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Article Type:
Research Article

Summer Land–Atmosphere Coupling Strength over the United States: Results from the Regional Climate Model RegCM4–CLM3.5

Rui Mei Department of Civil and Environmental Engineering, and Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, Connecticut

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Guiling Wang Department of Civil and Environmental Engineering, and Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, Connecticut

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Huanghe Gu Department of Civil and Environmental Engineering, and Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, Connecticut, and State Key Laboratory of Hydrology–Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China

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Print Publication:
01 Jun 2013
DOI:
https://doi.org/10.1175/JHM-D-12-043.1
Page(s):
946–962
Restricted access

Abstract

This study investigates the land–atmosphere coupling strength during summer over the United States using the Regional Climate Model version 4 (RegCM4)–Community Land Model version 3.5 (CLM3.5). First, a 10-yr simulation driven with reanalysis lateral boundary conditions (LBCs) is conducted to evaluate the model performance. The model is then used to quantify the land–atmosphere coupling strength, predictability, and added forecast skill (for precipitation and 2-m air temperature) attributed to realistic land surface initialization following the Global Land–Atmosphere Coupling Experiment (GLACE) approaches. Similar to previous GLACE results using global climate models (GCMs), GLACE-type experiments with RegCM4 identify the central United States as a region of strong land–atmosphere coupling, with soil moisture–temperature coupling being stronger than soil moisture–precipitation coupling, and confirm that realistic soil moisture initialization is more promising in improving temperature forecasts than precipitation forecasts. At a 1–15-day lead, the added forecast skill reflects predictability (or land–atmosphere coupling strength) indicating that that model can capture the realistic land–atmosphere coupling at a short time scale. However, at a 16–30-day lead, predictability cannot translate to added forecast skill, implying that the coupling at the longer time scale may not be represented well in the model. In addition, comparison of results from GLACE2-type experiments with RegCM4 driven by reanalysis LBCs and those driven by GCM LBCs suggest that the intrinsic land–atmosphere coupling strength within the regional model is the dominant factor for the added forecast skill at a 1–15-day lead, while the impact of LBCs from the GCM may play a dominant role in determining the signal of added forecast skill in the regional model at a 16–30-day lead. It demonstrates the complexities of using regional climate model for GLACE-type studies.

Corresponding author address: Guiling Wang, Department of Civil and Environmental Engineering, University of Connecticut, 261 Glenbrook Rd., Storrs, CT 06269-2037. E-mail: gwang@engr.uconn.edu

Abstract

This study investigates the land–atmosphere coupling strength during summer over the United States using the Regional Climate Model version 4 (RegCM4)–Community Land Model version 3.5 (CLM3.5). First, a 10-yr simulation driven with reanalysis lateral boundary conditions (LBCs) is conducted to evaluate the model performance. The model is then used to quantify the land–atmosphere coupling strength, predictability, and added forecast skill (for precipitation and 2-m air temperature) attributed to realistic land surface initialization following the Global Land–Atmosphere Coupling Experiment (GLACE) approaches. Similar to previous GLACE results using global climate models (GCMs), GLACE-type experiments with RegCM4 identify the central United States as a region of strong land–atmosphere coupling, with soil moisture–temperature coupling being stronger than soil moisture–precipitation coupling, and confirm that realistic soil moisture initialization is more promising in improving temperature forecasts than precipitation forecasts. At a 1–15-day lead, the added forecast skill reflects predictability (or land–atmosphere coupling strength) indicating that that model can capture the realistic land–atmosphere coupling at a short time scale. However, at a 16–30-day lead, predictability cannot translate to added forecast skill, implying that the coupling at the longer time scale may not be represented well in the model. In addition, comparison of results from GLACE2-type experiments with RegCM4 driven by reanalysis LBCs and those driven by GCM LBCs suggest that the intrinsic land–atmosphere coupling strength within the regional model is the dominant factor for the added forecast skill at a 1–15-day lead, while the impact of LBCs from the GCM may play a dominant role in determining the signal of added forecast skill in the regional model at a 16–30-day lead. It demonstrates the complexities of using regional climate model for GLACE-type studies.

Corresponding author address: Guiling Wang, Department of Civil and Environmental Engineering, University of Connecticut, 261 Glenbrook Rd., Storrs, CT 06269-2037. E-mail: gwang@engr.uconn.edu
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  • Alfaro, S. C., and Gomes L. , 2001: Modeling mineral aerosol production by wind erosion: Emission intensities and aerosol size distributions in source areas. J. Geophys. Res., 106 (D16), 18 075–18 084.

    • Search Google Scholar
    • Export Citation

  • Anthes, R. A., 1977: A cumulus parameterization scheme utilizing a one-dimensional cloud model. Mon. Wea. Rev., 105, 270–286.

