S. Starkweather1,2, M. B. Armstrong3, and H. Shapiro4
1Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USA
2Physical Sciences Laboratory, NOAA, Boulder, CO, USA
3Sea Grant Knauss Fellowship, Arctic Research Program, Global Ocean Monitoring and Observing Program, NOAA, Silver Spring, MD, USA
4UIC Science, Utqiaġvik, AK, USA
Headlines
- Internationally coordinated Arctic observing infrastructure and data-sharing capabilities deliver critical information that supports decision-making from local to global scales, including through contributions to scientific assessments such as the Arctic Report Card.
- Persistent gaps in Arctic observing systems limit our ability to fully understand and respond to changing conditions. For example, sparse precipitation gauges in complex terrain cause underestimates, hindering assessments of how shifting precipitation affects river discharge and complicating management strategies.
- The resilience of the internationally supported Arctic Observing Network depends on sustaining investments, protecting specialized expertise, and ensuring continuity of both satellite and in situ observing systems and the high-quality data products derived from them.
Tracking Arctic change with the Arctic Observing Network
For 20 years, the Arctic Report Card (ARC) has vividly depicted the profound transformations of Arctic systems and their far-reaching consequences for societies in the region and beyond (see ARC 2025 Executive Summary). Accurate and timely observations of these rapid, system-wide changes are essential, making the internationally coordinated Arctic Observing Network (AON) a crucial foundation for assessments such as the ARC. This essay for the 20th anniversary Report Card provides an updated assessment of the AON, focusing on its strengths, persistent gaps, and risks in supporting scientific assessments typified by the ARC.
The AON refers to an international system of observing, data collection, and sharing, independently supported by a diverse range of actors, including governments, universities, regional organizations, and communities. The concept was conceived to collectively confront the significant challenges that conventional monitoring systems face in the Arctic, with the goal of better “linking and supplementing” diverse efforts through coordination, integration, and strategic investment (NRC 2006). The international Sustaining Arctic Observing Networks (SAON) initiative was established in 2011 to facilitate AON’s progress by establishing inventories of relevant assets, enabling data sharing, and collectively addressing gaps.
In addition to the need for better integration, collective challenges for SAON’s partners include sparse ground-based observations, unreliable infrastructure, and limited telecommunications-factors that hinder in situ data collection and timely access (Cherry and Delamere 2025; NSTC 2022). Satellites play an essential role in providing services such as weather forecasting, wildfire detection, coastal mapping, and supporting scientific assessments that inform management and adaptation actions. However, they also face limitations: geostationary coverage degrades above 60° N, frequent cloud cover and limited daylight mask many signals, and, in some cases, resolution is inadequate in space and/or time (Cherry and Delamere 2025).
Indigenous Knowledge systems, with deep roots tracking change and supporting holistic decision-making for millennia (Daniel 2019), speak to the need for stronger support of community-led monitoring (e.g., Glenn-Borade et al. 2023) and effective co-production of knowledge partnerships (Ellam Yua et al. 2022; Rudolf et al. 2025). Indigenous-led monitoring is an essential contribution to the AON, yet this recognition must be paired with meaningful investment (see essay Weaving the Seen and Unseen) and inclusion of Indigenous leadership in advancing the AON, both of which have been slow to emerge.
In Europe, SAON’s goals have been advanced under the Horizon 2020 program via EU-PolarNet, INTAROS, and Arctic PASSION (see Glossary for all acronyms). In the U.S., SAON’s goals are advanced through the U.S. AON initiative, which coordinates across multiple federal agencies and non-federal partners to improve observing and data systems. Using its BENEFIT framework and software tool (Marowitz et al. 2025)—Benefit Evaluation, Network Exploration, Find gaps, Improve Together—the initiative assesses societal benefits, identifies strengths, recommends actions, and documents status. Inspired by systems engineering methods applied to federal observing systems (e.g., U.S. GEO 2024), U.S. AON’s BENEFIT framework has been adapted through Arctic-centered, grassroots, and internationally coordinated methods. BENEFIT measures AON performance against benchmarks specified in SAON’s International Arctic Observing Assessment Framework (IAOAF; IDA 2017), which organizes key objectives for observing systems into twelve Societal Benefit Areas (SBAs; Box 1) and serves to align SAON’s partners.
