Impact of Land Surface Initialization Approach on Subseasonal Forecast Skill: A Regional Analysis in the Southern Hemisphere

Annette L. Hirsch ARC Centre of Excellence for Climate System Science, and Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia

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Jatin Kala ARC Centre of Excellence for Climate System Science, and Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia

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Andy J. Pitman ARC Centre of Excellence for Climate System Science, and Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia

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Claire Carouge ARC Centre of Excellence for Climate System Science, and Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia

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Jason P. Evans ARC Centre of Excellence for Climate System Science, and Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia

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Vanessa Haverd CSIRO Marine and Atmospheric Research, Canberra, Australian Capital Territory, Australia

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David Mocko SAIC at NASA Goddard Space Flight Center, Greenbelt, Maryland

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Abstract

The authors use a sophisticated coupled land–atmosphere modeling system for a Southern Hemisphere subdomain centered over southeastern Australia to evaluate differences in simulation skill from two different land surface initialization approaches. The first approach uses equilibrated land surface states obtained from offline simulations of the land surface model, and the second uses land surface states obtained from reanalyses. The authors find that land surface initialization using prior offline simulations contribute to relative gains in subseasonal forecast skill. In particular, relative gains in forecast skill for temperature of 10%–20% within the first 30 days of the forecast can be attributed to the land surface initialization method using offline states. For precipitation there is no distinct preference for the land surface initialization method, with limited gains in forecast skill irrespective of the lead time. The authors evaluated the asymmetry between maximum and minimum temperatures and found that maximum temperatures had the largest gains in relative forecast skill, exceeding 20% in some regions. These results were statistically significant at the 98% confidence level at up to 60 days into the forecast period. For minimum temperature, using reanalyses to initialize the land surface contributed to relative gains in forecast skill, reaching 40% in parts of the domain that were statistically significant at the 98% confidence level. The contrasting impact of the land surface initialization method between maximum and minimum temperature was associated with different soil moisture coupling mechanisms. Therefore, land surface initialization from prior offline simulations does improve predictability for temperature, particularly maximum temperature, but with less obvious improvements for precipitation and minimum temperature over southeastern Australia.

Corresponding author address: Annette L. Hirsch, ARC Centre of Excellence for Climate System Science, Level 4 Mathews Building, University of New South Wales, Kensington, Sydney NSW 2052, Australia. E-mail: a.hirsch@student.unsw.edu.au

Abstract

The authors use a sophisticated coupled land–atmosphere modeling system for a Southern Hemisphere subdomain centered over southeastern Australia to evaluate differences in simulation skill from two different land surface initialization approaches. The first approach uses equilibrated land surface states obtained from offline simulations of the land surface model, and the second uses land surface states obtained from reanalyses. The authors find that land surface initialization using prior offline simulations contribute to relative gains in subseasonal forecast skill. In particular, relative gains in forecast skill for temperature of 10%–20% within the first 30 days of the forecast can be attributed to the land surface initialization method using offline states. For precipitation there is no distinct preference for the land surface initialization method, with limited gains in forecast skill irrespective of the lead time. The authors evaluated the asymmetry between maximum and minimum temperatures and found that maximum temperatures had the largest gains in relative forecast skill, exceeding 20% in some regions. These results were statistically significant at the 98% confidence level at up to 60 days into the forecast period. For minimum temperature, using reanalyses to initialize the land surface contributed to relative gains in forecast skill, reaching 40% in parts of the domain that were statistically significant at the 98% confidence level. The contrasting impact of the land surface initialization method between maximum and minimum temperature was associated with different soil moisture coupling mechanisms. Therefore, land surface initialization from prior offline simulations does improve predictability for temperature, particularly maximum temperature, but with less obvious improvements for precipitation and minimum temperature over southeastern Australia.

