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The INTAROS Roadmap describes the way forward to improve and sustain the observing capacity in the Arctic. The Roadmap addresses the full data delivery chain from observing sensors to data repositories with focus on in situ observations. The document describes key factors determining how well an observing system can function in the Arctic, involving technological advances, infrastructure and data networks. Furthermore, the document emphasize the importance of cross disciplinary collaboration and stakeholder engagement as part of the data delivery chain. The development of in situ observing systems in the Arctic, especially ocean-based observations in the ice-covered regions, depends heavily on mature technology, transport infrastructures and logistical services allowing personnel to access the areas. Deployment and operation of observing platforms require use of icebreakers, aircraft, manned ice stations and automated systems that can operate year-round Based on the experience and knowledge of the INTAROS consortium, the following recommendations are formulated: The importance of in situ observations must be promoted as the backbone for building knowledge about climate and environmental change in the Arctic, at the same level as satellite observations and modelling systems The funding mechanisms for in situ observing systems need to be strengthened and coordinated between programmes, projects and institutions involved in Arctic observation, including local communities The Joint Statement of Ministers (ASM 2021), signed by 25 countries and six Indigenous Peoples organizations, states that they agree to strengthen cooperation on implementing Arctic observing and data sharing, implying that they need to allocate resources for in situ measurements contributing to the observing systems. Technology development for more robust and reliable in situ observing systems is needed. Here, major industry actors can play a role by investing in platforms and sensors that can operate autonomously in the Arctic The data delivery chain from in situ observing systems must be operationalised for each of the discipline-oriented systems in order to facilitate data sharing. This requires collaboration between the research communities, data services and other actors involved in the delivery chain. Collaboration can be enhanced by setting up mediators who can communicate between the actors Observing systems must be adapted to evolving priorities, requirements, and technological developments. This requires regular dialogue with researchers, stakeholders in private and public sector, researchers, service providers, local communities and Indigenous rightholders in the Arctic. Competence building need to be strengthened in observing methods, technologies, and procedures across gender and generations. The INTAROS roadmap builds on the experience and knowledge from the INTAROS consortium comprising more than 300 scientists from 49 institutions in Europe, Asia, and North America. In addition the document builds on discussions with representatives of Indigenous and local communities, private and public stakeholders, scientists and service providers at more than 50 workshops organised by the INTAROS project.

This report gives an overview of the activities, results and impacts of INTAROS Work Package 6 (WP6). The aim of WP6 is to demonstrate how an integrated observation system can be of specific benefit for society at local, regional or pan-Arctic scale. Through WP6 we show the capability of an enhanced Arctic Observation System towards advancing the economic role of the Arctic by providing support for better-documented processes and better-informed decisions within key sectors such as shipping, petroleum, fishing, and tourism. Further, WP6 demonstrates how the Arctic Observation system may be applied to further develop the accuracy of climate models, improve the understanding of biogeochemical cycles and ecosystem functioning, enhance fisheries and environmental management, increase the level of preparedness towards natural hazards, and develop better management and decision making concepts for selected local communities. Through WP6 INTAROS demonstrates enhanced data search and retrieval, assimilation into models, validation of estimated and projected climate parameters, scientific analysis, decision-support and policy-making. Following a general introduction to INTAROS WP6, eight chapters summarize, for each topic covered, the main activities and results, data and models used, stakeholder/user benefits, and further development and exploitation of results. The topics span broadly and target very different end-user groups but they all share the same overall challenge: how to synthesize data across time and space from different sources, formats and scientific disciplines into aggregated synoptic data products that are relevant for end-users. The following topics are addressed: Improving skill of model predictions in the Arctic, Applying observations and models for environmental and fisheries management, Ice-ocean statistics, Remote sensing applications, Natural hazards in the Arctic, Greenhouse gas exchange in the Arctic, Case studies of community-based observing systems and Benefits of ocean observing for blue growth in the Arctic. The report then describes concrete showcases, software applications. Selected main results across WP6 are then presented, before the report ends with conclusions and perspectives.

