• Bentzien, S., and P. Friederichs, 2012: Generating and calibrating probabilistic quantitative precipitation forecasts from the high-resolution NWP model COSMO-DE. Wea. Forecasting, 27, 9881002, https://doi.org/10.1175/WAF-D-11-00101.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boer, G., 2009: Climate trends in a seasonal forecasting system. Atmos.–Ocean, 47, 123138, https://doi.org/10.3137/AO1002.2009.

  • Brier, G. W., 1950: Verification of forecasts expressed in terms of probability. Mon. Wea. Rev., 78, 13, https://doi.org/10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buehner, M., A. Caya, T. Carrieres, L. Pogson, and M. Lajoie, 2013a: Overview of sea ice data assimilation activities at Environment Canada. Proc. ECMWF-WWRP/THORPEX Polar Prediction Workshop, Reading, United Kingdom, ECMWF, 24–27.

  • Buehner, M., A. Caya, L. Pogson, T. Carrieres, and P. Pestieau, 2013b: A new Environment Canada regional ice analysis system. Atmos.–Ocean, 51, 1834, https://doi.org/10.1080/07055900.2012.747171.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buehner, M., A. Caya, T. Carrieres, and L. Pogson, 2016: Assimilation of SSMIS and ASCAT data and the replacement of highly uncertain estimates in the environment Canada regional ice prediction system. Quart. J. Roy. Meteor. Soc., 142, 562573, https://doi.org/10.1002/qj.2408.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buizza, R., 2008: The value of probabilistic prediction. Atmos. Sci. Lett., 9, 3642, https://doi.org/10.1002/asl.170.

  • Cannon, A. J., 2008: Probabilistic multisite precipitation downscaling by an expanded Bernoulli–Gamma density network. J. Hydrometeor., 9, 12841300, https://doi.org/10.1175/2008JHM960.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cohen, A. C., Jr., 1950: Estimating the mean and variance of normal populations from singly truncated and doubly truncated samples. Ann. Math. Stat., 21, 557569, https://doi.org/10.1214/aoms/1177729751.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Comiso, J. C., 2003: Warming trends in the Arctic from clear sky satellite observations. J. Climate, 16, 34983510, https://doi.org/10.1175/1520-0442(2003)016<3498:WTITAF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Comiso, J. C., C. L. Parkinson, R. Gersten, and L. Stock, 2008: Accelerated decline in the Arctic sea ice cover. Geophys. Res. Lett., 35, L01703, https://doi.org/10.1029/2007GL031972.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Director, H. M., A. E. Raftery, and C. M. Bitz, 2017: Improved sea ice forecasting through spatiotemporal bias correction. J. Climate, 30, 94939510, https://doi.org/10.1175/JCLI-D-17-0185.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Director, H. M., A. E. Raftery, and C. M. Bitz, 2019: Probabilistic forecasting of the Arctic sea ice edge with contour modeling. arXiv preprint arXiv:1908.09377.

    • Search Google Scholar
    • Export Citation
  • Dirkson, A., W. J. Merryfield, and A. Monahan, 2017: Impacts of sea ice thickness initialization on seasonal arctic sea ice predictions. J. Climate, 30, 10011017, https://doi.org/10.1175/JCLI-D-16-0437.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dirkson, A., B. Denis, and W. Merryfield, 2019a: A multimodel approach for improving seasonal probabilistic forecasts of regional Arctic sea ice. Geophys. Res. Lett., 46, 10 84410 853, https://doi.org/10.1029/2019GL083831.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dirkson, A., W. J. Merryfield, and A. H. Monahan, 2019b: 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, and T. Palmer, 2005: The rationale behind the success of multi-model ensembles in seasonal forecasting––II. Calibration and combination. Tellus, 57A, 234252, https://doi.org/10.1111/j.1600-0870.2005.00104.x.

    • Search Google Scholar
    • Export Citation
  • Eicken, H., 2013: Arctic sea ice needs better forecasts. Nature, 497, 431433, https://doi.org/10.1038/497431a.