Corresponding author address: Annette L. Hirsch, ARC Centre of Excellence for Climate System Science, Level 4 Mathews Building, University of New South Wales, Kensington, Sydney NSW 2052, Australia. E-mail: a.hirsch@student.unsw.edu.au
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  • Abramowitz, G., Pitman A. J. , Gupta H. , Kowalczyk E. , and Wang Y. , 2007: Systematic bias in land surface models. J. Hydrometeor., 8, 9891001, doi:10.1175/JHM628.1.

    • Search Google Scholar
    • Export Citation
  • Abramowitz, G., Leuning R. , Clark M. , and Pitman A. J. , 2008: Evaluating the performance of land surface models. J. Climate, 21, 54685481, doi:10.1175/2008JCLI2378.1.

    • Search Google Scholar
    • Export Citation
  • Alexander, L. V., and Coauthors, 2006: Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res.,111, D05109, doi:10.1029/2005JD006290.

  • Avila, F. B., Pitman A. J. , Donat M. G. , Alexander L. V. , and Abramowitz G. , 2012: Climate model simulated changes in temperature extremes due to land cover change. J. Geophys. Res.,117, D04108, doi:10.1029/2011JD016382.

  • Beljaars, A. C. M., Viterbo P. , and Miller M. J. , 1996: The anomalous rainfall over the United States during July 1993: Sensitivity to land surface parameterization and soil moisture anomalies. Mon. Wea. Rev., 124, 362383, doi:10.1175/1520-0493(1996)124<0362:TAROTU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Betts, A. K., 2009: Land-surface-atmosphere coupling in observations and models. J. Adv. Model. Earth Syst.,1, doi:10.3894/JAMES.2009.1.4.

  • Caesar, J., Alexander L. , and Vose R. , 2006: Large-scale changes in observed daily maximum and minimum temperatures: Creation and analysis of a new gridded data set. J. Geophys. Res.,111, D05101, doi:10.1029/2005JD006280.

  • Clapp, R. B., and Hornberger G. M. , 1978: Empirical equations for some soil hydraulic properties. Water Resour. Res., 14, 601604, doi:10.1029/WR014i004p00601.

    • Search Google Scholar
    • Export Citation
  • Cruz, F. T., Pitman A. J. , and Wang Y. , 2010: Can the stomatal response to higher atmospheric carbon dioxide explain the unusual temperatures during the 2002 Murray–Darling Basin drought. J. Geophys. Res.,115, D02101, doi:10.1029/2009JD012767.

  • Decker, M., Pitman A. J. , and Evans J. P. , 2013: Groundwater constraints on simulated transpiration variability over southeastern Australian forests. J. Hydrometeor., 14, 543–559, doi:10.1175/JHM-D-12-058.1.

    • Search Google Scholar
    • Export Citation
  • 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, doi:10.1002/qj.828.

    • Search Google Scholar
    • Export Citation
  • de Noblet-Ducoudré, N., and Coauthors, 2012: Determining robust impacts of land-use-induced land cover changes on surface climate over North America and Eurasia: Results from the first set of LUCID experiments. J. Climate, 25, 32613281, doi:10.1175/JCLI-D-11-00338.1.

    • Search Google Scholar
    • Export Citation
  • Douville, H., 2010: Relative contribution of soil moisture and snow mass to seasonal climate predictability: A pilot study. Climate Dyn., 34, 797818, doi:10.1007/s00382-008-0508-1.

    • Search Google Scholar
    • Export Citation
  • Douville, H., and Chauvin F. , 2000: Relevance of soil moisture for seasonal climate predictions: A preliminary study. Climate Dyn., 16, 719736, doi:10.1007/s003820000080.

    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 30773107, doi:10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Evans, J. P., and McCabe M. F. , 2010: Regional climate simulation over Australia’s Murray-Darling basin: A multitemporal assessment. J. Geophys. Res.,115, D14114, doi:10.1029/2010JD013816.

