Skip to main content

Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting

Climate Dynamics volume 52, pages 5507–5525 (2019)Cite this article

Abstract

Predicting Arctic sea ice extent is a notoriously difficult forecasting problem, even for lead times as short as one month. Motivated by Arctic intraannual variability phenomena such as reemergence of sea surface temperature and sea ice anomalies, we use a prediction approach for sea ice anomalies based on analog forecasting. Traditional analog forecasting relies on identifying a single analog in a historical record, usually by minimizing Euclidean distance, and forming a forecast from the analog’s historical trajectory. Here an ensemble of analogs is used to make forecasts, where the ensemble weights are determined by a dynamics-adapted similarity kernel, which takes into account the nonlinear geometry on the underlying data manifold. We apply this method for forecasting pan-Arctic and regional sea ice area and volume anomalies from multi-century climate model data, and in many cases find improvement over the benchmark damped persistence forecast. Examples of success include the 3–6 month lead time prediction of Arctic sea ice area, the winter sea ice area prediction of some marginal ice zone seas, and the 3–12 month lead time prediction of sea ice volume anomalies in many central Arctic basins. We discuss possible connections between KAF success and sea ice reemergence, and find KAF to be successful in regions and seasons exhibiting high interannual variability.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

References

  • Alessandrini S, Delle Monache L, Sperati S, Nissen J (2015) A novel application of an analog ensemble for short-term wind power forecasting. Renew Energy 76:768–781

    Article  Google Scholar 

  • Alexander R, Zhao Z, Szkely E, Giannakis D (2017) Kernel analog forecasting of tropical intraseasonal oscillations. J Atmos Sci 74(4):1321–1342

    Article  Google Scholar 

  • Atencia A, Zawadzki I (2015) A comparison of two techniques for generating nowcasting ensembles. Part II: Analogs selection and comparison of techniques. Mon Weather Rev 143(7):2890–2908

    Article  Google Scholar 

  • Blanchard-Wrigglesworth E, Bitz CM (2014) Characteristics of Arctic sea-ice thickness variability in GCMs. J Clim 27(21):8244–8258

    Article  Google Scholar 

  • Blanchard-Wrigglesworth E, Armour KC, Bitz CM, DeWeaver E (2011a) Persistence and inherent predictability of Arctic sea ice in a GCM ensemble and observations. J Clim 24(1):231–250

    Article  Google Scholar 

  • Blanchard-Wrigglesworth E, Bitz C, Holland M (2011b) Influence of initial conditions and climate forcing on predicting Arctic sea ice. Geophys Res Lett 38:18

    Article  Google Scholar 

  • Blanchard-Wrigglesworth E, Cullather R, Wang W, Zhang J, Bitz C (2015) Model forecast skill and sensitivity to initial conditions in the seasonal Sea Ice Outlook. Geophys Res Lett 42(19):8042–8048

    Article  Google Scholar 

  • Broomhead DS, King GP (1986) Extracting qualitative dynamics from experimental data. Phys D Nonlinear Phenom 20(2–3):217–236

    Article  Google Scholar 

  • Bushuk M, Giannakis D (2015) Sea-ice reemergence in a model hierarchy. Geophys Res Lett 42(13):5337–5345

    Article  Google Scholar 

  • Bushuk M, Giannakis D (2017) The seasonality and interannual variability of Arctic sea ice reemergence. J Clim 30(12):4657–4676. https://doi.org/10.1175/JCLI-D-16-0549.1

    Article  Google Scholar 

  • Bushuk M, Giannakis D, Majda AJ (2014) Reemergence mechanisms for North Pacific sea ice revealed through nonlinear Laplacian spectral analysis. J Clim 27(16):6265–6287

    Article  Google Scholar 

  • Bushuk M, Giannakis D, Majda AJ (2015) Arctic sea ice reemergence: the role of large-scale oceanic and atmospheric variability. J Clim 28(14):5477–5509

    Article  Google Scholar 

  • Bushuk M, Msadek R, Winton M, Vecchi GA, Gudgel R, Rosati A, Yang X (2017) Summer enhancement of Arctic sea ice volume anomalies in the September-ice zone. J Clim 30(7):2341–2362

    Article  Google Scholar 

  • Chevallier M, Salas-Mélia D (2012) The role of sea ice thickness distribution in the Arctic sea ice potential predictability: a diagnostic approach with a coupled GCM. J Clim 25(8):3025–3038

    Article  Google Scholar 

  • Chevallier M, Salas y Mélia D, Voldoire A, Déqué M, Garric G (2013) Seasonal forecasts of the pan-Arctic sea ice extent using a GCM-based seasonal prediction system. J Clim 26(16):6092–6104