  • Flato, G. M., and W. D. Hibler III, 1992: Modeling pack ice as a cavitating fluid. J. Phys. Oceanogr., 22, 626651, https://doi.org/10.1175/1520-0485(1992)022<0626:MPIAAC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fučkar, N. S., D. Volpi, V. Guemas, and F. J. Doblas-Reyes, 2014: A posteriori adjustment of near-term climate predictions: Accounting for the drift dependence on the initial conditions. Geophys. Res. Lett., 41, 52005207, https://doi.org/10.1002/2014GL060815.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gneiting, T., and A. E. Raftery, 2007: Strictly proper scoring rules, prediction, and estimation. J. Amer. Stat. Assoc., 102, 359378, https://doi.org/10.1198/016214506000001437.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gneiting, T., A. E. Raftery, A. H. Westveld III, 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., 69B, 243268, https://doi.org/10.1111/j.1467-9868.2007.00587.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goosse, H., O. Arzel, C. M. Bitz, A. de Montety, and M. Vancoppenolle, 2009: Increased variability of the Arctic summer ice extent in a warmer climate. Geophys. Res. Lett., 36, L23702, https://doi.org/10.1029/2009GL040546.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harrell, F. E., and C. Davis, 1982: A new distribution-free quantile estimator. Biometrika, 69, 635640, https://doi.org/10.1093/biomet/69.3.635.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • IPCC, 2019: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. Cambridge University Press, in press, https://www.ipcc.ch/srocc/.

    • Search Google Scholar
    • Export Citation
  • Johnson, N. L., S. Kotz, and N. Balakrishnan, 1994: Continuous Univariate Distributions. Vol. 1, Wiley, 158 pp.

  • Jolliffe, I. T., and D. B. Stephenson, 2012: Forecast Verification: A Practitioner’s Guide in Atmospheric Science. John Wiley & Sons, 372 pp.

    • Search Google Scholar
    • Export Citation
  • Jones, E., T. Oliphant, and P. Peterson, 2001: SciPy: Open source scientific tools for Python. Accessed 28 January 2020, https://www.scipy.org/about.html.

  • Kharin, V., G. Boer, W. Merryfield, J. 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
  • Kharin, V., W. Merryfield, G. Boer, and W.-S. Lee, 2017: A postprocessing method for seasonal forecasts using temporally and spatially smoothed statistics. Mon. Wea. Rev., 145, 35453561, https://doi.org/10.1175/MWR-D-16-0337.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kharin, V., Q. Teng, F. W. Zwiers, G. J. Boer, J. Derome, and J. S. Fontecilla, 2009: Skill assessment of seasonal hindcasts from the Canadian historical forecast project. Atmos.–Ocean, 47, 204223, https://doi.org/10.3137/AO1101.2009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kraft, D., 1988: A software package for sequential quadratic programming. Tech. Rep. DFVLR-FB 88-28, DLR German Aerospace Center–Institute for Flight Mechanics, Koln, Germany, 33 pp.

  • 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
  • Krzysztofowicz, R., 1999: Bayesian theory of probabilistic forecasting via deterministic hydrologic model. Water Resour. Res., 35, 27392750, https://doi.org/10.1029/1999WR900099.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kwok, R., G. Cunningham, M. Wensnahan, I. Rigor, H. Zwally, and D. Yi, 2009: Thinning and volume loss of the Arctic Ocean sea ice cover: 2003–2008. J. Geophys. Res., 114, C07005, https://doi.org/10.1029/2009JC005312.

