skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: 1,000 Predictions: What's New and What's Old in a Retrospective Analysis of the Sea Ice Outlook, 2008-2020
Each Arctic summer since 2008, the Sea Ice Outlook (SIO) has invited researchers and the engaged public to contribute predictions regarding the September extent of Arctic sea ice. Then, each September, we see the accuracy or inaccuracy of those predictions. More than 1,000 individual predictions, based on many different methods, were contributed from 2008 to 2020. Earlier papers analyzed the ensemble skill of the first few hundred SIO contributions through 2013 ( Stroeve et al. 2014) and through 2015 ( Hamilton & Stroeve 2016). Here, I bring those analyses up to date with data through 2020. The main conclusions from earlier papers have proven to be robust, but unexpected new insights emerged as well. The long term downward trend in ice extent is reasonably well described as linear (R2 = 0.79) or quadratic (R2 = 0.81). Very large changes from the previous year’s extent in 2012 and 2013 resulted in the largest prediction errors. Both errors reflect one 2012 cyclone. For reasons not yet understood, SIO predictions especially those from dynamic modeling predict the previous year’s extent rather than the current year.  more » « less
Award ID(s):
1748325
PAR ID:
10207507
Author(s) / Creator(s):
Date Published:
Journal Name:
American Geophysical Union
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract This study quantifies the state of the art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multimodel dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–20 for predictions of pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on 1 June, 1 July, 1 August, and 1 September. This diverse set of statistical and dynamical models can individually predict linearly detrended pan-Arctic SIE anomalies with skill, and a multimodel median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and central Arctic sectors. The skill of dynamical and statistical models is generally comparable for pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least 3 months in advance. 
    more » « less
  2. Although standard statistical methods and climate models can simulate and predict sea-ice changes well, it is still very hard to distinguish some direct and robust factors associated with sea-ice changes from its internal variability and other noises. Here, with long-term observations (38 years from 1980 to 2017), we apply the causal effect networks algorithm to explore the direct precursors of September Arctic sea-ice extent by adjusting the maximal lead time from one to eight months. For lead time of more than three months, June downward longwave radiation flux in the Canadian Arctic Archipelago is the only one precursor. However, for lead time of 1–3 months, August sea-ice concentration in Western Arctic represents the strongest positive correlation with September sea-ice extent, while August sea-ice concentration factors in other regions have weaker influences on the marginal seas. Other precursors include August wind anomalies in the lower latitudes accompanied with an Arctic high pressure anomaly, which induces the sea-ice loss along the Eurasian coast. These robust precursors can be used to improve the seasonal predictions of Arctic sea ice and evaluate the climate models. 
    more » « less
  3. This dataset contains data used in the paper: Revisiting the Last Ice Area Projections from a High-Resolution Global Earth System Model - Fol et al (2025). Results are organized in excel files or numpy arrays with the dataset name, variable and ensemble member (for simulations) in the name of the file. See below for more information on what variables are included in the files and their structure.  CESM_HR :  CESM_HR_SIAFluxes - per ensemble member: Timeseries of monthly SIA flux per gate. CESM_HR_fluxesCAADiv.npy, _fluxesQEIDiv.npy, _fuxesQEIDivMeltSeason.npy : Timeseries of annual divergence over the Queen Elizabeth Islands and the Southern Canadian Arctic Archipelago derived from monthly SIA fluxes at the entry and exit gates. CESM_HR_ThicknessDistribution.xlsx : Thickness distribution for the LIA-N, QEI, and CAA-S computed from the simulated thickness distribution (aicen001, aicen002, aicen003, aicen004, aicen005). CESM_HR_tendencies - per ensemble member- per region (LIA-N, QEI, CAA-S). Timeseries of melt season integrated thermodynamic, dynamic (advection and ridging terms) sea ice area loss. CESH_HR_sitPanArctic - per ensemble member: Timeseries of pan-Arctic mean may sea ice thickness. CESM_HR_sieSept and CESM_HR_sieMarch - per ensemble member- per region (LIA-N, QEI, CAA-S) and pan-Arctic : Timeseries of March or September sea ice extent, sea ice area. CESM_HR_sic - per ensemble member- per region (LIA-N, QEI, CAA-S): Timeseries of mean sea ice concentration for grid cells having more than 15% of SIC (no open water).  CESM_HR_meltSeason - per ensemble member- per region (LIA-N, QEI, CAA-S): Timeseries of annual freeze and melt onset dates allowing the definition of the melt season based on the thermodynamic sea ice area tendency crossing 0.  CESM_HR_mean_mayThickness.npy and CESM_HR_meanseptconc.npy: Results for map of the mean september sea ice concentration and may ice thickness for 1981-2000, 2001-2020, 2021-2040 and 2041-2060. CESM_LR :  CESM_LR_sieSept - per region (LIA-N, QEI, CAA-S) and pan-Arctic: Timeseries of September sea ice extent and sea ice area. CESM_LR_sitPanArctic.xlsx: Timeseries of pan-Arctic mean May sea ice thickness. CESM_LR_tendencies - per region (LIA-N, QEI, CAA-S). Timeseries of melt season integrated thermodynamic, dynamic (advection and ridging terms) sea ice area loss. CESM_LR_meltSeason - per region (LIA-N, QEI, CAA-S): Timeseries of annual freeze and melt onset dates allowing the definition of the melt season based on the thermodynamic sea ice area tendency crossing 0. CESM2_LE:  CESM2_LE_CAA_sept, _LIAN_sept, _QEI_sept, panArctic: Mean September sea ice extent and sea ice area per region. There is one excel tab per ensemble member in each file. CESM2_LE_pan_Arctic_hi_may: Pan-Arctic mean May sea ice thickness. There is one excel tab per ensemble member in each file. PIOMAS:  PIOMAS_panArctic_hi.xlsx: Timeseries of mean may sea ice thickness. PIOMAS_mean_1981_2000_mean_mayThickness.npy : Results for map of the mean May ice thickness for 1981-2000 and 2001-2020. Observations: CIS_marchSept_1982_1990_sie - per region (QEI and CAA-S) :  Timeseries of March and September mean sea ice extent and area. CIS ice charts do not fully cover the LIA-N.  