NSFGEO-NERC Advancing Predictability of Sea Ice: Phase 2 of the Sea Ice Prediction Network (SIPN2)

Lead Research Organisation: University College London
Department Name: Earth Sciences

Abstract

The shrinking Arctic sea-ice cover has captured the attention of the world. The downward September
trend has accelerated over the last decade, with the 10 lowest September sea-ice extents occurring in the
last 10 years. An essentially ice-free Arctic during summer is expected by mid-century. Loss of the seaice
cover has profound consequences for ecosystems and human activities in the Arctic, so there is an
urgent need to advance sea-ice predictions in all seasons at both the pan-Arctic and regional scales. A key
finding that emerged from the Sea Ice Prediction Network (SIPN) effort is that predictions of September
sea-ice extent tend to have less skill in extreme years that strongly depart from the trend line. The
objective of proposed research under Phase 2 of SIPN (SIPN2) is to improve forecast skill through
adopting a multi-disciplinary approach that includes modeling, new products, data analysis, scientific
networks, and stakeholder engagement. Specifically, the team will: (1) Investigate the sensitivity of
subseasonal-to-seasonal sea-ice predictability in the Alaska Arctic to variations in oceanic heat and largescale
atmospheric forcing using a dynamical model (NCAR CESM) and statistical forecasting tools,
focusing on spatial fields in addition to total extent summaries; (2) Assess the accuracy of Sea Ice
Outlook (SIO) submissions based on methodology and initialization; (3) Develop new observation-based
products for improving sea-ice predictions, including sea-ice thickness, surface roughness, melt ponds,
and snow depth; (4) Evaluate the socio-economic value of sea-ice forecasts to stakeholders who manage
ship traffic and coastal village resupply in the Alaska Sector, and engage the public in Arctic climate and
sea-ice prediction through blog exchanges, accessible SIO reports, bi-monthly webinars, and by making
public data sources useful to non-scientists and scientists alike; and (5) Continue and evolve network
activities to generate SIO forecasts and reporting for September minima as in SIPN and expand SIPN2
forecasts to include full spatial resolution and emerging ice-anomaly-months (October/November).

Planned Impact

A better quantification of the role of oceanic heat and climate variations in the Pacific sector, new observational-based sea-ice products, and network activities will advance understanding of seasonal predictability of Arctic sea ice, the limits of this predictability, and the economic value of forecasts for stakeholders. The network will examine origins and impacts of extreme ocean surface warming in preconditioning the ice cover in the Pacific Arctic for continued major reductions in sea-ice extent and duration.

This work will directly engage stakeholders that create and use sea-ice forecasts in Alaska and lead to improved safety around sea ice. Work under SIPN2 will also track public awareness and perceptions regarding sea ice, helping to raise understanding through accessible reports, discussions, and public data sources useful to non-scientists and scientists alike. Stakeholder engagement during the research process will potentially facilitate rapid research-to-operations implementation of the products of this work. Ongoing efforts by the SEARCH sea-ice action team and Sea Ice for Walrus Outlook (SIWO) will be leveraged to provide additional understanding of stakeholder needs.

Increasing uncertainty about future sea ice conditions presents a distinct challenge to industry, policymakers, and planners responsible for economic, safety, and risk mitigation decisions. The ability to accurately forecast the extent and duration of Arctic sea ice on different timescales provides significant implications for the operation of wide ranging Arctic maritime activities.
The Arctic sea ice prediction community has advanced rapidly in the past decade with many new sea ice forecast products and services that are targeted for different user groups. However, it is still unclear how well end users are able to utilize these products and services into their planning. There is a need for better engagement with a broad range of Arctic stakeholders and a need to tailor new products and services to end user-specific requirements. Towards this end a workshop is being hosted at Arctic Frontiers in Tromso (January 2018) by PI Stroeve, in collaboration with CliC, EU Polar Net and University of Bergen to bring together sea ice stakeholders and forecasters to:
1) Assess the value of forecasts by the user community.
2) Determine if and how ice forecasts are currently being used in decision making.
2) Communicate the relevant metrics needed by various stakeholders.
3) Identify where improvements in sea ice forecasts would help stakeholders make decisions.
4) Communicate the limits and opportunities of current forecasting systems.
 
