• Dirkson, A., W. J. Merryfield, and A. H. Monahan, 2019: Calibrated probabilistic forecasts of Arctic sea ice concentration. J. Climate, 32, 12511271, https://doi.org/10.1175/JCLI-D-18-0224.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doblas-Reyes, F. J., R. Hagedorn, T. N. Palmer, and J. J. Morcrette, 2006: Impact of increasing greenhouse gas concentrations in seasonal ensemble forecasts. Geophys. Res. Lett., 33, L07708, https://doi.org/10.1029/2005GL025061.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doblas-Reyes, F. J., J. Garcia-Serrano, F. Lienert, A. P. Biescas, and L. R. L. Rodrigues, 2013: Seasonal climate predictability and forecasting: Status and prospects. Wiley Interdiscip. Rev.: Climate Change, 4, 245268, https://doi.org/10.1002/wcc.217.

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

  • Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman, 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Wea. Rev., 133, 10981118, https://doi.org/10.1175/MWR2904.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gneiting, T., F. Balabdaoui, and A. E. Raftery, 2007: Probabilistic forecasts, calibration and sharpness. J. Roy. Stat. Soc., 69, 243268, https://doi.org/10.1111/j.1467-9868.2007.00587.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hartmann, D. L., and et al. , 2013: Observations: Atmosphere and surface. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 159–254.

  • Hermanson, L., and et al. , 2018: Different types of drifts in two seasonal forecast systems and their dependence on ENSO. Climate Dyn., 51, 14111426, https://doi.org/10.1007/s00382-017-3962-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hersbach, H., 2000: Decomposition of the continuous ranked probability score for ensemble prediction systems. Wea. Forecasting, 15, 559570, https://doi.org/10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hudson, D., and et al. , 2017: ACCESS-S1: The new Bureau of Meteorology multi-week to seasonal prediction system. J. South. Hemisphere Earth Syst. Sci., 67, 132159, https://doi.org/10.22499/3.6703.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hudson, D., Alves, O., Shi, L., and G. Young, 2018: Improved skill for regional climate in the ACCESS-Based POAMA model. Final Rep. VG13092, Horticulture Innovation, NSW Australia, 18 pp., https://ausveg.com.au/app/uploads/technical-insights/VG13092.pdf.

  • Innes, A., F. Baier, A. Benedetti, I. Bouarar, S. Chabrillat, and H. Clark, 2013: The MACC reanalysis: An 8 yr data set of atmospheric composition. Atmos. Chem. Phys., 13, 40734109, https://doi.org/10.5194/acp-13-4073-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jia, G., and et al. , 2019: Land–climate interactions. Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems, P. Bernier, J. C. Espinoza, and S. Semenov, Eds., Cambridge University Press, 131–247.

  • Jia, X. J., and H. Lin, 2013: The possible reasons for the misrepresented long-term climate trends in the seasonal forecasts of HFP2. Mon. Wea. Rev., 141, 31543169, https://doi.org/10.1175/MWR-D-12-00302.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, S. J., and et al. , 2019: SEAS5: The new ECMWF seasonal forecast system. Geosci. Model Dev., 12, 10871117, https://doi.org/10.5194/gmd-12-1087-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, D. A., W. Wang, and R. Fawcett, 2009: High-quality spatial climate data-sets for Australia. Aust. Meteor. Oceanogr. J., 58, 233248, https://doi.org/10.22499/2.5804.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jung, M., and et al. , 2010: Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature, 467, 951954, https://doi.org/10.1038/nature09396.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kharin, V. V., G. J. Boer, W. J. Merryfield, J. F. Scinocca, and W. S. Lee, 2012: Statistical adjustment of decadal predictions in a changing climate. Geophys. Res. Lett., 39, L19705, https://doi.org/10.1029/2012GL052647.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kirtman, B., and et al. , 2013: Near-term climate change: Projections and predictability. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 953–1028.

  • Krakauer, N. Y., 2019: Temperature trends and prediction skill in NMME seasonal forecasts. Climate Dyn., 53, 72017213, https://doi.org/10.1007/s00382-017-3657-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kushnir, Y., and et al. , 2019: Towards operational predictions of the near-term climate. Nat. Climate Change, 9, 94101, https://doi.org/10.1038/s41558-018-0359-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lausier, A. M., and S. Jain, 2018: Overlooked trends in observed global annual precipitation reveal underestimated risks. Sci. Rep., 8, 16746, https://doi.org/10.1038/s41598-018-34993-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matheson, J. E., and R. L. Winkler, 1976: Scoring rules for continuous probability distributions. Manage. Sci., 22, 10871096, https://doi.org/10.1287/mnsc.22.10.1087.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peng, S., Y. Ding, Z. Wen, Y. Chen, Y. Cao, and J. Ren, 2017: Spatiotemporal change and trend analysis of potential evapotranspiration over the Loess Plateau of China during 2011–2100. Agric. For. Meteor., 233, 183194, https://doi.org/10.1016/j.agrformet.2016.11.129.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Polade, S. D., A. Gershunov, D. R. Cayan, M. D. Dettinger, and D. W. Pierce, 2017: Precipitation in a warming world: Assessing projected hydro-climate changes in California and other Mediterranean climate regions. Sci. Rep., 7, 10783, https://doi.org/10.1038/s41598-017-11285-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Renard, B., D. Kavetski, G. Kuczera, M. Thyer, and S. W. Franks, 2010: Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors. Water Resour. Res., 46, W05521, https://doi.org/10.1029/2009WR008328.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Robertson, A. W., A. Kumar, M. Peña, and F. Vitart, 2015: Improving and promoting subseasonal to seasonal prediction. Bull. Amer. Meteor. Soc., 96, ES49ES53, https://doi.org/10.1175/BAMS-D-14-00139.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sansom, P. G., C. A. Ferro, D. B. Stephenson, L. Goddard, and S. J. Mason, 2016: Best practices for postprocessing ensemble climate forecasts. Part I: Selecting appropriate recalibration methods. J. Climate, 29, 72477264, https://doi.org/10.1175/JCLI-D-15-0868.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schepen, A., Q. J. Wang, and Y. Everingham, 2016: Calibration, bridging, and merging to improve GCM seasonal temperature forecasts in Australia. Mon. Wea. Rev., 144, 24212441, https://doi.org/10.1175/MWR-D-15-0384.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shao, Y., Q. J. Wang, A. Schepen, and D. Ryu, 2021: Embedding trend into seasonal temperature forecasts through statistical calibration of GCM outputs. Int. J. Climatol., 41, E1553E1565, https://doi.org/10.1002/joc.6788.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Strazzo, S., D. C. Collins, A. Schepen, Q. J. Wang, E. Becker, and L. W. Jia, 2019: Application of a hybrid statistical-dynamical system to seasonal prediction of North American temperature and precipitation. Mon. Wea. Rev., 147, 607625, https://doi.org/10.1175/MWR-D-18-0156.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Troccoli, A., 2010: Seasonal climate forecasting. Meteor. Appl., 17, 251268, https://doi.org/10.1002/met.184.

