• An-Vo, D., S. Mushtaq, K. Reardon-Smith, L. Kouadio, S. Attard, D. Cobon, and R. Stone, 2019: Value of seasonal forecasting for sugarcane farm irrigation planning. Eur. J. Agron., 104, 3748, https://doi.org/10.1016/j.eja.2019.01.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnston, A. G., S. Li, S. J. Mason, D. G. DeWitt, L. Goddard, and X. Gong, 2010: Verification of the first 11 years of IRI’s seasonal climate forecasts. J. Appl. Meteor. Climatol., 49, 493520, https://doi.org/10.1175/2009JAMC2325.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bhend, J., and P. Whetton, 2015: Evaluation of simulated recent climate change in Australia. Aust. Meteor. Oceanogr. J., 65, 418, https://doi.org/10.22499/2.6501.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brocca, L., and Coauthors, 2016: Rainfall estimation by inverting SMOS soil moisture estimates: A comparison of different methods over Australia. J. Geophys. Res. Atmos., 121, 12 06212 079, https://doi.org/10.1002/2016JD025382.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cai, M., C.-S. Shin, H. van den Dool, W. Wang, S. Saha, and A. Kumar, 2009: The role of long-term trend in seasonal predictions: Implication of global warming in the NCEP CFS. Wea. Forecasting, 24, 965973, https://doi.org/10.1175/2009WAF2222231.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • CSIRO and BoM, 2020: State of the Climate 2020. 24 pp., http://www.bom.gov.au/state-of-the-climate/documents/State-of-the-Climate-2020.pdf.

  • 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
  • Gelman, A., J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari, D. B. Rubin, 2014: Bayesian Data Analysis. 3rd ed. CRC Press 202 pp.

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

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

  • Huang, B., C.-S. Shin, and A. Kumar, 2019: Predictive skill and predictable patterns of the U.S. seasonal precipitation in CFSv2 reforecasts of 60 years (1958–2017). J. Climate, 32, 86038637, https://doi.org/10.1175/JCLI-D-19-0230.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Inness, A., and Couthors, 2013: The MACC reanalysis: An 8 yr data set of atmospheric composition. Chem. Phys., 13, 40734109, https://doi.org/10.5194/acp-13-4073-2013.

    • Search Google Scholar
    • Export Citation
  • Johnson, S. J., and Coauthors, 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
  • Kendall, M. G., 1975. Rank Correlation Methods. 4th ed. Charles Griffin, 202 pp.

  • Kirtman, B. P., and Coauthors, 2014: The North American multimodel ensemble: Phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bull. Amer. Meteor. Soc., 95, 585601, https://doi.org/10.1175/BAMS-D-12-00050.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krakauer, N. Y., 2017: 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
  • Krikken, F., M. Schmeits, W. Vlot, V. Guemas, and W. Hazeleger, 2016: Skill improvement of dynamical seasonal Arctic sea ice forecasts. Geophys. Res. Lett., 43, 51245132, https://doi.org/10.1002/2016GL068462.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumar, S., V. Merwade, J. L. Kinter III, and D. Niyogi, 2013: Evaluation of temperature and precipitation trends and long-term persistence in CMIP5 twentieth-century climate simulations. J. Climate, 26, 41684185, https://doi.org/10.1175/JCLI-D-12-00259.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Livezey, R. E., and M. M. Timofeyeva, 2008: The first decade of long-lead US seasonal forecasts - Insights from a skill analysis. Bull. Amer. Meteor. Soc., 89, 843854, https://doi.org/10.1175/2008BAMS2488.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mann, H. B., 1945: Nonparametric tests against trend. Econometrica, 13, 245259, https://doi.org/10.2307/1907187.

  • 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
  • Pechlivanidis, I., L. Crochemore, J. Rosberg, and T. Bosshard, 2020: What are the key drivers controlling the quality of seasonal streamflow forecasts? Water Resour. Res., 56, e2019WR026987, https://doi.org/10.1029/2019WR026987.

    • Crossref
    • Export Citation
  • Peel, M. C., B. L. Finlayson, and T. A. McMahon, 2007: Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci., 11, 16331644, https://doi.org/10.5194/hess-11-1633-2007.

    • 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, https://doi.org/10.1029/2009WR008328.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Risbey, J. S., and Coauthors, 2021: Standard assessments of climate forecast skill can be misleading. Nat. Commun., 12, 4346, https://doi.org/10.1038/s41467-021-23771-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rowell, D. P., 2012: Sources of uncertainty in future changes in local precipitation. Climate Dyn., 39, 19291950, https://doi.org/10.1007/s00382-011-1210-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2014: The NCEP Climate Forecast System version 2. J. Climate, 27, 21852208, https://doi.org/10.1175/JCLI-D-12-00823.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sayemuzzaman, M., and M. K. Jha, 2014: Seasonal and annual precipitation time series trend analysis in North Carolina, United States. Atmos. Res., 137, 183194, https://doi.org/10.1016/j.atmosres.2013.10.012.