Box 1. Societal Benefit Areas of the International Arctic Observing Assessment Framework. SAON’s partners generated this framework that organizes more than 150 key objectives for Arctic observations into the 12 Societal Benefit Areas defined here. This framework acts as an international benchmark for evaluating observation value, enabling a coordinated societal benefit-based approach to advance the AON in support of informed decision-making and adaptation planning in the rapidly changing Arctic.
1. Disaster Preparedness: Preparing for, preventing, mitigating, responding to, and recovering from natural and human-made disasters and emergencies.
2. Environmental Quality: Mitigating impacts on ecosystem functions and biodiversity, monitoring the effects of transboundary pollutants in the Arctic, and managing waste.
3. Food Security: Ensuring food accessibility, availability, sustainability, safety, and exchange networks for Arctic populations and communities.
4. Fundamental Understanding of Arctic Systems: Advancing understanding of Arctic social, economic, and environmental systems to reduce uncertainty and identify drivers.
5. Human Health: Tracking, forecasting, preventing, mitigating, and responding to threats to physical and mental health in Arctic communities.
6. Infrastructure and Operations: Ensuring safe and efficient operation of built and service infrastructure throughout its life cycle in the Arctic.
7. Marine and Coastal Ecosystems and Processes: Understanding, managing, and monitoring marine and coastal resources, ecosystems, and biogeochemical processes.
8. Natural Resources: Supporting sustainable and responsible discovery, extraction, and processing of renewable and non-renewable natural resources and their impacts.
9. Resilient Communities: Sustaining and preserving the vitality, security, and livelihoods of Arctic communities amid changing environmental conditions.
10. Sociocultural Services: Maintaining the environment’s role in cultural life, preserving heritage, integrating knowledge, and understanding socioeconomics.
11. Terrestrial and Freshwater Ecosystems and Processes: Understanding, managing, and monitoring terrestrial and freshwater resources, ecosystems, and biogeochemical processes.
12. Weather and Climate: Improved forecasts, climate projections, and timely warnings for safety, socioeconomic benefits, and informed decision-making.
As AON is a work in progress, there is no authoritative inventory internationally or within the U.S., though relevant AON assets have been outlined in high-level reports (e.g., NSTC 2022; EU-PolarNet 2 2022) and are being assembled under the Registry of Polar Observing Networks initiative. As a proxy for a clear inventory, the U.S. AON engages with AON-relevant programs, projects, and efforts like the ARC to link the performance of observing systems to the societal benefits they provide.
To produce the findings in this essay, ARC authors who contributed during 2021-24 were invited to participate in a BENEFIT assessment of their most recent essays, each of which evaluates a physical, ecological, or integrated indicator. Authors of 15 ARC essays participated in all or part of this assessment of strengths, gaps, and risks to the AON (see Methods and data section).
Strengths: Supporting societal benefits
The strengths of the AON can be understood through its performance and relevance in supporting efforts like the ARC. Overall, participating authors rated AON performance as high (Fig. 1), with a composite score of 76/100, indicating that as of ARC 2024, the network “fully satisfies” requirements for the ARC scientific assessments included in this work. Their criticality ratings underscore the importance of satellite systems, as they provide the spatial and temporal coverage, consistency, and timeliness essential for ARC essays. Ecological indicators were less consistently supported by the AON than physical or integrated indicators. This was particularly true for emerging ecological topics—such as Beavers and Pollinators—whose performance was lower than those tracking species already embedded in regulatory regimes like Geese, Ice Seals, and Caribou, which are closely tracked to inform harvest limits and, in the case of geese, vectors for disease risks like avian influenza.
