Improved sea ice forecasting through spatiotemporal bias correction

Journal of Climate

Volumn 30, Issue 23, 2017, Pages 9493-9510

  Director, Hannah M ^a   Raftery, Adrian E ^a   Bitz, Cecilia M ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Arctic; Bias; Model errors; Sea ice; Statistical techniques

Indexed keywords

ERRORS; FORECASTING; MEAN SQUARE ERROR; SEA ICE;

ARCTIC; BIAS; MODEL ERRORS; ROOT MEAN SQUARE ERRORS; SEA ICE CONCENTRATION; SPATIO-TEMPORAL MODELS; STATISTICAL MODELING; STATISTICAL TECHNIQUES;

ICE;

CONCENTRATION (COMPOSITION); ERROR CORRECTION; FORECASTING METHOD; ICE COVER; MODELING; SEA ICE; SPATIOTEMPORAL ANALYSIS; STATISTICAL ANALYSIS;

ARCTIC;

EID: 85034074019     PISSN: 08948755     EISSN: None     Source Type: Journal    
DOI: 10.1175/JCLI-D-17-0185.1     Document Type: Article

References (45)

1. Arisido, M. W., C. Gaetan, D. Zanchettin, and A. Rubino, 2017: A Bayesian hierarchical approach for spatial analysis of climate model bias in multi-model ensembles. Stochastic Environ. Res. Risk Assess., doi:https://doi.org/10.1007/s00477-017-1383-2, in press
2. Blanchard-Wrigglesworth, E., K. C. Armour, C. M. Bitz, and E. DeWeaver, 2011a: Persistence and inherent predictability of Arctic sea ice in a GCM ensemble and observations. J. Climate, 24, 231-250, doi:https://doi.org/10.1175/2010JCLI3775.1
3. Blanchard-Wrigglesworth, E., C. M. Bitz, and M. M. Holland, 2011b: Influence of initial conditions and climate forcing on predicting Arctic sea ice. Geophys. Res. Lett., 38, L18503, doi:https://doi.org/10.1029/2011GL048807
4. Blanchard-Wrigglesworth, E., R. I. Cullather, W. Wang, J. Zhang, and C. M. Bitz, 2015: Model forecast skill and sensitivity to initial conditions in the seasonal sea ice outlook. Geophys. Res. Lett., 42, 8042-8048, doi:https://doi.org/10.1002/2015GL065860
5. Blanchard-Wrigglesworth, E., and Coauthors, 2017: Multi-model seasonal forecast of Arctic sea-ice: forecast uncertainty at pan-Arctic and regional scales. Climate Dyn., 49, 1399-1410, doi:https://doi.org/10.1007/s00382-016-3388-9
6. Brockwell, P. J., and R. A. Davis, 2016: Introduction to Time Series and Forecasting. 3rd ed. Springer, 425 pp
7. Collins, M., 2002: Climate predictability on interannual to decadal time scales: The initial value problem. Climate Dyn., 19, 671-692, doi:https://doi.org/10.1007/s00382-002-0254-8
8. Comiso, J. C., 2000: Bootstrap sea ice concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS, version 2. National Snow and Ice Data Center, accessed 1 July 2016, http://nsidc.org/data/docs/daac/nsidc0079_bootstrap_seaice.gd.html
9. 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, doi:https://doi.org/10.1029/2007GL031972
10. Day, J., S. Tietsche, and E. Hawkins, 2014: Pan-Arctic and regional sea ice predictability: Initialization month dependence. J. Climate, 27, 4371-4390, doi:https://doi.org/10.1175/JCLI-D-13-00614.1
11. Director, H. M., A. E. Raftery, and C. M. Bitz, 2017: IceCast: Apply statistical post-processing to improve sea ice predictions, version 1.1.0. R package, https://CRAN.R-project.org/package=IceCast
12. Douglas, D. H., and T. K. Peucker, 1973: Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica, 10, 112-122, doi:https://doi.org/10.3138/FM57-6770-U75U-7727
13. Dukhovskoy, D. S., J. Ubnoske, E. Blanchard-Wrigglesworth, H. R. Hiester, and A. Proshutinsky, 2015: Skill metrics for evaluation and comparison of sea ice models. J. Geophys. Res. Oceans, 120, 5910-5931, doi:https://doi.org/10.1002/2015JC010989
14. Fuckar, 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, 5200-5207, doi:https://doi.org/10.1002/2014GL060815
15. García-Serrano, J., and F. J. Doblas-Reyes, 2012: On the assessment of near-surface global temperature and North Atlantic multi-decadal variability in the ENSEMBLES decadal hindcast. Climate Dyn., 39, 2025-2040, doi:https://doi.org/10.1007/s00382-012-1413-1
16. Goddard, L., and Coauthors, 2013: A verification framework for interannual-to-decadal predictions experiments. Climate Dyn., 40, 245-272, doi:https://doi.org/10.1007/s00382-012-1481-2
17. Goessling, H. F., S. Tietsche, J. J. Day, E. Hawkins, and T. Jung, 2016: Predictability of the Arctic sea-ice edge. Geophys. Res. Lett., 43, 1642-1650, doi:https://doi.org/10.1002/2015GL067232
18. Guemas, V., and Coauthors, 2016: A review on Arctic sea-ice predictability and prediction on seasonal to decadal time-scales. Quart. J. Roy. Meteor. Soc., 142, 546-561, doi:https://doi.org/10.1002/qj.2401
19. Hazeleger, W., V. Guemas, B. Wouters, S. Corti, I. Andreu-Burillo, F. J. Doblas-Reyes, K. Wyser, and M. Caian, 2013: Multiyear climate predictions using two initialization strategies. Geophys. Res. Lett., 40, 1794-1798, doi:https://doi.org/10.1002/grl.50355