Description UCL collaborators funded by NERC held an Arctic Sea Ice Prediction Stakeholders Workshop as a side event at the Arctic Frontiers conference on January 22, 2018 (http://www.climate-cryosphere.org/news/clic-news/1586-workshop-announcement-arctic-sea-ice-prediction-stakeholders-workshop-at-arctic-frontiers-2018). The focus of this workshop was to assess the economic value of sea ice forecasts for stakeholders and end-users working in and around Arctic sea ice. The workshop was designed as an interactive session where researchers and forecast developers shared information on data and resources needed for environmental parameters and recommendations on how the use of forecasts can be improved with suggestions for development and collaborative efforts. The first session of the workshop included presentations from forecasters to describe the capabilities of short, long and seasonal forecasts. In the second session, speakers from polar tourism, fishing, ice information provision and long-term planning (energy extraction and logistics planning) presented on the scales they work on and at what spatial and temporal resolutions they require information. The third session was interactive and consisted of multiple breakout groups where participants discussed gaps in knowledge and provided recommendations to the community as an outcome from the workshop. 

EU Polarnet compiled the workshop in a report (https://www.eupolarnet.eu/fileadmin/user_upload/www.eupolarnet.eu/Members_documents/Deliverables/WP4/D411_
Minutes_of_stakeholder_dialogue_at_Arctic_Frontiers_Conference.pdf).  We also wrote an EOS article to summarize the outcome of this workshop, submitted in October 2018. It is still under review. We then expanded on these outcomes for the Polar Geography Special Issue. Additionally, these results provide a starting point for SIPN2 and other partners to develop training workshops for users and integrate user feedback in future project/proposal ideas.

Efforts are also underway at UCL to improve upon melt pond detection satellites. Our aim is to retrieve Pan-Arctic melt ponds using visible imagery, developed using MODIS data but capable of being applied to any visible satellite imagery. We are focused on developing machine learning approaches such as random forest, support vector machine, and deep neural networks. In order to implement machine learning successfully, a high quality training set is needed. Nicholas and Polashenski (2018) developed a method for classifying melt ponds from WorldView (WV), which are used as a training set for machine learning and validation. Our test case for algorithm development is the year 2015 for which we have downloaded and processed coincident WV and MODIS data.

To blend MODIS and WV, we first have to re-project the MODIS images to match the WV data. This was achieved using ArcGIS to re-project the MODIS from EASE-grid to polarstereography. One identified challenge in matching these images is that temporal differences (in this case 10 minutes) can result in mis-match of individual ice floes. Thus, some nudging is required after reprojection.Since MODIS is an optical sensor, the reflectance is considerably affected by the geometry of the Sun and the sensor. In particular, the sun elevation is lower in the Arctic, which enhances surface reflectance in the forward scattering direction. The use of a band ratio is able to minimize these effects. As an example, we compared Bidirectional Reflectance Distribution Functions (BRDFs), as well as anisotropically-corrected individual band reflectances and band ratios for several surface types: melt ponds, bare ice, and snow-covered ice. These were compared to in-situ BRDFs collected near Barrow and Resolute Alaska by Don Perovich. At least two different days (i.e., with different geometry of the Sun and sensor) are included in each surface type class. The band ratios are more stable than the anisotropically-corrected individual band reflectances (Fig. 2). Therefore, we will next focus on using band ratios for machine learning as a training data set. Various band ratios for melt ponds and sea ice will be developed by considering spectral characteristics for each surface type.
Exploitation Route We developed new data sets and insights that can be used for data assimilation and testing of sea ice forecasting models.
Sectors Environment,Transport

URL http://arcus.org/sipn2
 
Description Work under this project forms part of a large network of scientists, stakeholders and the general public. This Sea Ice Prediction Network engages in several activities both in the US and in the UK and elsewhere. Forecasts of summer sea ice conditions are disseminated via the Sea Ice Outlook website hosted at arcus.org. https://www.arcus.org/sipn/sea-ice-outlook. Since this project is a joint NSF/NERC project we all work together to host workshops and webinars. A key outcome during the first year of this project was a workshop held at Arctic Frontiers in Tromso, where we brought sea ice forecasters together with various representatives form the shipping and oil/gas industries to discuss how best we can make forecasts that stakeholders can use. What came as a surprise was that after the sea ice forecasters spoke, the stakeholders that followed stated that they didn't understand anything that the scientists presented. Thus, we discovered there is a serious language barrier between the two groups of people and as such, stakeholders currently do not use any of the forecasts provided by scientists in their decision making. This was a sobering realization but is helping the network to better understand what is needed in order to have our sea ice forecasts be of use to various stakeholders. We additionally produced the first 2000 to 2020 melt pond data set from the MODIS instrument and have made this available to the science community. This data set can be useful for testing the relationships between melt onset and end of summer sea ice extent.
Sector Environment,Transport
Impact Types Societal,Economic,Policy & public services