    • Search Google Scholar
    • Export Citation
  • Markus, T., J. C. Stroeve, and J. Miller, 2009: Recent changes in Arctic sea ice melt onset, freezeup, and melt season length. J. Geophys. Res., 114, C12024, https://doi.org/10.1029/2009JC005436.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maslanik, J., C. Fowler, J. Stroeve, S. Drobot, J. Zwally, D. Yi, and W. Emery, 2007: A younger, thinner Arctic ice cover: Increased potential for rapid, extensive sea-ice loss. Geophys. Res. Lett., 34, L24501, https://doi.org/10.1029/2007GL032043.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maslanik, J., J. Stroeve, C. Fowler, and W. Emery, 2011: Distribution and trends in Arctic sea ice age through spring 2011. Geophys. Res. Lett., 38, L13502, https://doi.org/10.1029/2011GL047735.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Merryfield, W., B. Denis, J. Fontecilla, W. Lee, V. Kharin, J. Hodgson, and B. Archambault, 2011: The Canadian Seasonal to Interannual Prediction System (CanSIPS). Environment and Climate Change Canada, 51 pp., https://collaboration.cmc.ec.gc.ca/cmc/cmoi/product_guide/docs/lib/op_systems/doc_opchanges/technote_cansips_20111124_e.pdf.

  • Merryfield, W., and et al. , 2013: The Canadian Seasonal to Interannual Prediction System. Part I: Models and initialization. Mon. Wea. Rev., 141, 29102945, https://doi.org/10.1175/MWR-D-12-00216.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Messner, J. W., G. J. Mayr, D. S. Wilks, and A. Zeileis, 2014a: Extending extended logistic regression: Extended versus separate versus ordered versus censored. Mon. Wea. Rev., 142, 30033014, https://doi.org/10.1175/MWR-D-13-00355.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Messner, J. W., G. J. Mayr, A. Zeileis, and D. S. Wilks, 2014b: Heteroscedastic extended logistic regression for postprocessing of ensemble guidance. Mon. Wea. Rev., 142, 448456, https://doi.org/10.1175/MWR-D-13-00271.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murphy, A. H., 1973: A new vector partition of the probability score. J. Appl. Meteor., 12, 595600, https://doi.org/10.1175/1520-0450(1973)012<0595:ANVPOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ospina, R., and S. L. Ferrari, 2010: Inflated beta distributions. Stat. Hefte, 51, 111.

  • Parkinson, C. L., 2014: Spatially mapped reductions in the length of the Arctic sea ice season. Geophys. Res. Lett., 41, 43164322, https://doi.org/10.1002/2014GL060434.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peng, G., W. Meier, D. Scott, and M. Savoie, 2013: A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring. Earth Syst. Sci. Data, 5, 311318, https://doi.org/10.5194/essd-5-311-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Polyakov, I. V., J. E. Walsh, and R. Kwok, 2012: Recent changes of Arctic multiyear sea ice coverage and the likely causes. Bull. Amer. Meteor. Soc., 93, 145151, https://doi.org/10.1175/BAMS-D-11-00070.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raftery, A. E., T. Gneiting, F. Balabdaoui, and M. Polakowski, 2005: Using Bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev., 133, 11551174, https://doi.org/10.1175/MWR2906.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Richardson, D. S., 2000: Skill and relative economic value of the ECMWF ensemble prediction system. Quart. J. Roy. Meteor. Soc., 126, 649667, https://doi.org/10.1002/qj.49712656313.

