Prediction of Arctic Sea Ice Extent

 

Decline in the Arctic sea ice extent (SIE) has profound socio-economic implications and is a focus of active scientific research. Of particular interest is prediction of SIE on subseasonal time scales, i.e.~from early summer into fall, when sea ice coverage in Arctic reaches its minimum. However, subseasonal forecasting of SIE is very challenging due to the high variability of ocean and atmosphere over Arctic in summer, as well as shortness of observational data and inadequacies of the physics-based models to simulate sea-ice dynamics. The Sea Ice Outlook (SIO) by Sea Ice Prediction Network (SIPN) is a collaborative effort to facilitate and improve subseasonal prediction of September SIE by physics-based and data-driven statistical models.



Data-adaptive Harmonic Decomposition (DAHD) and Multiscale Stuart-Landau Models (MSLM) techniques [Chekroun and Kondrashov, 2017; Kondrashov et al. 2018]  have been shown successful for retrospective and real-time summertime forecasting of Arctic Sea Ice extent in key four Arctic regions .


In particular, the real-time DAHD-MSLM prediction of September SIE was fairly accurate and very competitive among

statistical and physics-based models in 2016, 2017 and 2018 Sea Ice Outlook (SIO) submissions: the average of summertime DAHD-MSLM Outlooks (June, July, August) was within 0.2 million km2 of the observed September pan-Arctic SIE for three years in a row, given a total SIE area of roughly 5.0 million km2:


                                           

                                          2016: 4.90 (DAHD-MSLM) vs 4.70 (observed)  million km2

                                           2017: 4.57 (DAHD-MSLM) vs 4.80 (observed)  million km2

                                                                2018: 4.53  (DAHD-MSLM) vs 4.71 (observed)  million km2



The key factors to this success are associated with DAHD-MSLM ability to disentangle complex regional dynamics of Arctic Sea ice by data-adaptive harmonic spatio-temporal patterns that reduce the data-driven modeling effort to elemental models  stacked per frequency with fixed and small number of model coefficients to estimate.


References

  1. Kondrashov, D., M. D. Chekroun, and M. Ghil, 2018:

Data-adaptive harmonic decomposition and prediction of Arctic sea ice extent,

Dynamics and Statistics of the Climate System, 3(1), doi:10.1093/climsys/dzy001.


  1. Chekroun, M. D., and D. Kondrashov, 2017:

Data-adaptive harmonic spectra and multilayer Stuart-Landau models,

Chaos, 27, 093110: doi:10.1063/1.4989400, HAL preprint.


  1. Kondrashov, D., M. D. Chekroun, X. Yuan, and M. Ghil, 2018:

Data-adaptive Harmonic Decomposition and Stochastic Modeling of Arctic Sea Ice,

In: Tsonis A. (eds) Advances in Nonlinear Geosciences. Springer, doi:10.1007/978-3-319-58895-7_10.



Predictions of the Arctic SIE in the Sea Ice Outlook (SIO) for 2016; the red square marks DAHD-MSLM prediction of September SIE. Contributions as box plots, broken down by type of method. Boxes show medians and interquartile ranges. Colors identify method types, and n denotes the number of contributions. Individual boxes for each method represent, from left to right, contributions to the June, July, and August SIO. The heavy gray line shows the 2016 observed September SIE from the NSIDC index, from https://www.arcus.org/sipn/sea-ice-outlook/2016/post-season.



Predictions of the Arctic SIE in the Sea Ice Outlook (SIO) for 2017; the red square marks DAHD-MSLM prediction of September SIE. Contributions as box plots, broken down by type of method. Boxes show medians and interquartile ranges. Colors identify method types, and n denotes the number of contributions. Individual boxes for each method represent, from left to right, contributions to the June, July, and August SIO. The heavy gray line shows the 2017 observed September SIE from the NSIDC index, from https://www.arcus.org/sipn/sea-ice-outlook/2017/post-season.



Predictions of the Alaskan-region SIE in the Sea Ice Outlook (SIO) for 2017; the red square marks DAHD-MSLM prediction of September SIE. Contributions as box plots, broken down by type of method. Boxes show medians and interquartile ranges. Colors identify method types, and n denotes the number of contributions. Individual boxes for each method represent, from left to right, contributions to the June, July, and August SIO. The heavy gray line shows the 2017 observed September SIE from the NSIDC index, from https://www.arcus.org/sipn/sea-ice-outlook/2017/post-season.


2017 Sea Ice Outlook

2016 Sea Ice Outlook

2018 Sea Ice Outlook


2018 Outlook contributions by group for June (blue dot), July (green triangle), and August (orange diamond) are organized by general type of method. The 2018 observed September sea ice minimum is shown by dotted grey line, from https://www.arcus.org/sipn/sea-ice-outlook/2018/post-season.



2018 Outlook contributions for Alaska shown by group for June (blue dot), July (green triangle), and August (orange diamond) are organized by general type of method. The 2018 observed September sea ice minimum in Alaska is shown by dotted grey line. from https://www.arcus.org/sipn/sea-ice-outlook/2018/post-season.