  • Evans, J. P., and Westra S. , 2012: Investigating the mechanisms of diurnal rainfall variability using a regional climate model. J. Climate, 25, 72327247, doi:10.1175/JCLI-D-11-00616.1.

    • Search Google Scholar
    • Export Citation
  • Evans, J. P., Pitman A. J. , and Cruz F. T. , 2011: Coupled atmospheric and land surface dynamics over southeast Australia: A review, analysis and identification of future research priorities. Int. J. Climatol., 31, 17581772, doi:10.1002/joc.2206.

    • Search Google Scholar
    • Export Citation
  • Evans, J. P., Ekström M. , and Ji F. , 2012: Evaluating the performance of a WRF physics ensemble over south-east Australia. Climate Dyn., 39, 12411258, doi:10.1007/s00382-011-1244-5.

    • Search Google Scholar
    • Export Citation
  • Fennessy, M. J., and Shukla J. , 1999: Impact of initial soil wetness on seasonal atmospheric prediction. J. Climate, 12, 31673180, doi:10.1175/1520-0442(1999)012<3167:IOISWO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Haverd, V., and Coauthors, 2012: Multiple observation types reduce uncertainty in Australia’s terrestrial carbon and water cycles. Biogeosci. Discuss., 9, 12 18112 258.

    • Search Google Scholar
    • Export Citation
  • Hong, S. Y., Noh Y. , and Dudhia J. , 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 23182341, doi:10.1175/MWR3199.1.

    • Search Google Scholar
    • Export Citation
  • Jaeger, E. B., and Seneviratne S. I. , 2011: Impact of soil moisture–atmosphere coupling on European climate extremes and trends in a regional climate model. Climate Dyn., 36, 19191939, doi:10.1007/s00382-010-0780-8.

    • Search Google Scholar
    • Export Citation
  • Jones, D., Wang W. , and Fawcett R. , 2009: High-quality spatial climate data-sets for Australia. Aust. Meteor. Mag., 58, 233248.

  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170181, doi:10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., and Fritsch J. M. , 1990: A one-dimensional entraining detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 27842802, doi:10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., and Fritsch J. M. , 1993: Convective parameterization for mesoscale models: The Kain–Fritsch scheme. The Representation of Cumulus Convection in Numerical Models,Meteor. Monogr., No. 46, Amer. Meteor. Soc., 165–170.

  • King, A. D., Alexander L. V. , and Donat M. G. , 2013: The efficacy of using gridded data to examine extreme rainfall characteristics: A case study for Australia. Int. J. Climatol., 33, 2376–2387, doi: 10.1002/joc.3588.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and Coauthors, 2006: GLACE: The Global Land–Atmosphere Coupling Experiment. Part I: Overview. J. Hydrometeor., 7, 590610, doi:10.1175/JHM510.1.

    • 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, 43224335, doi:10.1175/2009JCLI2832.1.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and Coauthors, 2010: 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.

  • 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, 805822, doi:10.1175/2011JHM1365.1.

    • Search Google Scholar
    • Export Citation
  • Kowalczyk, E. A., and Coauthors, 2013: The land surface model component of ACCESS: Description and impact on the simulated surface climatology. Aust. Meteor. Oceanogr. J., 63, 65–82.

    • Search Google Scholar
    • Export Citation
  • Kumar, S. V., and Coauthors, 2006: Land information system: An inter-operable framework for high resolution land surface modeling. Environ. Model. Software, 21, 14021415, doi:10.1016/j.envsoft.2005.07.004.

    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., Taubman S. J. , Brown P. D. , Lacono M. J. , and Clough S. A. , 1997: Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the long-wave. J. Geophys. Res., 102 (D14), 16 66316 682, doi:10.1029/97JD00237.

    • Search Google Scholar
    • Export Citation
  • Peters-Lidard, C. D., and Coauthors, 2007: High-performance earth system modeling with NASA/GSFC’s land information system. Innovations Syst. Software Eng., 3, 157165, doi:10.1007/s11334-007-0028-x.