    Article  Google Scholar 

  • Comeau D, Zhao Z, Giannakis D, Majda AJ (2017) Data-driven prediction strategies for low-frequency patterns of North Pacific climate variability. Clim Dyn 48(5):1855–1872

    Article  Google Scholar 

  • Day J, Tietsche S, Hawkins E (2014) Pan-Arctic and regional sea ice predictability: initialization month dependence. J Clim 27(12):4371–4390

    Article  Google Scholar 

  • Deyle ER, Sugihara G (2011) Generalized theorems for nonlinear state space reconstruction. PLoS One 6(3):e18,295

    Article  Google Scholar 

  • Van den Dool H (1994) Searching for analogues, how long must we wait? Tellus A 46(3):314–324

    Article  Google Scholar 

  • Drosdowsky W (1994) Analog (nonlinear) forecasts of the Southern Oscillation index time series. Weather Forecast 9(1):78–84

    Article  Google Scholar 

  • Fernández A, Rabin N, Fishelov D, Dorronsoro JR (2013) Auto-adaptative laplacian pyramids for high-dimensional data analysis. arXiv:13116594 (arXiv preprint )

  • Gent PR, Danabasoglu G, Donner LJ, Holland MM, Hunke EC, Jayne SR, Lawrence DM, Neale RB, Rasch PJ, Vertenstein M (2011) The community climate system model version 4. J Clim 24(19):4973–4991

    Article  Google Scholar 

  • Germe A, Chevallier M, y Mélia DS, Sanchez-Gomez E, Cassou C (2014) Interannual predictability of Arctic sea ice in a global climate model: regional contrasts and temporal evolution. Clim Dyn 43(9–10):2519–2538

    Article  Google Scholar 

  • Giannakis D (2015) Dynamics-adapted cone kernels. SIAM J Appl Dyn Syst 14(2):556–608

    Article  Google Scholar 

  • Giannakis D, Majda AJ (2012a) Comparing low-frequency and intermittent variability in comprehensive climate models through nonlinear Laplacian spectral analysis. Geophys Res Lett 39:10

    Google Scholar 

  • Giannakis D, Majda AJ (2012b) Nonlinear Laplacian spectral analysis for time series with intermittency and low-frequency variability. Proc Natl Acad Sci 109(7):2222–2227

    Article  Google Scholar 

  • Giannakis D, Majda AJ (2013) Nonlinear Laplacian spectral analysis: capturing intermittent and low-frequency spatiotemporal patterns in high-dimensional data. Stat Anal Data Min 6(3):180–194

    Article  Google Scholar 

  • Giannakis D, Majda AJ (2014) Data-driven methods for dynamical systems: quantifying predictability and extracting spatiotemporal patterns. Mathematical and computational modeling: with applications in engineering and the natural and social sciences, p 288

  • Goessling HF, Tietsche S, Day JJ, Hawkins E, Jung T (2016) Predictability of the Arctic sea ice edge. Geophys Res Lett 43(4):1642–1650

    Article  Google Scholar 

  • Goosse H, Arzel O, Bitz CM, de Montety A, Vancoppenolle M (2009) Increased variability of the Arctic summer ice extent in a warmer climate. Geophys Res Lett 36:23

    Article  Google Scholar 

  • Guemas V, Blanchard-Wrigglesworth E, Chevallier M, Day JJ, Déqué M, Doblas-Reyes FJ, Fučkar NS, Germe A, Hawkins E, Keeley S (2016) A review on Arctic sea-ice predictability and prediction on seasonal to decadal time-scales. Q J R Meteorol Soc 142(695):546–561

    Article  Google Scholar 

  • Hamilton LC, Stroeve J (2016) 400 predictions: the search sea ice outlook 2008–2015. Polar Geogr 39(4):274–287

    Article  Google Scholar 

  • Holland MM, Stroeve J (2011) Changing seasonal sea ice predictor relationships in a changing Arctic climate. Geophys Res Lett 38:18

    Google Scholar 

  • Holland MM, Bitz CM, Tremblay L, Bailey DA, et al (2008) The role of natural versus forced change in future rapid summer Arctic ice loss. Arctic sea ice decline: observations, projections, mechanisms, and implications, pp 133–150

  • Holland MM, Bailey DA, Vavrus S (2011) Inherent sea ice predictability in the rapidly changing Arctic environment of the Community Climate System Model, version 3. Clim Dyn 36(7–8):1239–1253