Essays tracking physical components of the Arctic system (Fig. 2) provided robust support for scientific understanding [SBAs 4, 7, 11]. These essays also showed high relevance toward the management-relevant key objectives within the Weather and Climate [12] and Environmental Quality [2] SBAs, particularly in advancing our understanding of the changing ecosystem characteristics of snow cover, sea ice, and land ice. Gene Petrescu of NOAA’s Arctic Test Bed summarizes the ARC impact on regional operations as “putting the weather and ice conditions of the past year into context for climate-related messaging.”
Essays tracking ecological change (Fig. 3) demonstrated strong support for scientific understanding [SBAs 4, 7, 11], while also showing strong relevance for regionally critical issues, including Food Security [3], through providing timely updates on subsistence species, their food chains, and ecosystem health.
While this assessment included only one topic integrating social and ecosystem change (Fig. 1)—Marine Ship Traffic, it suggests that integrated indicators extend the societal relevance of the AON and the ARC [SBAs 2, 6, 7].
Gaps: Understanding the impact of data limitations
To understand AON limitations, ARC essay authors assessed the observing systems and data products underlying ARC essays. These systems and products were rated against ideal performance, and where ratings fell short, authors described underlying gaps (Table 1). Gaps may occur at any stage in the AON value chain, including data collection or processing, and manifest in derived products such as reanalyses. Gaps reflect the difference between current and desired capabilities. This analysis synthesizes gaps across physical, ecological, and integrated essays to inform future efforts.
| Indicator Type | Essay Topic | Gaps |
|---|---|---|
| Physical Indicators | Sea Ice | Need for more small-scale observations and better sampling of properties (e.g., impurities in snow and ice), alongside improved satellite data resolution and coverage, especially near coasts and for melt ponds. Reducing uncertainties in thickness estimations is crucial, particularly regarding snow cover and the availability of summer retrievals. |
| Greenland Ice Sheet | Low spatial resolution at the ice sheet edge, significant uncertainties in coastal ice sheet elevation, and limited site variation for in situ data collection. | |
| Precipitation | Much of the Arctic terrestrial drainage remains ungauged, with a lack of coverage in high elevations and other data-sparse regions, alongside a sharp decline in reporting gauges over recent decades. The sparse network is prone to precipitation undercatch errors that are difficult to correct, and short-term forecasts from reanalyses like ERA5 remain uncertain, particularly over high latitudes. Key datasets such as GPCC lack marine coverage, and while integration with reanalysis or satellite products (e.g., MSWEP) could improve monitoring, high-latitude ocean areas have yet to be properly evaluated. | |
| Sea Surface Temperature | Subsurface ocean temperature data are incomplete. Spatial resolution of satellite-derived SST is limited, especially in coastal regions where SST variability is significant. | |
| Terrestrial Snow Cover | Need for higher resolution in higher latitudes and mountain regions for global snow water equivalent products. Larger uncertainty in snow cover extent trends during fall and winter due to coarse resolution, difficulty distinguishing snow from clouds, and inconsistent temporal coverage has resulted in limited analysis for these seasons. | |
| Surface Air Temperature | Sparse station coverage and relatively few weather stations add uncertainty to the early portion of the record for GHCNm data. Similarly, sparse surface air temperature observations in the Arctic before the satellite era increase uncertainty in the early years of the ERA5 reanalysis record. | |
| Lake Ice | Data from in situ ice sensors have good coverage but are still limited. GMASI (Autosnow Ice) data are useless in winter but adds value in summer, and is useful for detecting snow under warm clouds in spring where surface stations are unavailable. | |
| Ecological Indicators | Tundra Greenness | The metric of tundra greenness used in the ARC is the Normalized Difference Vegetation Index (NDVI), a spectral metric that is linked to the abundance of green vegetation. While this metric has a long history of use and provides a period of record exceeding 40 years, it is a spectral proxy and is subject to some limitations. Alternative metrics, like ground-based metrics, exist that could eventually provide more robust assessments of Arctic change, but these metrics remain in their infancy and do not have a sufficient period of record to provide a meaningful “big picture” of conditions and change across the Arctic tundra biome. |