NSDICCDR_1979_2023_sia - per region (LIA-N, QEI, CAA-S) and pan-Arctic : Timeseries of March and September mean sea ice extent and area. NSDICCDR_1981_2000_mean_septConc.npy: Results for map of the mean September sea ice concentration for 1981-2000 and 2001-2020. NSDICCDR_1981_2000_LIAN_sic.xlsx: Timeseries of monthly mean sea ice concentration in the LIA-N. NSDICCDR_1981_2000_QEI_sic.xlsx: Timeseries of monthly mean sea ice concentration in the QEI. These results are derived from the following datasets:  Ensemble members 1 and 3 of simulations from the high-resolution Community Earth System Model version 1.3 (CESM1.3-HR) produced for the International Laboratory for High-Resolution Earth System Prediction (iHESP) by the Qingdao National Laboratory for Marine Science and Technology (QNLM), Texas A&M University (TAMU), and the U.S. National Center for Atmospheric Research (NCAR). The lower resolution simulation is also used (CESM1.3-LR). (Chang et al., 2020; Zhang et al., 2020). The 100-ensemble members Community Earth System Model (version 2) Large Ensemble (CESM2-LE) (Danabasoglu et al., 2020). Satellite-derived monthly mean SIA fluxes through entry and exit gates of the CAA and Nares Strait (Howell et a., 2019; 2021; 2023; 2024; Smedsrud et al., 2017; Kwok, 2006). The National Snow and Ice Data Center (NSIDC) Climate Data Record (CDR) (version 4) sea ice concentration, stored on a 25 x 25 km polar stereographic grid centered on the North Pole from 1979 to 2023 (Meier et al., 2021). The gridded version of the regional Canadian Ice Service (CIS) Digital Archive ice charts from the Eastern and Western Arctic regions (Tivy et al., 2011). The Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) assimilated sea ice concentration and derived ice thickness distribution estimates in the Arctic from 1978 to 2022 (Zhang et al., 2000).   
    more » « less
  4. Abstract Over the past decades, Arctic climate has exhibited significant changes characterized by strong Pan-Arctic warming and a large scale wind shift trending toward an anticyclonic anomaly centered over Greenland and the Arctic ocean. Recent work has suggested that this wind change is able to warm the Arctic atmosphere and melt sea ice through dynamical-driven warming, moistening and ice drift effects. However, previous examination of this linkage lacks a capability to fully consider the complex nature of the sea ice response to the wind change. In this study, we perform a more rigorous test of this idea by using a coupled high-resolution modelling framework with observed winds nudged over the Arctic that allows for a comparison of these wind-induced effects with observations and simulated effects forced by anthropogenic forcing. Our nudging simulation can well capture observed variability of atmospheric temperature, sea ice and the radiation balance during the Arctic summer and appears to simulate around 30% of Arctic warming and sea ice melting over the whole period (1979-2020) and more than 50% over the period 2000 to 2012, which is the fastest Arctic warming decade in the satellite era. In particular, in the summer of 2020, a similar wind pattern reemerged to induce the second-lowest sea ice extent since 1979, suggesting that large scale wind changes in the Arctic is essential in shaping Arctic climate on interannual and interdecadal time scales and may be critical to determine Arctic climate variability in the coming decades. 
    more » « less
  5. Abstract. Basic statistical metrics such as autocorrelations and across-region lagcorrelations of sea ice variations provide benchmarks for the assessments offorecast skill achieved by other methods such as more sophisticatedstatistical formulations, numerical models, and heuristic approaches. In thisstudy we use observational data to evaluate the contribution of the trend tothe skill of persistence-based statistical forecasts of monthly and seasonalice extent on the pan-Arctic and regional scales. We focus on the BeaufortSea for which the Barnett Severity Index provides a metric of historicalvariations in ice conditions over the summer shipping season. The varianceabout the trend line differs little among various methods of detrending(piecewise linear, quadratic, cubic, exponential). Application of thepiecewise linear trend calculation indicates an acceleration of the winterand summer trends during the 1990s. Persistence-based statistical forecastsof the Barnett Severity Index as well as September pan-Arctic ice extent showsignificant statistical skill out to several seasons when the data includethe trend. However, this apparent skill largely vanishes when the data aredetrended. In only a few regions does September ice extent correlatesignificantly with antecedent ice anomalies in the same region more than 2months earlier. The springtime “predictability barrier” in regionalforecasts based on persistence of ice extent anomalies is not reduced by theinclusion of several decades of pre-satellite data. No region showssignificant correlation with the detrended September pan-Arctic ice extent atlead times greater than a month or two; the concurrent correlations arestrongest with the East Siberian Sea. The Beaufort Sea's ice extent as farback as July explains about 20 % of the variance of the Barnett SeverityIndex, which is primarily a September metric. The Chukchi Sea is the onlyother region showing a significant association with the Barnett SeverityIndex, although only at a lead time of a month or two. 
    more » « less