20. Holland, M. M., D. A. Bailey, and S. Vavrus, 2011: Inherent sea ice predictability in the rapidly changing Arctic environment of the Community Climate System Model, version 3. Climate Dyn., 36, 1239-1253, doi:https://doi.org/10.1007/s00382-010-0792-4
21. Huber, P. J., and E. M. Ronchetti, 2011: Robust Statistics. Springer, 380 pp
22. Huntington, H. P., and Coauthors, 2015: Vessels, risks, and rules: Planning for safe shipping in Bering Strait. Mar. Policy, 51, 119-127, doi:https://doi.org/10.1016/j.marpol.2014.07.027
23. Jones, P. W., 1997: A user's guide for SCRIP: A spherical coordinate remapping and interpolation package. Los Alamos National Laboratory Rep., 29 pp
24. 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, doi:https://doi.org/10.1029/2012GL052647
25. 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, 5124-5132, doi:https://doi.org/10.1002/2016GL068462
26. Maraun, D., 2016: Bias correcting climate change simulations-a critical review. Curr. Climate Change Rep., 2, 211-220, doi:https://doi.org/10.1007/s40641-016-0050-x
27. Meehl, G. A., and Coauthors, 2014: Decadal climate prediction: An update from the trenches. Bull. Amer. Meteor. Soc., 95, 243-267, doi:https://doi.org/10.1175/BAMS-D-12-00241.1
28. Messner, J. W., G. J. Mayr, A. Zeileis, and D. S. Wilks, 2014: Heteroscedastic extended logistic regression for postprocessing of ensemble guidance. Mon. Wea. Rev., 142, 448-456, doi:https://doi.org/10.1175/MWR-D-13-00271.1
29. Msadek, R., G. A. Vecchi, M. Winton, and R. G. Gudgel, 2014: Importance of initial conditions in seasonal predictions of Arctic sea ice extent. Geophys. Res. Lett., 41, 5208-5215, doi:https://doi.org/10.1002/2014GL060799
30. National Center for Atmospheric Research, 2017: Earth system grid at NCAR. NCAR, https://www.earthsystemgrid.org/home.html
31. National Snow and Ice Data Center, 2017: Region mask for the Northern Hemisphere. National Snow and Ice Data Center, accessed 22 June 2017, http://nsidc.org/data/polar-stereo/tools_masks.html
32. Parkinson, C. L., and D. J. Cavalieri, 2008: Arctic sea ice variability and trends, 1979-2006. J. Geophys. Res., 113, C07003, doi:https://doi.org/10.1029/2007JC004558
33. Sea Ice Prediction Network, 2014: Sea Ice Outlook 2014: August Report. SIPN, https://www.arcus.org/sipn/sea-ice-outlook/2014/august
34. Smith, L. C., and S. R. Stephenson, 2013: New trans-Arctic shipping routes navigable by midcentury. Proc. Natl. Acad. Sci. USA, 110, E1191-E1195, doi:https://doi.org/10.1073/pnas.1214212110
35. Stephenson, S. R., L. W. Brigham, and L. C. Smith, 2014: Marine accessibility along Russia's northern sea route. Polar Geogr., 37, 111-133, doi:https://doi.org/10.1080/1088937X.2013.845859
36. Stroeve, J. C., V. Kattsov, A. Barrett, M. Serreze, T. Pavlova, M. Holland, and W. N. Meier, 2012a: Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations. Geophys. Res. Lett., 39, L16502, doi:https://doi.org/10.1029/2012GL052676
37. Stroeve, J. C., M. C. Serreze, M. M. Holland, J. E. Kay, J. Malanik, and A. P. Barrett, 2012b: The Arctic's rapidly shrinking sea ice cover: a research synthesis. Climatic Change, 110, 1005-1027, doi:https://doi.org/10.1007/s10584-011-0101-1
38. Stroeve, J. C., L. C. Hamilton, C. M. Bitz, and E. Blanchard-Wrigglesworth, 2014: Predicting September sea ice: Ensemble skill of the SEARCH sea ice outlook 2008-2013. Geophys. Res. Lett., 41, 2411-2418, doi:https://doi.org/10.1002/2014GL059388
39. Strong, C., 2012: Atmospheric influence on Arctic marginal ice zone position and width in the Atlantic sector, February-April 1979-2010. Climate Dyn., 39, 3091-3102, doi:https://doi.org/10.1007/s00382-012-1356-6
40. Tietsche, S., D. Notz, J. H. Jungclaus, and J. Marotzke, 2013: Predictability of large interannual Arctic sea-ice anomalies. Climate Dyn., 41, 2511-2526, doi:https://doi.org/10.1007/s00382-013-1698-8
41. Tietsche, S., and Coauthors, 2014: Seasonal to interannual Arctic sea ice predictability in current global climate models. Geophys. Res. Lett., 41, 1035-1043, doi:https://doi.org/10.1002/2013GL058755
42. van Oldenborgh, G. J., F. J. Doblas-Reyes, B. Wouters, and W. Hazeleger, 2012: Decadal prediction skill in a multi-model ensemble. Climate Dyn., 38, 1263-1280, doi:https://doi.org/10.1007/s00382-012-1313-4
43. Vecchi, G., and Coauthors, 2014: On the seasonal forecasting of regional tropical cyclone activity. J. Climate, 27, 7994-8016, doi:https://doi.org/10.1175/JCLI-D-14-00158.1
44. Venables, W. N., and B. D. Ripley, 2002: Modern Applied Statistics with S. 4th ed. Springer, 498 pp
45. Wilks, D., 2009: Extending logistic regression to provide full-probability-distribution MOS forecasts. Meteor. Appl., 16, 361-368, doi:https://doi.org/10.1002/met.134