    Article  Google Scholar 

  • Holland MM, Bailey DA, Briegleb BP, Light B, Hunke E (2012) Improved sea ice shortwave radiation physics in CCSM4: the impact of melt ponds and aerosols on Arctic sea ice*. J Clim 25(5):1413–1430

    Article  Google Scholar 

  • Hunke E, Lipscomb W (2008) CICE: the Los Alamos sea ice model, documentation and software, version 4.0, Los Alamos National Laboratory tech. rep. Tech. rep., LA-CC-06-012

  • Kaleschke L, Tian-Kunze X, Maaß N, Mäkynen M, Drusch M (2012) Sea ice thickness retrieval from smos brightness temperatures during the Arctic freeze-up period. Geophys Res Lett 39:5

    Article  Google Scholar 

  • Koenigk T, Mikolajewicz U (2009) Seasonal to interannual climate predictability in mid and high northern latitudes in a global coupled model. Clim Dyn 32(6):783–798

    Article  Google Scholar 

  • Kwok R, Cunningham G, Zwally H, Yi D (2007) Ice, cloud, and land elevation satellite (icesat) over Arctic sea ice: retrieval of freeboard. J Geophys Res Oceans 112:C12

    Google Scholar 

  • Lindsay R, Zhang J, Schweiger A, Steele M (2008) Seasonal predictions of ice extent in the Arctic Ocean. J Geophys Res Oceans 113:C2

    Article  Google Scholar 

  • Liu Y, Ren HL (2017) Improving ENSO prediction in CFSV2 with an analogue-based correction method. Int J Climatol 37(15):5035–5046

    Article  Google Scholar 

  • Lorenz EN (1969) Atmospheric predictability as revealed by naturally occurring analogues. J Atmos Sci 26(4):636–646

    Article  Google Scholar 

  • Neale RB, Richter JH, Conley AJ, Park S, Lauritzen PH, Gettelman A, Williamson DL, et al. (2010) Description of the NCAR community atmosphere model (cam 4.0). NCAR Tech Note NCAR/TN-485+ STR

  • Ogi M, Yamazaki K, Wallace JM (2010) Influence of winter and summer surface wind anomalies on summer Arctic sea ice extent. Geophys Res Lett 37:7

    Article  Google Scholar 

  • Packard NH, Crutchfield JP, Farmer JD, Shaw RS (1980) Geometry from a time series. Phys Rev Lett 45(9):712

    Article  Google Scholar 

  • Rabin N, Coifman RR (2012) Heterogeneous datasets representation and learning using diffusion maps and Laplacian pyramids. In: SDM, SIAM, pp 189–199

  • Rigor IG, Wallace JM (2004) Variations in the age of Arctic sea-ice and summer sea-ice extent. Geophys Res Lett 31:9

    Article  Google Scholar 

  • Rigor IG, Wallace JM, Colony RL (2002) Response of sea ice to the Arctic oscillation. J Clim 15(18):2648–2663

    Article  Google Scholar 

  • Robinson JC (2005) A topological delay embedding theorem for infinite-dimensional dynamical systems. Nonlinearity 18(5):2135

    Article  Google Scholar 

  • Sauer T, Yorke JA, Casdagli M (1991) Embedology. J Stat Phys 65(3–4):579–616

    Article  Google Scholar 

  • Schröder D, Feltham DL, Flocco D, Tsamados M (2014) September Arctic sea-ice minimum predicted by spring melt-pond fraction. Nat Clim Change 4(5):353–357

    Article  Google Scholar 

  • Sigmond M, Fyfe J, Flato G, Kharin V, Merryfield W (2013) Seasonal forecast skill of Arctic sea ice area in a dynamical forecast system. Geophys Res Lett 40(3):529–534

    Article  Google Scholar 

  • Sigmond M, Reader MC, Flato GM, Merryfield WJ, Tivy A (2016) Skillful seasonal forecasts of Arctic sea ice retreat and advance dates in a dynamical forecast system. Geophys Res Lett 43(24):12,457–12,465

    Article  Google Scholar 

  • Smith LC, Stephenson SR (2013) New trans-Arctic shipping routes navigable by midcentury. Proc Natl Acad Sci 110(13):E1191–E1195

    Article  Google Scholar 

  • Smith R, Jones P, Briegleb B, Bryan F, Danabasoglu G, Dennis J, Dukowicz J, Eden C, Fox-Kemper B, Gent P, et al. (2010) The Parallel Ocean Program (POP) reference manual: ocean component of the community climate system model (CCSM). Los Alamos National Laboratory, LAUR-10-01853