    • 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
  • Schepen, A., Y. Everingham, and Q. J. Wang, 2020: On the joint calibration of multivariate seasonal climate forecasts from GCMs. Mon. Wea. Rev., 148, 437456, https://doi.org/10.1175/MWR-D-19-0046.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sen, P. K., 1968: Estimates of the regression coefficient based on Kendall’s tau. J. Amer. Stat. Assoc., 63, 13791389, https://doi.org/10.1080/01621459.1968.10480934.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shao, Y., Q. J. Wang, A. Schepen, and D. Ryu, 2021a: 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
  • Shao, Y., Q. J. Wang, A. Schepen, and D. Ryu, 2021b: Going with the trend: forecasting seasonal climate conditions under climate change. Mon. Wea. Rev., 149, 25132522, https://doi.org/10.1175/MWR-D-20-0318.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shin, C.-S., and B. Huang, 2019: A spurious warming trend in the NMME equatorial Pacific SST hindcasts. Climate Dyn., 53, 72877303, https://doi.org/10.1007/s00382-017-3777-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stockdale, T. N., 2021: SEAS5 user guide. ECMWF, 44 pp., https://doi.org/10.21957/2y67999y.

    • Crossref
    • Export Citation
  • Theil, H., 1992: A rank-invariant method of linear and polynomial regression analysis. Henri Theil’s Contributions to Economics and Econometrics, Springer, 345–381.

    • Crossref
    • Export Citation
  • Wang, F., W. Shao, H. Yu, G. Kan, X. He, D. Zhang, M. Ren, and G. Wang, 2020: Re-evaluation of the power of the Mann-Kendall test for detecting monotonic trends in hydrometeorological time series. Front. Earth Sci., 8, 14, https://doi.org/10.3389/feart.2020.00014.

    • Crossref
    • Search Google Scholar
    • 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., D. L. Shrestha, D. E. Robertson, and P. Pokhrel, 2012: A log-sinh transformation for data normalization and variance stabilization. Water Resour. Res., 48, W05514, https://doi.org/10.1029/2011WR010973.

    • 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
  • Wasko, C., Y. Shao, E. Vogel, L. Wilson, Q. Wang, A. Frost, and C. Donnelly, 2021: Understanding trends in hydrologic extremes across Australia. J. Hydrol., 593, 125877, https://doi.org/10.1016/j.jhydrol.2020.125877.

    • Crossref
    • Export Citation
  • Wilks, D. S., 2016: “The stippling shows statistically significant grid points”: How research results are routinely overstated and overinterpreted, and what to do about it. Bull. Amer. Meteor. Soc., 97, 22632273, https://doi.org/10.1175/BAMS-D-15-00267.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 321 155 12
Full Text Views 86 43 1
PDF Downloads 97 52 1

Improved Trend-Aware Postprocessing of GCM Seasonal Precipitation Forecasts

Yawen ShaoaDepartment of Infrastructure Engineering, University of Melbourne, Melbourne, Australia

Search for other papers by Yawen Shao in
Current site
Google Scholar
PubMed
Close
,
Quan J. WangaDepartment of Infrastructure Engineering, University of Melbourne, Melbourne, Australia

Search for other papers by Quan J. Wang in
Current site
Google Scholar
PubMed
Close
,
Andrew SchepenbCSIRO Land and Water, Brisbane, Australia

Search for other papers by Andrew Schepen in
Current site
Google Scholar
PubMed
Close
,
Dongryeol RyuaDepartment of Infrastructure Engineering, University of Melbourne, Melbourne, Australia

Search for other papers by Dongryeol Ryu in
Current site
Google Scholar
PubMed
Close
, and
Florian PappenbergercEuropean Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

Search for other papers by Florian Pappenberger in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Climate trends have been observed over the recent decades in many parts of the world, but current global climate models (GCMs) for seasonal climate forecasting often fail to capture these trends. As a result, model forecasts may be biased above or below the trendline. In our previous research, we developed a trend-aware forecast postprocessing method to overcome this problem. The method was demonstrated to be effective for embedding observed trends into seasonal temperature forecasts. In this study, we further develop the method for postprocessing GCM seasonal precipitation forecasts. We introduce new formulation and evaluation features to cater for special characteristics of precipitation amounts, such as having a zero lower bound and highly positive skewness. We apply the improved method to calibrate ECMWF SEAS5 forecasts of seasonal precipitation for Australia. Our evaluation shows that the calibrated forecasts reproduce observed trends over the hindcast period of 36 years. In some regions where observed trends are statistically significant, forecast skill is greatly improved by embedding trends into the forecasts. In most regions, the calibrated forecasts outperform the raw forecasts in terms of bias, skill, and reliability. Wider applications of the new trend-aware postprocessing method are expected to boost user confidence in seasonal precipitation forecasts.

© 2022 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

Climate trends have been observed over the recent decades in many parts of the world, but current global climate models (GCMs) for seasonal climate forecasting often fail to capture these trends. As a result, model forecasts may be biased above or below the trendline. In our previous research, we developed a trend-aware forecast postprocessing method to overcome this problem. The method was demonstrated to be effective for embedding observed trends into seasonal temperature forecasts. In this study, we further develop the method for postprocessing GCM seasonal precipitation forecasts. We introduce new formulation and evaluation features to cater for special characteristics of precipitation amounts, such as having a zero lower bound and highly positive skewness. We apply the improved method to calibrate ECMWF SEAS5 forecasts of seasonal precipitation for Australia. Our evaluation shows that the calibrated forecasts reproduce observed trends over the hindcast period of 36 years. In some regions where observed trends are statistically significant, forecast skill is greatly improved by embedding trends into the forecasts. In most regions, the calibrated forecasts outperform the raw forecasts in terms of bias, skill, and reliability. Wider applications of the new trend-aware postprocessing method are expected to boost user confidence in seasonal precipitation forecasts.

© 2022 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

Supplementary Materials

    • Supplemental Materials (PDF 769 KB)
Save