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The timing of sea ice retreat and advance in Arctic coastal waters varies substantially from year to year. Various activities, ranging from marine transport to the use of sea ice as a platform for industrial activity or winter travel, are af- fected by variations in the timing of breakup and freeze-up, resulting in a need for indicators to document the regional and temporal variations in coastal areas. The primary objec- tive of this study is to use locally based metrics to construct indicators of breakup and freeze-up in the Arctic and subarc- tic coastal environment. The indicators developed here are based on daily sea ice concentrations derived from satellite passive-microwave measurements. The “day of year” indica- tors are designed to optimize value for users while building on past studies characterizing breakup and freeze-up dates in the open pack ice. Relative to indicators for broader adja- cent seas, the coastal indicators generally show later breakup at sites known to have landfast ice. The coastal indicators also show earlier freeze-up at some sites in comparison with freeze-up for broader offshore regions, likely tied to ear- lier freezing of shallow-water regions and areas affected by freshwater input from nearby streams and rivers. A factor analysis performed to synthesize the local indicator varia- tions shows that the local breakup and freeze-up indicators have greater spatial variability than corresponding metrics based on regional ice coverage. However, the trends towards earlier breakup and later freeze-up are unmistakable over the post-1979 period in the synthesized metrics of coastal breakup and freeze-up and the corresponding regional ice coverage. The findings imply that locally defined indicators can serve as key links between pan-Arctic or global indica- tors such as sea ice extent or volume and local uses of sea ice, with the potential to inform community-scale adaptation and response. 

Community monitoring can track environmental phenomena, resource use, and natural resource management processes of concern to community mem- bers. It can also contribute to planning and decision-making and empower community members in resource management. While community moni- toring that addresses the environmental crisis is growing, it also gathers data on other global challenges: climate change, social welfare, and health. Some environmental community monitoring programs are challenged by limited collective action and community participation, insufficient state re- sponsiveness to data and proposals, and lack of sustainability over time. Addi- tionally, community members monitoring the environment are increasingly harassed and sometimes killed. Community monitoring is more effective with improved data collection, improved data management and sharing, and stronger efforts to meet community information needs, enable conflict resolution, and strengthen self-determination. Other promising areas for development are further incorporating governance issues, embracing integrated approaches at the community level, and establishing stronger links to national and global frameworks. 

Assimilation of remote-sensing products of sea ice thickness (SIT) into sea ice–ocean models has been shown to improve the quality of sea ice forecasts. Key open questions are whether assimilation of lower-level data products such as radar freeboard (RFB) can further improve model performance and what performance gains can be achieved through joint assimilation of these data products in combination with a snow depth product. The Arctic Mission Benefit Analysis system was developed to address this type of question. Using the quantitative network design (QND) approach, the system can evaluate, in a mathematically rigorous fashion, the observational constraints imposed by individual and groups of data products. We demonstrate the approach by presenting assessments of the observation impact (added value) of different Earth observation (EO) products in terms of the uncertainty reduction in a 4-week forecast of sea ice volume (SIV) and snow volume (SNV) for three regions along the Northern Sea Route in May 2015 using a coupled model of the sea ice–ocean system, specifically the Max Planck Institute Ocean Model. We assess seven satellite products: three real products and four hypothetical products. The real products are monthly SIT, sea ice freeboard (SIFB), and RFB, all derived from CryoSat-2 by the AlfredWegener Institute. These are complemented by two hypothetical monthly laser freeboard (LFB) products with low and high accuracy, as well as two hypothetical monthly snow depth products with low and high accuracy. On the basis of the per-pixel uncertainty ranges provided with the CryoSat-2 SIT, SIFB, and RFB products, the SIT and RFB achieve a much better performance for SIV than the SIFB product. For SNV, the performance of SIT is only low, the performance of SIFB is higher and the performance of RFB is yet higher. A hypothetical LFB product with low accuracy (20 cm uncertainty) falls between SIFB and RFB in performance for both SIV and SNV. A reduction in the uncertainty of the LFB product to 2 cm yields a significant increase in performance. Combining either of the SIT or freeboard products with a hypothetical snow depth product achieves a significant performance increase. The uncertainty in the snow product matters: a higher-accuracy product achieves an extra performance gain. Providing spatial and temporal uncertainty correlations with the EO products would be beneficial not only for QND assessments, but also for assimilation of the products. 

Impacts of a warming climate are amplified in the Arctic. One notorious impact is recent and record-breaking summertime sea-ice loss. Expanding areas of open water and a prolonged ice-free season create opportunity for some industries but challenge indigenous peoples relying on sea ice for transportation and access to food. The observed and projected increase of Arctic maritime activity requires accurate sea-ice forecasts to protect life, environment, and property. Motivated by emerging prediction needs on the operational timescale (≤10 days), this study explores where local indigenous knowledge (LIK) fits into the forecaster toolbox and how it can be woven into useful sea-ice information products. The 2011 spring ice retreat season in the Bering Sea is presented as a forecasting case study. LIK, housed in a database of community-based ice and weather logs, and an ice-ocean forecast model developed by the US Navy are analyzed for their ability to provide information relevant to stakeholder needs. Additionally, metrics for verifying numerical sea-ice forecasts on multiple scales are derived. The model exhibits skill relative to persistence and climatology on the regional scale. At the community scale, we discuss how LIK and new model guidance can enhance public sea-ice information resources.