  • Delworth, T. L., and Manabe S. , 1989: The influence of soil wetness on near-surface atmospheric variability. J. Climate, 2, 1447–1462.

    • Search Google Scholar
    • Export Citation

  • Dickinson, R. E., Henderson-Sellers A. , and Kennedy P. J. , 1993: Biosphere-atmosphere transfer scheme (BATS) version 1 as coupled to the NCAR Community Climate Model. NCAR Tech. Note NCAR/TN-387+STR, 72 pp. [Available online at http://nldr.library.ucar.edu/collections/technotes/asset-000-000-000-198.pdf.]

  • Dirmeyer, P. A., Guo Z. , and Gao X. , 2004: Comparison, validation, and transferability of eight multiyear global soil wetness products. J. Hydrometeor., 5, 1011–1033.

    • Search Google Scholar
    • Export Citation

  • Dirmeyer, P. A., Gao X. , Zhao M. , Guo Z. , Oki T. , and Hanasaki N. , 2006a: GSWP-2: Multimodel analysis and implications for our perception of the land surface. Bull. Amer. Meteor. Soc., 87, 1381–1397.

    • Search Google Scholar
    • Export Citation

  • Dirmeyer, P. A., Koster R. D. , and Guo Z. , 2006b: Do global models properly represent the feedback between land and atmosphere? J. Hydrometeor., 7, 1177–1198.

    • Search Google Scholar
    • Export Citation

  • Elguindi, N., and Coauthors, 2011: Regional climatic model RegCM user manual version 4.1. Abdus Salam International Centre for Theoretical Physics, Trieste, Italy, 32 pp. [Available online at http://gforge.ictp.it/gf/download/docmanfileversion/21/544/ReferenceMan4.1.pdf.]

  • Emanuel, K. A., 1991: A scheme for representing cumulus convection in large-scale models. J. Atmos. Sci., 48, 2313–2335.

  • Giorgi, F., and Coauthors, 2012: RegCM4: Model description and preliminary tests over multiple CORDEX domains. Climate Res., 52, 7–29.

    • Search Google Scholar
    • Export Citation

  • Grell, G. A., 1993: Prognostic evaluation of assumptions used by cumulus parameterizations. Mon. Wea. Rev., 121, 764–787.

  • Grell, G. A., Dudhia J. , and Stauffer D. R. , 1994: A description of the fifth generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note NCAR/TN-398+STR, 121 pp. [Available online at http://www.mmm.ucar.edu/mm5/documents/mm5-desc-pdf/cover.pdf.]

  • Guo, Z., and Coauthors, 2006: GLACE: The Global Land–Atmosphere Coupling Experiment. Part II: Analysis. J. Hydrometeor., 7, 611–625.

    • Search Google Scholar
    • Export Citation

  • Holtslag, A. A. M., de Bruijn E. I. F. , and Pan H. L. , 1990: A high-resolution air mass transformation model for short-range weather forecasting. Mon. Wea. Rev., 118, 1561–1575.

    • Search Google Scholar
    • Export Citation

  • Kanamistu, M., Ebisuzaki W. , Woollen J. , Yang S.-K. , Hnilo J. J. , Fiorino M. , and Potter G. L. , 2002: NCEP–DOE AMIP-II reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 1631–1643.

    • Search Google Scholar
    • Export Citation

  • Kiehl, J. T., Hack J. J. , Bonan G. B. , B. A. Boville, B. P. Briegleb, D. L. Williamson, and P. J. Rasch, 1996: Description of the NCAR Community Climate Model (CCM3). NCAR Tech. Note NCAR/TN-420+STR, 152 pp. [Available online at http://nldr.library.ucar.edu/collections/technotes/asset-000-000-000-231.pdf.]

  • Kim, Y. J., and Wang G. L. , 2007: Impact of initial soil moisture anomalies on subsequent precipitation over North America in the coupled land–atmosphere model CAM3–CLM3. J. Hydrometeor., 8, 513–533.

    • Search Google Scholar
    • Export Citation

  • Koster, R. D., Dirmeyer P. A. , Hahmann A. N. , Ijpelaar R. , Tyahla L. , Cox P. , and Suarez M. J. , 2002: Comparing the degree of land–atmosphere interaction in four atmospheric general circulation models. J. Hydrometeor., 3, 363–375.

    • Search Google Scholar
    • Export Citation

  • Koster, R. D., and Coauthors, 2004a: Regions of strong coupling between soil moisture and precipitation. Science, 305, 1138–1140.

  • Koster, R. D., and Coauthors, 2004b: Realistic initialization of land surface states: Impacts on subseasonal forecast skill. J. Hydrometeor., 5, 1049–1063.

