.org/10.2151/jmsj.87.895.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Noda, A. T., K. Oouchi, M. Satoh, H. Tomita, S. Iga, and Y. Tsushima, 2010: Importance of the subgrid-scale turbulent moist process: Cloud distribution in global cloud-resolving simulations. Atmos. Res., 96, 208217, https://doi.org/10.1016/j.atmosres.2009.05.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Noguer, M., R. Jones, and J. Murphy, 1998: Sources of systematic errors in the climatology of a regional climate model over Europe. Climate Dyn., 14, 691712, https://doi.org/10.1007/s003820050249.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prein, A. F., A. Gobiet, M. Suklitsch, H. Truhetz, N. K. Awan, K. Keuler, and G. Georgievski, 2013a: Added value of convection permitting seasonal simulations. Climate Dyn., 41, 26552677, https://doi.org/10.1007/s00382-013-1744-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prein, A. F., G. J. Holland, R. M. Rasmussen, J. Done, K. Ikeda, M. P. Clark, and C. H. Liu, 2013b: Importance of regional climate model grid spacing for the simulation of heavy precipitation in the Colorado headwaters. J. Climate, 26, 48484857, https://doi.org/10.1175/JCLI-D-12-00727.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prein, A. F., and Coauthors, 2015: A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges. Rev. Geophys., 53, 323361, https://doi.org/10.1002/2014RG000475.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Putman, W. M., and M. Suarez, 2011: Cloud-system resolving simulations with the NASA Goddard Earth Observing System global atmospheric model (GEOS-5). Geophys. Res. Lett., 38, L16809, https://doi.org/10.1029/2011GL048438.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ratnam, J. V., Y. Morioka, S. K. Behera, and T. Yamagata, 2015: A model study of regional air-sea interaction in the austral summer precipitation over southern Africa. J. Geophys. Res. Atmos., 120, 23422357, doi:10.1002/2014JD022154.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ratnam, J. V., S. K. Behera, T. Doi, S. B. Ratna, and W. A. Landman, 2016: Improvements to the WRF seasonal hindcasts over South Africa by bias correcting the driving SINTEX-F2v CGCM fields. J. Climate, 29, 28152829, https://doi.org/10.1175/JCLI-D-15-0435.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rauscher, S. A., T. D. Ringler, W. C. Skamarock, and A. A. Mirin, 2013: Exploring a global multiresolution modeling approach using aquaplanet simulations. J. Climate, 26, 24322452, doi: http://dx.doi.org/10.1175/JCLI-D-12-00154.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rummukainen, M., 2010: State-of-the-art with regional climate models. Wiley Interdiscip. Rev.: Climate Change, 1, 8296, https://doi.org/10.1002/wcc.8.

    • Search Google Scholar
    • Export Citation
  • Sakaguchi, K., and Coauthors, 2015: Exploring a multiresolution approach using AMIP simulations. J. Climate, 28, 55495574, https://doi.org/10.1175/JCLI-D-14-00729.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sasaki, H., K. Kurihara, I. Takayabu, and T. Uchiyama, 2008: Preliminary experiments of reproducing the present climate using the non-hydrostatic regional climate model. Sci. Online Lett. Atmos., 4, 2528.

    • Search Google Scholar
    • Export Citation
  • Satoh, M., T. Matsuno, H. Tomita, H. Miura, T. Nasuno, and S. Iga, 2008: Nonhydrostatic Icosahedral Atmospheric Model (NICAM) for global cloud resolving simulations. J. Comput. Phys., 227, 34863514, https://doi.org/10.1016/j.jcp.2007.02.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Satoh, M., and Coauthors, 2014: The Non-hydrostatic Icosahedral Atmospheric Model: Description and development. Prog. Earth Planet. Sci., 1, 18, https://doi.org/10.1186/s40645-014-0018-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sekiguchi, M., and T. Nakajima, 2008: A k-distribution-based radiation code and its computational optimization for an atmospheric general circulation model. J. Quant. Spectrosc. Radiat. Transfer, 109, 27792793, https://doi.org/10.1016/j.jqsrt.2008.07.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., http://dx.doi.org/10.5065/D68S4MVH.

    • Crossref
    • Export Citation
  • Skamarock, W. C., J. B. Klemp, M. G. Duda, L. Fowler, S.-H. Park, and T. D. Ringler, 2012: A multiscale nonhydrostatic atmospheric model using centroidal Voronoi tesselations and C-grid staggering. Mon. Wea. Rev., 140, 30903105, https://doi.org/10.1175/MWR-D-11-00215.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Takata, K., S. Emori, and S. Watanabe, 2003: Development of the minimal advanced treatments of surface interaction and runoff. Global Planet. Change, 38, 209222, https://doi.org/10.1016/S0921-8181(03)00030-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tomita, H., 2008a: A stretched grid on a sphere by new grid transformation. J. Meteor. Soc. Japan, 86A, 107119, https://doi.org/10.2151/jmsj.86A.107.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tomita, H., 2008b: New microphysics with five and six categories with diagnostic generation of cloud ice. J. Meteor. Soc. Japan, 86A, 121142, https://doi.org/10.2151/jmsj.86A.121.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tomita, H., and M. Satoh, 2004: A new dynamical framework of nonhydrostatic global model using the icosahedral grid. Fluid Dyn. Res., 34, 357400, https://doi.org/10.1016/j.fluiddyn.2004.03.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Uchida, J., M. Mori, H. Nakamura, M. Satoh, K. Suzuki, and T. Nakajima, 2016: Error and energy budget analysis of a nonhydrostatic stretched-grid global atmospheric model. Mon. Wea. Rev., 144, 14231447, https://doi.org/10.1175/MWR-D-15-0271.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y., L. R. Leung, J. L. McGregor, D.-K. Lee, W.-C. Wang, Y. Ding, and F. Kimura, 2004: Regional climate modeling: Progress, challenges, and prospects. J. Meteor. Soc. Japan, 82, 15991628, https://doi.org/10.2151/jmsj.82.1599.

    • 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, 25992617, https://doi.org/10.1175/1520-0477(1997)078<2599:ATOLBC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zarzycki, C. M., M. N. Levy, C. Jablonowski, J. R. Overfelt, M. A. Taylor, and P. A. Ullrich, 2014: Aquaplanet experiments using CAM’s variable-resolution dynamical core. J. Climate, 27, 54815503, https://doi.org/10.1175/JCLI-D-14-00004.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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