  • Stroeve J, Hamilton LC, Bitz CM, Blanchard-Wrigglesworth E (2014) Predicting September sea ice: ensemble skill of the SEARCH sea ice outlook 2008–2013. Geophys Res Lett 41(7):2411–2418

    Article  Google Scholar 

  • Stroeve JC, Kattsov V, Barrett A, Serreze M, Pavlova T, Holland M, Meier WN (2012) Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations. Geophys Res Lett 39:16

    Article  Google Scholar 

  • Takens F (1981) Detecting strange attractors in turbulence. Dynamical systems and turbulence, Warwick 1980. Springer, Berlin, pp 366–381

    Chapter  Google Scholar 

  • Tietsche S, Notz D, Jungclaus JH, Marotzke J (2013) Predictability of large interannual Arctic sea-ice anomalies. Clim Dyn 41(9–10):2511–2526

    Article  Google Scholar 

  • Tietsche S, Day J, Guemas V, Hurlin W, Keeley S, Matei D, Msadek R, Collins M, Hawkins E (2014) Seasonal to interannual Arctic sea ice predictability in current global climate models. Geophys Res Lett 41(3):1035–1043

    Article  Google Scholar 

  • Tietsche S, Hawkins E, Day JJ (2016) Atmospheric and oceanic contributions to irreducible forecast uncertainty of Arctic surface climate. J Clim 29(1):331–346

    Article  Google Scholar 

  • Tilling RL (2016) Near-real-time Arctic sea ice thickness and volume from CryoSat-2. Cryosphere 10(5):2003

    Article  Google Scholar 

  • Wang L, Yuan X, Ting M, Li C (2016) Predicting summer Arctic sea ice concentration intraseasonal variability using a vector autoregressive model. J Clim 29(4):1529–1543

    Article  Google Scholar 

  • Wang W, Chen M, Kumar A (2013) Seasonal prediction of Arctic sea ice extent from a coupled dynamical forecast system. Mon Weather Rev 141(4):1375–1394

    Article  Google Scholar 

  • Xavier PK, Goswami BN (2007) An analog method for real-time forecasting of summer monsoon subseasonal variability. Mon Weather Rev 135(12):4149–4160

    Article  Google Scholar 

  • Yeager SG, Karspeck AR, Danabasoglu G (2015) Predicted slowdown in the rate of Atlantic sea ice loss. Geophys Res Lett 42:24

    Article  Google Scholar 

  • Zhang J, Rothrock D (2003) Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates. Mon Weather Rev 131(5):845–861

    Article  Google Scholar 

  • Zhang J, Steele M, Lindsay R, Schweiger A, Morison J (2008) Ensemble 1-year predictions of Arctic sea ice for the spring and summer of 2008. Geophys Res Lett 35:8

    Google Scholar 

  • Zhao Z, Giannakis D (2016) Analog forecasting with dynamics-adapted kernels. Nonlinearity 29(9):2888

    Article  Google Scholar 

Download references

Acknowledgements

The research of Andrew Majda and Dimitrios Giannakis is partially supported by ONR MURI Grant 25-74200-F7112. Darin Comeau was supported as a postdoctoral fellow through this Grant. Dimitrios Giannakis and Zhizhen Zhao are partially supported by NSF Grant DMS-1521775. Dimitrios Giannakis also acknowledges support from ONR Grant N00014-14-1-0150. Darin Comeau also acknowledges additional support from Regional and Global Climate Modeling program of the US Department of Energy Office of Science, as a contribution to the HiLAT Project. We thank Mitch Bushuk for helpful discussions. We also thank two anonymous reviewers for their helpful comments in reviewing this manuscript.

Author information

Author notes

  1. Darin Comeau

    Present address: Climate, Ocean, and Sea Ice Modeling Group, Computational Physics and Methods Group (CCS-2), Los Alamos National Laboratory, Los Alamos, NM, USA

Authors and Affiliations

  1. Center for Atmosphere Ocean Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA

    Darin Comeau, Dimitrios Giannakis & Andrew J. Majda

  2. Department of Electrical and Computer Engineering, Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Zhizhen Zhao

Authors
  1. Darin Comeau
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dimitrios Giannakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhizhen Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andrew J. Majda
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Darin Comeau.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Comeau, D., Giannakis, D., Zhao, Z. et al. Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting. Clim Dyn 52, 5507–5525 (2019). https://doi.org/10.1007/s00382-018-4459-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-018-4459-x

Keywords

  • Analog Forecasting
  • Damped Persistence
  • Central Arctic Basin
  • Intensity Anomalies
  • Prediction Lead Time