Assessment of a High-Resolution Climate Model for Surface Water and Energy Flux Simulations over Global Land: An Intercomparison with Reanalyses

Di Tian Department of Crop, Soil, and Environmental Sciences, Auburn University, Auburn, Alabama

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Ming Pan Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey

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Eric F. Wood Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey

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Abstract

Land surface water and energy fluxes from the ensemble mean of the Atmospheric Model Intercomparison Project (AMIP) simulations of a Geophysical Fluid Dynamics Laboratory (GFDL) high-resolution climate model (AM2.5) were evaluated using offline simulations of a calibrated land surface model [Princeton Global Forcing (PGF)/VIC] and intercompared with three reanalysis datasets: MERRA-Land, ERA-Interim/Land, and CFSR. Using PGF/VIC as the reference, the AM2.5 precipitation, evapotranspiration, and runoff showed a global positive bias of ~0.44, ~0.27, and ~0.15 mm day−1, respectively. For the energy budget, while the AM2.5 net radiation agreed very well with the PGF/VIC, the AM2.5 improperly partitioned the net radiation, with the latent heat showing positive bias and sensible heat showing negative bias. The AM2.5 net radiation, latent heat, and sensible heat relative to the PGF/VIC had a global negative bias of ~1.42 W m−2, positive bias of ~7.8 W m−2, and negative bias of ~8.7 W m−2, respectively. The three reanalyses show greater biases in net radiation, likely due to the deficiencies in cloud parameterizations. At a regional scale, the biases of the AM2.5 water and energy budget components are mostly comparable to the three reanalyses and PGF/VIC. While the AM2.5 well simulated the actual values of water and energy fluxes, the temporal anomaly correlations of the three reanalyses with PGF/VIC were mostly greater than the AM2.5, partly due to the ensemble mean of the AM2.5 members averaging out the intrinsic variability of the land surface fluxes. The discrepancies among land surface model simulations, reanalyses, and high-resolution climate model simulations demonstrate the challenges in estimating and evaluating land surface hydrologic fluxes at regional-to-global scales.

© 2018 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: Di Tian, tiandi@auburn.edu

Abstract

Land surface water and energy fluxes from the ensemble mean of the Atmospheric Model Intercomparison Project (AMIP) simulations of a Geophysical Fluid Dynamics Laboratory (GFDL) high-resolution climate model (AM2.5) were evaluated using offline simulations of a calibrated land surface model [Princeton Global Forcing (PGF)/VIC] and intercompared with three reanalysis datasets: MERRA-Land, ERA-Interim/Land, and CFSR. Using PGF/VIC as the reference, the AM2.5 precipitation, evapotranspiration, and runoff showed a global positive bias of ~0.44, ~0.27, and ~0.15 mm day−1, respectively. For the energy budget, while the AM2.5 net radiation agreed very well with the PGF/VIC, the AM2.5 improperly partitioned the net radiation, with the latent heat showing positive bias and sensible heat showing negative bias. The AM2.5 net radiation, latent heat, and sensible heat relative to the PGF/VIC had a global negative bias of ~1.42 W m−2, positive bias of ~7.8 W m−2, and negative bias of ~8.7 W m−2, respectively. The three reanalyses show greater biases in net radiation, likely due to the deficiencies in cloud parameterizations. At a regional scale, the biases of the AM2.5 water and energy budget components are mostly comparable to the three reanalyses and PGF/VIC. While the AM2.5 well simulated the actual values of water and energy fluxes, the temporal anomaly correlations of the three reanalyses with PGF/VIC were mostly greater than the AM2.5, partly due to the ensemble mean of the AM2.5 members averaging out the intrinsic variability of the land surface fluxes. The discrepancies among land surface model simulations, reanalyses, and high-resolution climate model simulations demonstrate the challenges in estimating and evaluating land surface hydrologic fluxes at regional-to-global scales.

© 2018 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: Di Tian, tiandi@auburn.edu
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  • Balsamo, G., and Coauthors, 2015: ERA-Interim/Land: A global land surface reanalysis data set. Hydrol. Earth Syst. Sci., 19, 389407, https://doi.org/10.5194/hess-19-389-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barlage, M., M. Tewari, F. Chen, K. Manning, and G. Miguez-Macho, 2013: North American regional climate simulations with WRF/Noah-MP validation and the effect of ground water interaction. Proc. 14th WRF User’s Workshop, Boulder, CO, WRF, 40 pp., www2.mmm.ucar.edu/wrf/users/workshops/WS2013/ppts/5B.2.pdf.