 
Title Arctic Melt Pond Fraction and Binary Classification, 2000-2020 
Description This dataset provides Arctic monthly mean melt pond fractions and binary classification, from 2000-06-01 to 2020-08-31. Level-2 MODerate resolution Imaging Spectroradiometer (MODIS) top-of-the-atmosphere (TOA) reflectances for bands 1-4 were obtained, to which two machine learning algorithms such as multi-layer neural networks and logistic regression were applied to map melt pond fraction and binary melt pond/ice classification. This work was funded by NERC standard grant NE/R017123/1. 
Type Of Material Database/Collection of data 
Year Produced 2020 
Provided To Others? Yes  
Impact The longest melt pond data set available for Arctic sea ice 
URL https://data.bas.ac.uk/metadata.php?id=GB/NERC/BAS/PDC/01444
 
Description Collaborations with the broader SIPN network 
Organisation University of Alaska Fairbanks
Country United States 
Sector Academic/University 
PI Contribution This work is in collaboration with US researchers funded by the national science foundation to improve our ability to seasonally forecast sea ice conditions. My group has lead data set production and statistical sea ice forecasting approaches as well as participated in AGU townhalls, seminars, and writing of the monthly sea ice forecasting summaries.
Collaborator Contribution All collaborators contribute to the monthly summer sea ice analysis and year-end summary as well as lead independent research projects. Monthly project team meetings are held with all collaborators.
Impact New melt pond data set archived through UKRI. For a full list of all publications associated with this grant and its partnerships see here: https://www.arcus.org/sipn/presentations-and-publications
Start Year 2019
 
Description Collaborations with the broader SIPN network 
Organisation University of Colorado Boulder
Country United States 
Sector Academic/University 
PI Contribution This work is in collaboration with US researchers funded by the national science foundation to improve our ability to seasonally forecast sea ice conditions. My group has lead data set production and statistical sea ice forecasting approaches as well as participated in AGU townhalls, seminars, and writing of the monthly sea ice forecasting summaries.
Collaborator Contribution All collaborators contribute to the monthly summer sea ice analysis and year-end summary as well as lead independent research projects. Monthly project team meetings are held with all collaborators.
Impact New melt pond data set archived through UKRI. For a full list of all publications associated with this grant and its partnerships see here: https://www.arcus.org/sipn/presentations-and-publications
Start Year 2019
 
Description Collaborations with the broader SIPN network 
Organisation University of New Hampshire
Country United States 
Sector Academic/University 
PI Contribution This work is in collaboration with US researchers funded by the national science foundation to improve our ability to seasonally forecast sea ice conditions. My group has lead data set production and statistical sea ice forecasting approaches as well as participated in AGU townhalls, seminars, and writing of the monthly sea ice forecasting summaries.
Collaborator Contribution All collaborators contribute to the monthly summer sea ice analysis and year-end summary as well as lead independent research projects. Monthly project team meetings are held with all collaborators.
Impact New melt pond data set archived through UKRI. For a full list of all publications associated with this grant and its partnerships see here: https://www.arcus.org/sipn/presentations-and-publications
Start Year 2019
 
Description Collaborations with the broader SIPN network 
Organisation University of Washington Applied Physics Laboratory
Country United States 
Sector Public 
PI Contribution This work is in collaboration with US researchers funded by the national science foundation to improve our ability to seasonally forecast sea ice conditions. My group has lead data set production and statistical sea ice forecasting approaches as well as participated in AGU townhalls, seminars, and writing of the monthly sea ice forecasting summaries.
Collaborator Contribution All collaborators contribute to the monthly summer sea ice analysis and year-end summary as well as lead independent research projects. Monthly project team meetings are held with all collaborators.
Impact New melt pond data set archived through UKRI. For a full list of all publications associated with this grant and its partnerships see here: https://www.arcus.org/sipn/presentations-and-publications
Start Year 2019
 
Description Stakeholder Workshop 
Form Of Engagement Activity A formal working group, expert panel or dialogue
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Industry/Business
Results and Impact We held a workshop in conjunction with the Annual Arctic Frontiers meeting in Tromso to engage various business representatives that normally attend this conference to engage in fruitful discussions on how scientists can better meet stakeholder needs with short-term to seasonal sea ice forecasts. A key outcome was a language gap between scientists and stakeholders limits the use of current sea ice forecasts by the shipping and extractive industries. Part of the problem is a lack of understanding on the limitations/uncertainties in the forecasts as well as ship captains not being able to interpret complex data. We summarized these issues in an EOS Transactions article and provided some suggestions on how to improve this communication so that forecasts can be better used by the relevant stakeholders.
Year(s) Of Engagement Activity 2018