    • Search Google Scholar
    • Export Citation
  • Pitman, A. J., Avila F. B. , Abramowitz G. , Wang Y. P. , Phipps S. J. , and de Noblet-Ducoudré N. , 2011: Importance of background climate in determining impact of land–cover change on regional climate. Nat. Climate Change, 1, 472475, doi:10.1038/nclimate1294.

    • Search Google Scholar
    • Export Citation
  • Raupach, M. R., Finkele K. , and Zhang L. , 1997: SCAM (Soil-Canopy-Atmosphere Model): Description and comparison with field data. Tech. Rep. 132, CSIRO Centre for Environmental Mechanics, Canberra, ACT, Australia, 81 pp.

  • Reichle, R. H., Koster R. D. , de Lannoy G. J. M. , Forman B. A. , Liu Q. , Mahanama S. P. P. , and Tourè A. , 2011: Assessment and enhancement of MERRA land surface hydrology estimates. J. Climate, 24, 63226338, doi:10.1175/JCLI-D-10-05033.1.

    • Search Google Scholar
    • Export Citation
  • Risbey, J. S., Pook M. J. , McIntosh P. C. , Wheeler M. C. , and Hendon H. H. , 2009: On the remote drivers of rainfall variability in Australia. Mon. Wea. Rev., 137, 32333253, doi:10.1175/2009MWR2861.1.

    • Search Google Scholar
    • Export Citation
  • Santanello, J. A., Peters-Lidard C. D. , and Kumar S. V. , 2011: Diagnosing the sensitivity of local land–atmosphere coupling via the soil moisture–boundary layer interaction. J. Hydrometeor., 12, 766786, doi:10.1175/JHM-D-10-05014.1.

    • Search Google Scholar
    • Export Citation
  • Santanello, J. A., Peters-Lidard C. D. , Kennedy A. , and Kumar S. V. , 2013: Diagnosing the nature of land–atmosphere coupling: A case study of dry/wet extremes in the U.S. southern Great Plains. J. Hydrometeor., 14, 324, doi:10.1175/JHM-D-12-023.1.

    • Search Google Scholar
    • Export Citation
  • Seneviratne, S. I., Corti T. , Davin E. L. , Hirschi M. , Jaeger E. B. , Lehner I. , Orlowsky B. , and Teuling A. J. , 2010: Investigating soil moisture–climate interactions in a changing climate: A review. Earth Sci. Rev., 99, 125161, doi:10.1016/j.earscirev.2010.02.004.

    • 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. [Available online at http://www.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf.]

  • Timbal, B., Power S. , Colman R. , Viviand J. , and Lirola S. , 2002: Does soil moisture influence climate variability and predictability over Australia? J. Climate, 15, 12301238, doi:10.1175/1520-0442(2002)015<1230:DSMICV>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • van den Hurk, B., Doblas-Reyes F. , Balsamo G. , Koster R. D. , Seneviratne S. I. , and Camargo H. Jr., 2012: Soil moisture effects on seasonal temperature and precipitation forecast scores in Europe. Climate Dyn., 38, 349362, doi:10.1007/s00382-010-0956-2.

    • Search Google Scholar
    • Export Citation
  • Wang, Y. P., Kowalczyk E. , Leuning R. , Abramowitz G. , Raupach M. R. , Pak B. , van Gorsel E. , and Luhar A. , 2011: Diagnosing errors in a land surface model (CABLE) in the time and frequency domains. J. Geophys. Res.,116, G01034, doi:10.1029/2010JG001385.

  • Zhang, Q., Wang Y. P. , Pitman A. J. , and Dai Y. J. , 2011: Limitations of nitrogen and phosphorous on the terrestrial carbon uptake in the 20th century. Geophys. Res. Lett.,38, L22701, doi:10.1029/2011GL049244.

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