The INTAROS project develops an integrated Arctic Observation System (iAOS) by extending, improving and unifying existing systems in the different regions of the Arctic. Within INTAROS, WP5 (Data integration and management) develops the iAOS Portal, which provides access to selected datasets and services offered by INTAROS partners and by other Arctic data providers and service providers. This document describes the major functionality and features of this portal and how it has been implemented. Required functionality has been identified through dialogue with users and stakeholders. The approach has been to use agile development techniques to quickly refine user needs and implement new features iteratively. Two main categories of users of the portal were identified: (1) data/service providers and (2) data/services consumers. Providers make their data or services available through the portal, and update the descriptions of these if changes are made. Consumers search for and access data or services, with sufficient descriptions (metadata) for both data and services to whether to use them in their work. The population and promotion of the iAOS Portal equally depends on data providers and consumers including service providers. The iAOS Portal has been implemented using a leading data management system framework named CKAN (Comprehensive Knowledge Archive Network). CKAN offers a broad range of the needed features out of the box and has an active community that develops additional features (plugins) that can be added to the iAOS Portal as needed. Using the CKAN core components and mature plugins, we have implemented the following main components of the iAOS Portal: INTAROS Data Catalogue, holding descriptions (metadata) of datasets collected or generated with support from INTAROS. iAOS Portal Data Catalogue, holding metadata harvested from other catalogues offering relevant dataset for the applications under development in WP 6 of INTAROS. Services and Applications, describing services and applications developed in INTAROS and by other service providers offering products or functionality that can be exploited in WP 6. Tools and Resources, providing links to software libraries and tools, computer platforms, data systems, infrastructures and projects that offer data, information and/or software tools that can be useful for scientists and stakeholders in Arctic sciences and for local communities in the region. The ARCMAP web application for mapping and assessing Arctic in situ observing systems is available through the portal. ARCMAP that was developed in a spin-off project is also part of the iAOS Portal. The iAOS Portal will be extended with more features, data and services during the course of the INTAROS project as well as in future projects following up INTAROS and the recommendations from the INTAROS Roadmap. These projects will use the iAOS Portal as a mechanism to make their data and services known to the large network of Arctic scientists and local communities established within INTAROS. In this way, the iAOS Portal will become an important element in a Sustained integrated Arctic Observing System. The IAOS Portal is available at https://portal-intaros.nersc.no/.

For sea level studies, coastal adaptation, and planning for future sea level scenarios, regional responses require regionally-tailored sea level information. Global sea level products from satellite altimeters are now available through the European Space Agency’s (ESA) Climate Change Initiative. However, these global datasets are not entirely appropriate for supporting regional actions. For the Baltic Sea region, complications such as coastal complexity and sea-ice restrain our ability to exploit altimetry data opportunities. This presentation highlights the opportunities offered by such regionalised advances, through an examination by the ESA-funded Baltic SEAL project (http://balticseal.eu/). We present the challenges faced, and solutions implemented, to develop new dedicated along-track and gridded sea level datasets for Baltic Sea stakeholders, spanning the years 1995-2019. Advances in waveform classification and altimetry echo-fitting, expansion of echo-fitting to a wide range of altimetry missions, and multi-mission cross calibration, enabled all mission data to be integrated into a final gridded product. This gridded product provides new insights into the Baltic Sea’s mean sea level and its variability to be gathered. A Mean Sea Surface dataset was developed, in addition to an analysis of sea level trends in the region (using both tide gauge and altimetry data). The Baltic SEAL absolute sea level trend at the coast better aligns with information from the in-situ stations, when compared to current global products. A pronounced sea level trend gradient which increases towards the North-East was found. A proportion of the SL trend gradient can be directly linked to enhanced southerly wind forcing and associated Ekman transport towards the Bothnian Bay. The spatial and temporal density of the data allows for a robust comparison between the sea level time series and relevant climate indices such as the North Atlantic Oscillation, with implications for regionalising global climate change impacts. These investigations highlight the potential of regionalised products for the Baltic Sea region, and beyond, to study regional sea level variability. Figure Caption: Panel a: correlation of the NAO index with SLA from gridded altimetry. Panel b: normalised time series of NAO index (green) and SLA difference between Bay of Bothnia (northernmost sub-basin) and south-west sub-basins (orange). Each point represents the time average of the quantities of the winter months December, January and February.

Satellite altimetry is now a well-established method to measure water level changes in lakes and rivers to understand climate change, for water resource management, and security. The water level in lakes can generally be estimated with a relative RMSE of 10-20 cm when compared to in situ data. Contrary to what we expect, the RMSE is sometimes higher for larger lakes, not because of a poorer performance of the altimeters but due to a spatial signal in the elevation relative to the vertical reference potentially from un-modeled geoid signals, wind, or other static fields. Hence, when reconstructing the water level time series over large lakes, tracks crossing at different locations, are affected by a different contribution in elevation. This is especially problematic for geodetic missions like CryoSat-2 and SARAL/AltiKa, but also if more than one track is applied for the repeat missions. To reduce the error due to a spatially distributed elevation signal on large lakes we suggest setting up a model where the spatial and temporal signal is estimated simultaneously. This is done in a state-space model where the spatial part is modeled via a Gaussian Markov Random Field and the temporal part is modeled via a random walk. To separate the spatial and temporal signals require a good coverage in space and time which is achieved with ICESat-2. A further advantage of ICESat-2 is the high precision that enables us to detect smaller variations. Hence, we can use the highly precise ICESat-2 data to construct correction grids to account for the spatially distributed elevation signal. Such grids can be used to correct the other missions like CryoSat-2 and Sentinel-2 to improve the reconstructed water level time series. We show examples from the lakes Tanganyika, Lake Malawi, Titicaca, Baikal, Issykul, and others. We also demonstrate how the reconstructed water level time series based on other missions like CryoSat-2 and Sentinel-3 is improved when using the ICESat-2 based correction grids.