  • Betts, A. K., M. Köhler, and Y. Zhang, 2009: Comparison of river basin hydrometeorology in ERA‐Interim and ERA‐40 reanalyses with observations. J. Geophys. Res., 114, D02101, https://doi.org/10.1029/2008JD010761.

    • Search Google Scholar
    • Export Citation
  • Bonan, G., 2015: Ecological Climatology: Concepts and Applications. Cambridge University Press, 563 pp.

    • Crossref
    • Export Citation
  • Dai, A., 2006: Precipitation characteristics in eighteen coupled climate models. J. Climate, 19, 46054630, https://doi.org/10.1175/JCLI3884.1.

  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Delworth, T. L., and Coauthors, 2012: Simulated climate and climate change in the GFDL CM2.5 high-resolution coupled climate model. J. Climate, 25, 27552781, https://doi.org/10.1175/JCLI-D-11-00316.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grunmann, V. Koren, G. Gayno, and J. D. Tarpley, 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108, 8851, https://doi.org/10.1029/2002JD003296.

    • Search Google Scholar
    • Export Citation
  • Fernandes, K., R. Fu, and A. K. Betts, 2008: How well does the ERA40 surface water budget compare to observations in the Amazon River basin? J. Geophys. Res., 113, D11117, https://doi.org/10.1029/2007JD009220.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giorgi, F., and R. Francisco, 2000: Evaluating uncertainties in the prediction of regional climate change. Geophys. Res. Lett., 27, 12951298, https://doi.org/10.1029/1999GL011016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harris, I., P. D. Jones, T. J. Osborn, and D. H. Lister, 2014: Updated high‐resolution grids of monthly climatic observation—The CRU TS3. 10 Dataset. Int. J. Climatol., 34, 623642, https://doi.org/10.1002/joc.3711.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jia, L., and Coauthors, 2015: Improved seasonal prediction of temperature and precipitation over land in a high-resolution GFDL climate model. J. Climate, 28, 20442062, https://doi.org/10.1175/JCLI-D-14-00112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jia, L., and Coauthors, 2016: The roles of radiative forcing, sea surface temperatures, and atmospheric and land initial conditions in US summer warming episodes. J. Climate, 29, 41214135, https://doi.org/10.1175/JCLI-D-15-0471.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., M. J. Suarez, A. Ducharne, M. Stieglitz, and P. Kumar, 2000: A catchment‐based approach to modeling land surface processes in a general circulation model: 1. Model structure. J. Geophys. Res., 105, 24 80924 822, https://doi.org/10.1029/2000JD900327.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Materia, S., A. Borrelli, A. Bellucci, A. Alessandri, P. Di Pietro, P. Athanasiadis, A. Navarra, and S. Gualdi, 2014: Impact of atmosphere and land surface initial conditions on seasonal forecasts of global surface temperature. J. Climate, 27, 92539271, https://doi.org/10.1175/JCLI-D-14-00163.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McCabe, M. F., A. Ershadi, C. Jimenez, D. G. Miralles, D. Michel, and E. F. Wood, 2016: The GEWEX LandFlux project: Evaluation of model evaporation using tower-based and globally gridded forcing data. Geosci. Model Dev., 9, 283, https://doi.org/10.5194/gmd-9-283-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mitchell, T. D., and P. D. Jones, 2005: An improved method of constructing a database of monthly climate observations and associated high‐resolution grids. Int. J. Climatol., 25, 693712, https://doi.org/10.1002/joc.1181.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Niu, G. Y., and Coauthors, 2011: The community Noah land surface model with multiparameterization options (Noah‐MP): 1. Model description and evaluation with local‐scale measurements. J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pan, M., A. K. Sahoo, T. J. Troy, R. K. Vinukollu, J. Sheffield, and E. F. Wood, 2012: Multisource estimation of long-term terrestrial water budget for major global river basins. J. Climate, 25, 31913206, https://doi.org/10.1175/JCLI-D-11-00300.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raschke, E., S. Bakan, and S. Kinne, 2006: An assessment of radiation budget data provided by the ISCCP and GEWEX‐SRB. Geophys. Res. Lett., 33, L07812, https://doi.org/10.1029/2005GL025503.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., R. D. Koster, G. J. De Lannoy, B. A. Forman, Q. Liu, S. P. Mahanama, and A. Touré, 2011: Assessment and enhancement of MERRA land surface hydrology estimates. J. Climate, 24, 63226338, https://doi.org/10.1175/JCLI-D-10-05033.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rienecker, M. M., and Coauthors, 2008: The GEOS-5 Data Assimilation System—Documentation of Versions 5.0.1, 5.1.0, and 5.2.0. NASA Tech. Memo. NASA/TM-2008-104606, Vol. 27, 97 pp., https://gmao.gsfc.nasa.gov/pubs/docs/Rienecker369.pdf.