  • Troccoli, A., 2018: Weather & Climate Services for the Energy Industry. Springer, 224 pp.

    • Crossref
    • Export Citation
  • Troccoli, A., M. Harrison, D. L. Anderson, and S. J. Mason, Eds., 2008: Introduction. Seasonal Climate: Forecasting and Managing Risk, NATO Science Series, Vol. 82, Springer Science and Business Media, 3–11.

    • Crossref
    • Export Citation
  • Wang, Q. J., and D. E. Robertson, 2011: Multisite probabilistic forecasting of seasonal flows for streams with zero value occurrences. Water Resour. Res., 47, W02546, https://doi.org/10.1029/2010WR009333.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Q. J., D. E. Robertson, and F. H. S. Chiew, 2009: A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites. Water Resour. Res., 45, W05407, https://doi.org/10.1029/2008WR007355.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Q. J., Y. W. Shao, Y. Song, A. Schepen, D. E. Robertson, D. Ryu, and F. Pappenberger, 2019: An evaluation of ECMWF SEAS5 seasonal climate forecasts for Australia using a new forecast calibration algorithm. Environ. Modell. Software, 122, 104550, https://doi.org/10.1016/j.envsoft.2019.104550.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weisheimer, A., F. J. Doblas-Reyes, T. Jung, and T. N. Palmer, 2011: On the predictability of the extreme summer 2003 over Europe. Geophys. Res. Lett., 38, L05704, https://doi.org/10.1029/2010GL046455.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yeo, I. K., and R. A. Johnson, 2000: A new family of power transformations to improve normality or symmetry. Biometrika, 87, 954959, https://doi.org/10.1093/biomet/87.4.954.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, T. T. G., J. C. Bennett, Q. J. Wang, A. Schepen, A. W. Wood, D. E. Robertson, and M. H. Ramos, 2017: How suitable is quantile mapping for postprocessing GCM precipitation forecasts? J. Climate, 30, 31853196, https://doi.org/10.1175/JCLI-D-16-0652.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Going with the Trend: Forecasting Seasonal Climate Conditions under Climate Change

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  • 1 a Department of Infrastructure Engineering, University of Melbourne, Melbourne, Queensland, Australia
  • | 2 b CSIRO Land and Water, Brisbane, Queensland, Australia
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Abstract

For managing climate variability and adapting to climate change, seasonal forecasts are widely produced to inform decision-making. However, seasonal forecasts from global climate models are found to poorly reproduce temperature trends in observations. Furthermore, this problem is not addressed by existing forecast postprocessing methods that are needed to remedy biases and uncertainties in model forecasts. The inability of the forecasts to reproduce the trends severely undermines user confidence in the forecasts. In our previous work, we proposed a new statistical postprocessing model that counteracted departures in trends of model forecasts from observations. Here, we further extend this trend-aware forecast postprocessing methodology to carefully treat the trend uncertainty associated with the sampling variability due to limited data records. This new methodology is validated on forecasting seasonal averages of daily maximum and minimum temperatures for Australia based on the SEAS5 climate model of the European Centre for Medium-Range Weather Forecasts. The resulting postprocessed forecasts are shown to have proper trends embedded, leading to greater accuracy in regions with significant trends. The application of this new forecast postprocessing is expected to boost user confidence in seasonal climate forecasts.

© 2021 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: Yawen Shao, yawens@student.unimelb.edu.au

Abstract

For managing climate variability and adapting to climate change, seasonal forecasts are widely produced to inform decision-making. However, seasonal forecasts from global climate models are found to poorly reproduce temperature trends in observations. Furthermore, this problem is not addressed by existing forecast postprocessing methods that are needed to remedy biases and uncertainties in model forecasts. The inability of the forecasts to reproduce the trends severely undermines user confidence in the forecasts. In our previous work, we proposed a new statistical postprocessing model that counteracted departures in trends of model forecasts from observations. Here, we further extend this trend-aware forecast postprocessing methodology to carefully treat the trend uncertainty associated with the sampling variability due to limited data records. This new methodology is validated on forecasting seasonal averages of daily maximum and minimum temperatures for Australia based on the SEAS5 climate model of the European Centre for Medium-Range Weather Forecasts. The resulting postprocessed forecasts are shown to have proper trends embedded, leading to greater accuracy in regions with significant trends. The application of this new forecast postprocessing is expected to boost user confidence in seasonal climate forecasts.

© 2021 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: Yawen Shao, yawens@student.unimelb.edu.au

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