| Ocean Primary Productivity | Satellite estimates exclude sea ice algae and under-ice phytoplankton blooms. They can also underestimate primary productivity under highly stratified conditions. There also remain challenges in distinguishing between clouds, snow, and ice in polar regions with AVHRR data, leading to high uncertainty in surface parameter retrieval. Inadequate spatial density of ship, Argo, and buoy observations affects the quality and comprehensiveness of data in those areas. | |
| Geese | Visual estimates often underestimate and sample only a part of a species’ range. Research is ongoing to expand banding and apply aerial imagery surveys to more species and populations, but improvements are still needed in image analysis to reduce manual effort. | |
| Pollinators | Need for long-term monitoring in various Arctic locations, along with improved support and technologies for identifying insect material to the species level. Additionally, there are spatial and temporal gaps in monitoring, as well as gaps in data for certain taxa, like flies, and in understanding responses to stressors. | |
| Integrated Indicators | Ship Traffic | Deficiencies in the comprehensiveness of antennae available to generate AIS signals and the omission of certain ships and ship-types from AIS tracking make the dataset incomplete. |
Within physical indicators, common gaps include limited spatial resolution, particularly in coastal zones and mountainous areas, and a lack of in situ data needed to validate satellite products. For example, Precipitation essay authors indicated a lack of gauge coverage in higher elevations and other data-sparse regions, which can result in precipitation undercatch errors. Expanding gauge coverage would improve understanding of how changing precipitation patterns affect river discharge, flood events, and other consequential metrics.
Among ecological and integrated indicators, common gaps include reliance on proxy or indirect metrics, limited spatial and temporal coverage, and sparse in situ data. For example, species monitoring (e.g., Arctic Geese and Pollinators) often underrepresents full ranges due to limited geographic coverage and reliance on visual estimates. Similarly, Tundra Greenness estimates remain constrained by the limited spatial coverage of field measurements needed to validate satellite observations. Ocean Primary Productivity estimates are limited to areas with less than 10% of sea ice concentration, excluding primary production from sea ice algae or under-ice phytoplankton blooms, and are further constrained by sparse in situ observations.
Despite clear strengths, persistent gaps limit the AON’s ability to fully support Arctic assessments and decision-making, particularly at finer scales. Closing gaps would improve confidence in key indicators of Arctic change, enabling a shift from broad regional assessments toward finer scale, more locally relevant insights that help Arctic communities understand changing conditions, support safer and more effective operations for commercial and industrial sectors such as fisheries, and improve global weather, climate, and ecosystem predictions. These limitations point to the need for sustained investment in resolution, coverage, validation, and cross-system integration.
Risks: Sustaining a resilient observing system
ARC essay authors were surveyed about current risks to the AON, recognizing that the future of Arctic observing could include improvements or degradations to capabilities. Many responses noted recent or proposed changes to U.S. federal staffing and budget levels. Of the 31 observing systems in this assessment, 23 are either primarily supported by U.S. federal agencies or jointly supported by U.S. federal agencies and international partners (e.g., the International Arctic Buoy Program). Additionally, U.S. federal agencies create 8 out of 10 assessed datasets that synthesize observations into analysis and reanalysis products. Notably, agencies like NOAA, NASA, NSF, and DOI, which drive data collection and value-added efforts, face significant staff and budget reductions. For example, 4000 NASA staff left between January and July 2025 (Duster 2025).
Because the U.S. contributes significantly to Arctic observing, changes in the federal budget and staffing could impact the AON and products like the ARC. Risks to funding and staffing, alongside aging infrastructure, may compound existing AON gaps, jeopardizing long-term trend analyses and undermining decision-making. For physical indicators, these include potential reductions in satellite missions, data processing efforts, and in situ measurements (Table 2). Ecological indicators face many of the same potential impacts (e.g., see essay Tundra Greenness). Agency staffing and funding uncertainties have already led to canceled grants and personnel shortages impacting ecological research (Table 2), which has greater in-field requirements than physical research. Arctic fieldwork is 4-10 times more expensive than similar work in lower latitudes (Mallory et al. 2018).