    • Search Google Scholar
    • Export Citation

  • Koster, R. D., and Coauthors, 2006: GLACE: The Global Land–Atmosphere Coupling Experiment: Part 1: Overview. J. Hydrometeor., 7, 590–610.

    • Search Google Scholar
    • Export Citation

  • Koster, R. D., Guo Z. , Yang R. , Dirmeyer P. A. , Mitchell K. , and Puma M. J. , 2009: On the nature of soil moisture in land surface models. J. Climate, 22, 4322–4355.

    • Search Google Scholar
    • Export Citation

  • Koster, R. D., and Coauthors, 2010: The contribution of land surface initialization to subseasonal forecast skill: First results from a multi-model experiment. Geophys. Res. Lett., 37, L02402, doi:10.1029/2009GL041677.

    • Search Google Scholar
    • Export Citation

  • Koster, R. D., and Coauthors, 2011: The second phase of the global land–atmosphere coupling experiment: Soil moisture contributions to subseasonal forecast skill. J. Hydrometeor., 12, 805–822.

    • Search Google Scholar
    • Export Citation

  • Laurent, B., Marticorena B. , Bergametti G. , Leon J. , and Mahowald N. , 2008: Modeling mineral dust emissions from the Sahara desert using new surface properties and soil database. J. Geophys. Res., 113, D14218, doi:10.1029/2007JD009484.

    • Search Google Scholar
    • Export Citation

  • Leung, L. R., Qian Y. , and Bian X. D. , 2003: Hydroclimate of the western United States based on observations and regional climate simulation of 1981–2000. Part I: Seasonal statistics. J. Climate, 16, 1892–1911.

    • Search Google Scholar
    • Export Citation

  • Liang, X. Z., Li L. , Kunkel K. E. , Ting M. , and Wang J. X. L. , 2004: Regional climate model simulation of U.S. precipitation during 1982–2002. Part I: Annual cycle. J. Climate, 17, 3510–3528.

    • Search Google Scholar
    • Export Citation

  • Mei, R., and Wang G. L. , 2012: Summer land–atmosphere coupling strength in the United States: Comparison among observations, reanalysis data and numerical models. J. Hydrometeor., 13, 1010–1022.

    • Search Google Scholar
    • Export Citation

  • Mitchell, T. D., and Jones P. D. , 2005: An improved method of constructing a database of monthly climate observations and associated high resolution grids. Int. J. Climatol., 25, 693–712, doi:10.1002/joc.1181.

    • Search Google Scholar
    • Export Citation

  • Mo, K. C., Schemm J.-K. , Juang H. M. H. , Higgins R. W. , and Song Y. , 2005: Impact of model resolution on the prediction of summer precipitation over the United States and Mexico. J. Climate, 18, 3910–3927.

    • Search Google Scholar
    • Export Citation

  • Neale, R. B., and Coauthors, 2010: Description of the NCAR Community Atmosphere Model (CAM5.0). NCAR Tech. Note NCAR/TN-486+STR, 268 pp. [Available online at http://www.cesm.ucar.edu/models/cesm1.1/cam/docs/description/cam5_desc.pdf.]

  • Notaro, M., Liu Z. , and Williams J. W. , 2006: Observed vegetation–climate feedbacks in the United States. J. Climate, 19, 763–786.

  • Oglesby, R. J., and Erickson D. J. III, 1989: Soil moisture and the persistence of North American drought. J. Climate, 2, 1362–1380.

  • Oglesby, R. J., Marshall S. , Erickson D. J. III, Roads J. O. , and Robertson F. R. , 2002: Thresholds in atmosphere–soil moisture interactions: Results from climate model studies. J. Geophys. Res., 107, 4244, doi:10.1029/2001JD001045.

    • Search Google Scholar
    • Export Citation

  • Oleson, K. W., and Coauthors, 2008: Improvements to the Community Land Model and their impact on the hydrological cycle. J. Geophys. Res., 113, G01021, doi:10.1029/2007JG000563.

    • Search Google Scholar
    • Export Citation

  • Oleson, K. W., and Coauthors, 2010: Technical description of version 4.0 of the Community Land Model (CLM). NCAR Tech. Note NCAR/TN-478+STR, 257 pp. [Available online at http://www.cesm.ucar.edu/models/cesm1.0/clm/CLM4_Tech_Note.pdf.]

  • Pal, J. S., Small E. F. , and Eltahir E. A. B. , 2000: Simulation of regional-scale water and energy budgets: Representation of subgrid cloud and precipitation processes with RegCM. J. Geophys. Res., 105 (D24), 29 579–29 594.

    • Search Google Scholar
    • Export Citation

  • Pal, J. S., and Coauthors, 2007: Regional climate modeling for the developing world: The ICTP RegCM3 and RegCNET. Bull. Amer. Meteor. Soc., 88, 1395–1409.

