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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. 

Outreach and communication with the public have substantial value in polar research, in which studies often find changes of global importance that are happening far out of sight from the majority of people living at lower latitudes. Seeking evidence on the effectiveness of outreach programs, the U.S. National Science Foundation sponsored large-scale survey assessments before and after the International Polar Year in 2007/2008. Polar-knowledge questions have subsequently been tested and refined through other nationwide and regional surveys. More than a decade of such work has established that basic but fairly specific knowledge questions, with all answer choices sounding plausible but one being uniquely correct, can yield highly replicable results. Those results, however, paint a mixed picture of knowledge. Some factual questions seem to be interpreted by many respondents as if they had been asked for their personal beliefs about climate change, so their responses reflect sociopolitical identity rather than physical-world knowledge. Other factual questions, by design, do not link in obvious ways to climate-change beliefs—so responses have simpler interpretations in terms of knowledge gaps, and education needs. 

How much does the US public know about polar regions? Researchers exploring this topic have occasionally mixed factual questions in among the more typical opinion queries on general-public surveys. A recent article in the Journal of Geoscience Education (Hamilton 2020) describes a key finding from these surveys: there are "two kinds" of polar knowledge. One kind is evoked by questions like this: Which of the following three statements do you think is more accurate? Over the past few years, the ice on the Arctic Ocean in late summer... - Covers less area than it did 30 years ago (correct) - Declined but then recovered to about the same area it had 30 years ago - Covers more area than it did 30 years ago The declining area of late-summer Arctic sea ice, tracked by satellites over the past 40 years, is a basic and widely reported scientific fact. On surveys, however, many people do not recognize this fact, but answer instead based on their opinion about global warming. Similar results occur if we ask whether, in recent decades, CO2 concentrations in the atmosphere have increased: again, many people give answers contrary to science, but reflecting instead their beliefs or political identity. Although these questions involve important and well-established facts, survey responses defy simple interpretation as indicators of knowledge. 

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.