  • Rienecker, M. M., and Coauthors, 2011: MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 36243648, https://doi.org/10.1175/JCLI-D-11-00015.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 1015, https://doi.org/10.1175/2010BAMS3001.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schaake, J. C., V. I. Koren, Q.-Y. Duan, K. Mitchell, and F. Chen, 1996: Simple water balance model for estimating runoff at different spatial and temporal scales. J. Geophys. Res., 101, 74617475, https://doi.org/10.1029/95JD02892.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sheffield, J., and E. F. Wood, 2007: Characteristics of global and regional drought, 1950–2000: Analysis of soil moisture data from off-line simulation of the terrestrial hydrologic cycle. J. Geophys. Res., 112, D17115, https://doi.org/10.1029/2006JD008288.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sheffield, J., G. Goteti, and E. F. Wood, 2006: Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J. Climate, 19, 30883111, https://doi.org/10.1175/JCLI3790.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sheffield, J., K. M. Andreadis, E. F. Wood, and D. P. Lettenmaier, 2009: Global and continental drought in the second half of the twentieth century: Severity–area–duration analysis and temporal variability of large-scale events. J. Climate, 22, 19621981, https://doi.org/10.1175/2008JCLI2722.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Szeto, K. K., 2007: Assessing water and energy budgets for the Saskatchewan River Basin. J. Meteor. Soc. Japan, 85A, 167186, https://www.jstage.jst.go.jp/article/jmsj/85A/0/85A_0_167/_pdf.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tian, D., M. Pan, L. Jia, G. Vecchi, and E. F. Wood, 2016: Assessing GFDL high‐resolution climate model water and energy budgets from AMIP simulations over Africa. J. Geophys. Res. Atmos., 121, 84448459, https://doi.org/10.1002/2016JD025068.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Troy, T. J., E. F. Wood, and J. Sheffield, 2008: An efficient calibration method for continental‐scale land surface modeling. Water Resour. Res., 44, W09411, https://doi.org/10.1029/2007WR006513.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vinukollu, R. K., J. Sheffield, E. F. Wood, M. G. Bosilovich, and D. Mocko, 2012: Multimodel analysis of energy and water fluxes: Intercomparisons between operational analyses, a land surface model, and remote sensing. J. Hydrometeor., 13, 326, https://doi.org/10.1175/2011JHM1372.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, P., and P. A. Arkin, 1997: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, 2539, https://doi.org/10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, P., M. Chen, and W. Shi, 2010: CPC unified gauge-based analysis of global daily precipitation. 24th Conf. on Hydrology, Atlanta, GA, Amer. Meteor. Soc., 2.3A, https://ams.confex.com/ams/90annual/techprogram/paper_163676.htm.

  • Yang, R., M. Ek, and J. Meng, 2015: Surface water and energy budgets for the Mississippi River basin in three NCEP reanalyses. J. Hydrometeor., 16, 857873, https://doi.org/10.1175/JHM-D-14-0056.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zib, B. J., X. Dong, B. Xi, and A. Kennedy, 2012: Evaluation and intercomparison of cloud fraction and radiative fluxes in recent reanalyses over the Arctic using BSRN surface observations. J. Climate, 25, 22912305, https://doi.org/10.1175/JCLI-D-11-00147.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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