    • Search Google Scholar
    • Export Citation

  • Qian, T., Dai A. , Trenberth K. E. , and Oleson K. W. , 2006: Simulation of global land surface conditions from 1948 to 2004: Part I: Forcing data and evaluations. J. Hydrometeor., 7, 953–975.

    • Search Google Scholar
    • Export Citation

  • Rayner, N. A., and Coauthors, cited 2006: Met Office’s Global Ice coverage and Sea Surface Temperature (GISST), and Monthly night marine air temperature/SST anomalies (MOHMATN4)/(MOHSST6) data (1856–2006). NCAS British Atmospheric Data Centre. [Available online at http://badc.nerc.ac.uk/view/badc.nerc.ac.uk__ATOM__dataent_GISST.]

  • Road, J., Chen S. C. , and Kanamistu M. , 2003: U.S. regional climate simulations and seasonal forecasts. J. Geophys. Res., 108, 8606, doi:10.1029/2002JD002232.

    • Search Google Scholar
    • Export Citation

  • Schaake, J. C., and Coauthors, 2004: An intercomparison of soil moisture fields in the North American Land Data Assimilation System (NLDAS). J. Geophys. Res., 109, D01S90, doi:10.1029/2002JD003309.

    • Search Google Scholar
    • Export Citation

  • Schar, C., Luthi D. , Beyerle U. , and Heise E. , 1999: The soil–precipitation feedback: A process study with a regional climate model. J. Climate, 12, 722–741.

    • Search Google Scholar
    • Export Citation

  • Seneviratne, S. I., Luthi D. , Litschi M. , and Schar C. , 2006: Land–atmosphere coupling and climate change in Europe. Nature, 443, 205–209.

    • Search Google Scholar
    • Export Citation

  • Simmons, A., Uppala S. , Dee D. , and Kobayashi S. , 2007: ERA-Interim: New ECMWF reanalysis products from 1989 onwards. ECMWF Newsletter, No. 110, ECMWF, Reading, United Kingdom, 25–35.

  • Steiner, A. L., Pal J. S. , Rauscher S. A. , Bell J. L. , Diffenbaugh N. S. , Boone A. , Sloan L. C. , and Giorgi F. , 2009: Land surface coupling in regional climate simulations of the West African monsoon. Climate Dyn., 33, 869–892, doi:10.1007/s00382-009-0543-6.

    • Search Google Scholar
    • Export Citation

  • Tawfik, A. B., and Steiner A. L. , 2011: The role of soil ice in land-atmosphere coupling over the United States: A soil moisture–precipitation winter feedback mechanism. J. Geophys. Res., 116, D02113, doi:10.1029/2010JD014333.

    • Search Google Scholar
    • Export Citation

  • Walker, M. D., and Diffenbaugh N. S. , 2009: Evaluation of high-resolution simulations of daily-scale temperature and precipitation over the United States. Climate Dyn., 33, 1131–1147.

    • Search Google Scholar
    • Export Citation

  • Wang, G. L., Kim Y. J. , and Wang D. G. , 2007: Quantifying the strength of soil moisture–precipitation coupling and its sensitivity to changes in surface water budget. J. Hydrometeor., 8, 551–570.

    • Search Google Scholar
    • Export Citation

  • Wang, S. Y., Gillies R. R. , Takle E. S. , and Gutowski W. J. , 2009: Evaluation of precipitation in the Intermountain Region as simulated by the NARCCAP regional climate models. Geophys. Res. Lett., 36, L11704, doi:10.1029/2009GL037930.

    • Search Google Scholar
    • Export Citation

  • Xue, Y. K., Vasic R. , Janjic Z. , Liu Y. M. , and Chu P. C. , 2012: The impact of spring subsurface soil temperature anomaly in the western U.S. on North American summer precipitation: A case study using regional climate model downscaling. J. Geophys. Res., 117, D11103, doi:10.1029/2012JD017692.

    • Search Google Scholar
    • Export Citation

  • Yamada, T. J., Koster R. D. , Kanae S. , and Oki T. , 2007: Estimation of predictability with a newly derived index to quantify similarity among ensemble members. Mon. Wea. Rev., 135, 2674–2687.

    • Search Google Scholar
    • Export Citation

  • Zeng, X., and Beljaars A. , 2005: A prognostic scheme of sea surface skin temperature for modeling and data assimilation. Geophys. Res. Lett., 32, L14605, doi:10.1029/2005GL023030.

    • Search Google Scholar
    • Export Citation

  • Zhang, J., Wang W.-C. , and Leung L. R. , 2008: Contribution of land–atmosphere coupling to summer climate variability over the contiguous United States. J. Geophys. Res., 113, D22109, doi:10.1029/2008JD010136.

    • Search Google Scholar
    • Export Citation
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