    • 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
  • Scheuerer, M., 2014: Probabilistic quantitative precipitation forecasting using ensemble model output statistics. Quart. J. Roy. Meteor. Soc., 140, 10861096, https://doi.org/10.1002/qj.2183.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scheuerer, M., and T. M. Hamill, 2015: Statistical postprocessing of ensemble precipitation forecasts by fitting censored, shifted gamma distributions. Mon. Wea. Rev., 143, 45784596, https://doi.org/10.1175/MWR-D-15-0061.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., M. M. Holland, and J. Stroeve, 2007: Perspectives on the Arctic’s shrinking sea-ice cover. Science, 315, 15331536, https://doi.org/10.1126/science.1139426.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sigmond, M., M. Reader, G. Flato, W. Merryfield, and A. Tivy, 2016: Skillful seasonal forecasts of Arctic sea ice retreat and advance dates in a dynamical forecast system. Geophys. Res. Lett., 43, 12 45712 465, https://doi.org/10.1002/2016GL071396.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stammerjohn, S., R. Massom, D. Rind, and D. Martinson, 2012: Regions of rapid sea ice change: An inter-hemispheric seasonal comparison. Geophys. Res. Lett., 39, L06501, https://doi.org/10.1029/2012GL050874.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephenson, S. R., and R. Pincus, 2018: Challenges of sea-ice prediction for Arctic marine policy and planning. J. Borderl. Stud., 33, 255272, https://doi.org/10.1080/08865655.2017.1294494.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stroeve, J., T. Markus, L. Boisvert, J. Miller, and A. Barrett, 2014: Changes in Arctic melt season and implications for sea ice loss. Geophys. Res. Lett., 41, 12161225, https://doi.org/10.1002/2013GL058951.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stroeve, J. C., E. Blanchard-Wrigglesworth, V. Guemas, S. Howell, F. Massonnet, and S. Tietsche, 2015: Improving predictions of Arctic sea ice extent. Eos, Trans. Amer. Geophys. Union, 96, 11, https://doi.org/10.1029/2015EO031431.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stroeve, J. C., V. Kattsov, A. Barrett, M. Serreze, T. Pavlova, M. Holland, and W. N. Meier, 2012: Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations. Geophys. Res. Lett., 39, L16502, https://doi.org/10.1029/2012GL052676.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stroeve, J. C., A. D. Crawford, and S. Stammerjohn, 2016: Using timing of ice retreat to predict timing of fall freeze-up in the Arctic. Geophys. Res. Lett., 43, 63326340, https://doi.org/10.1002/2016GL069314.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thorarinsdottir, T. L., and T. Gneiting, 2010: Probabilistic forecasts of wind speed: Ensemble model output statistics by using heteroscedastic censored regression. J. Roy. Stat. Soc., 173A, 371388, https://doi.org/10.1111/j.1467-985X.2009.00616.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Titchner, H. A., and N. A. Rayner, 2014: The Met Office Hadley Centre Sea Ice and Sea surface temperature data set, version 2:1. Sea ice concentrations. J. Geophys. Res. Atmos., 119, 28642889, https://doi.org/10.1002/2013JD020316.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tivy, A., S. E. Howell, B. Alt, S. McCourt, R. Chagnon, G. Crocker, T. Carrieres, and J. J. Yackel, 2011: Trends and variability in summer sea ice cover in the Canadian Arctic based on the Canadian Ice Service Digital Archive, 1960–2008 and 1968–2008. J. Geophys. Res., 116, C03007, https://doi.org/10.1029/2009JC005855.

    • Search Google Scholar
    • Export Citation
  • Weisheimer, A., and T. Palmer, 2014: On the reliability of seasonal climate forecasts. J. Roy. Soc. Interface, 11, 20131162, https://doi.org/10.1098/rsif.2013.1162.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2009: Extending logistic regression to provide full-probability-distribution MOS forecasts. Meteor. Appl., 16, 361368, https://doi.org/10.1002/met.134.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. International Geophysics Series, Vol. 100, Academic Press, 704 pp.

  • WMO, 2017: WMO guidelines on the calculation of climate normals. World Meteorological Organization Switzerland, WMO-1203, 29 pp.

  • Yuan, X., and E. F. Wood, 2013: Multimodel seasonal forecasting of global drought onset. Geophys. Res. Lett., 40, 49004905, https://doi.org/10.1002/grl.50949.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, T., J. C. Bennett, Q. 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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Development and Calibration of Seasonal Probabilistic Forecasts of Ice-Free Dates and Freeze-Up Dates

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  • 1 Département des sciences de la Terre et de l’atmosphère, Université du Québec à Montréal, Montreal, Quebec, Canada
  • | 2 Environment and Climate Change Canada, Meteorological Services of Canada, Montreal, Quebec, Canada
  • | 3 Environment and Climate Change Canada, Canadian Center for Climate Modeling and Analysis, Victoria, British Columbia, Canada
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Abstract

Dynamical forecasting systems are being used to skillfully predict deterministic ice-free and freeze-up date events in the Arctic. This paper extends such forecasts to a probabilistic framework and tests two calibration models to correct systematic biases and improve the statistical reliability of the event dates: trend-adjusted quantile mapping (TAQM) and nonhomogeneous censored Gaussian regression (NCGR). TAQM is a probability distribution mapping method that corrects the forecast for climatological biases, whereas NCGR relates the calibrated parametric forecast distribution to the raw ensemble forecast through a regression model framework. For NCGR, the observed event trend and ensemble-mean event date are used to predict the central tendency of the predictive distribution. For modeling forecast uncertainty, we find that the ensemble-mean event date, which is related to forecast lead time, performs better than the ensemble variance itself. Using a multidecadal hindcast record from the Canadian Seasonal to Interannual Prediction System (CanSIPS), TAQM and NCGR are applied to produce categorical forecasts quantifying the probabilities for early, normal, and late ice retreat and advance. While TAQM performs better than adjusting the raw forecast for mean and linear trend bias, NCGR is shown to outperform TAQM in terms of reliability, skill, and an improved tendency for forecast probabilities to be no worse than climatology. Testing various cross-validation setups, we find that NCGR remains useful when shorter hindcast records (~20 years) are available. By applying NCGR to operational forecasts, stakeholders can be more confident in using seasonal forecasts of sea ice event timing for planning purposes.

SIGNIFICANCE STATEMENT

As Earth warms, the Arctic is shifting toward a longer open water season. With maritime access consequently increasing, stakeholders are valuing trustworthy information on the timing of transitional sea ice cover provided by seasonal forecasting models. In this study we advance seasonal predictions of the timing of local ice retreat and advance by extending these predictions to include critical information on forecast uncertainty. To do this, we tailor the established “ensemble model output statistics” calibration framework to sea ice retreat and advance dates, and construct probabilistic forecasts of early, normal, and late sea ice timing. Evaluating these predictions over a historical period indicates that stakeholders can place trust in forecast probabilities of sea ice timing for planning purposes.

© 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: Arlan Dirkson, arlan.dirkson@gmail.com

Abstract

Dynamical forecasting systems are being used to skillfully predict deterministic ice-free and freeze-up date events in the Arctic. This paper extends such forecasts to a probabilistic framework and tests two calibration models to correct systematic biases and improve the statistical reliability of the event dates: trend-adjusted quantile mapping (TAQM) and nonhomogeneous censored Gaussian regression (NCGR). TAQM is a probability distribution mapping method that corrects the forecast for climatological biases, whereas NCGR relates the calibrated parametric forecast distribution to the raw ensemble forecast through a regression model framework. For NCGR, the observed event trend and ensemble-mean event date are used to predict the central tendency of the predictive distribution. For modeling forecast uncertainty, we find that the ensemble-mean event date, which is related to forecast lead time, performs better than the ensemble variance itself. Using a multidecadal hindcast record from the Canadian Seasonal to Interannual Prediction System (CanSIPS), TAQM and NCGR are applied to produce categorical forecasts quantifying the probabilities for early, normal, and late ice retreat and advance. While TAQM performs better than adjusting the raw forecast for mean and linear trend bias, NCGR is shown to outperform TAQM in terms of reliability, skill, and an improved tendency for forecast probabilities to be no worse than climatology. Testing various cross-validation setups, we find that NCGR remains useful when shorter hindcast records (~20 years) are available. By applying NCGR to operational forecasts, stakeholders can be more confident in using seasonal forecasts of sea ice event timing for planning purposes.

SIGNIFICANCE STATEMENT

As Earth warms, the Arctic is shifting toward a longer open water season. With maritime access consequently increasing, stakeholders are valuing trustworthy information on the timing of transitional sea ice cover provided by seasonal forecasting models. In this study we advance seasonal predictions of the timing of local ice retreat and advance by extending these predictions to include critical information on forecast uncertainty. To do this, we tailor the established “ensemble model output statistics” calibration framework to sea ice retreat and advance dates, and construct probabilistic forecasts of early, normal, and late sea ice timing. Evaluating these predictions over a historical period indicates that stakeholders can place trust in forecast probabilities of sea ice timing for planning purposes.

© 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: Arlan Dirkson, arlan.dirkson@gmail.com
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