| Indicator Type | Essay Topic | What is at risk and criticality information | Further explanation of risk |
|---|---|---|---|
| Physical Indicators | Surface Air Temperature | No risks noted | – |
| Precipitation | Meteorological Stations Collecting Precipitation Data – a highly critical observing system for the Precipitation essay | Potential impacts as precipitation data are collected or funded by agencies or programs facing major budget cuts | |
| Sea Ice |
CryoSat-2, SMOS, and ICESat-2 – the three primary observing systems contributing to sea ice thickness estimates, of medium to medium-high criticality to the Sea Ice essay. DMSP SSM/I-SSMIS – This is a medium-low criticality input to three datasets, all of which support the Sea Ice essay |
CryoSat-2, SMOS, and ICESat-2 – All past their nominal mission lifetime. Constrained agency budgets and staffing could impact new instrumentation and/or data products development. DMSP SSM/I-SSMIS – This observing system is explicitly identified for decommissioning. It will be replaced by new passive microwave sensors. This transition introduces inconsistencies, which require staff time and expertise to recalibrate the data and make sure it is as consistent as possible with the long-term record. |
|
| Greenland Ice Sheet | DMSP SSM/I-SSMIS – This is a highly critical observing system supporting a single dataset within the Greenland Ice Sheet essay | DMSP SSM/I-SSMIS – See “Sea Ice” description above. | |
| Sea Surface Temperature |
OISST – The highest criticality dataset for the Sea Surface Temperature essay DMSP SSM/I-SSMIS – This is a medium-low criticality observing system for two datasets underlying the Sea Surface Temperature essay |
OISST – The generation and availability of this data product may be affected by the proposed fiscal 2026 budget cuts to U.S. federal agencies. Additionally, the satellite and in situ observing systems that support the OISST may be impacted. DMSP SSM/I-SSMIS – See “Sea Ice” description above. |
|
| Lake Ice | NIC IMS – The highest criticality, and only, dataset for the Lake Ice essay | NIC IMS – Creation of this dataset relies on human analysts, which contributes to its high performance. The generation and availability of this data product may be affected by proposed FY 2026 budget cuts at U.S. federal agencies. | |
| Ecological Indicators | Tundra Greenness |
AVHRR GIMMS-3g – The highest criticality dataset for the Tundra Greenness essay. MODIS – High-criticality observing system for the Tundra Greenness essay. |
AVHRR GIMMS-3g – will not be updated for 2025 or future years due to funding cuts at NASA GSFC. Potential replacement datasets exist, but have shortcomings in this context. MODIS – The sunsetting of MODIS was expected, and a transition to VIIRS has been planned. |
| Ocean Primary Productivity | MODIS – The highest criticality observing system for the Primary Productivity essay. | MODIS – The sunsetting of MODIS was expected, and a transition to VIIRS and PACE has been planned. This transition introduces inconsistencies, which require in situ measurements, staff time, and expertise to make sure it is as consistent as possible with the long-term record. | |
| Geese | All observing systems for goose population status monitoring were noted as at risk. | Risk of losing adequately trained staff, particularly experienced pilots, alongside continued funding reductions. Due to staffing shortages and funding uncertainty, summer 2025 goose monitoring and fieldwork activities were canceled at multiple locations. This follows years of static funding, which results in decreased funds due to inflation. In some proposed FY 2026 budgets, many of these programs, such as the USGS’s Ecosystem Mission Area and Cooperative Research Units and the USFWS’s North American Wetland Conservation Act and Migratory Bird Joint Venture Programs, among others, were slated for either reduced funding or elimination. | |
| Pollinators | CBMP Arctic Pollinator Expert Network, an international collaboration related to Arctic pollinators, is at risk. | Funded by agencies or programs facing major budget cuts. | |
| Beavers | A-BON – an organization created to “coordinate research and action among stakeholders” related to beavers in the circumpolar Arctic, is at risk. | Funding opportunities with the NSF-AON program, which has supported the A-BON, are on pause for the 2025 calendar year (McManus 2025). |
Given the international nature of the AON, changes to any contributing country’s capabilities affect efforts to leverage and coordinate observations across the Arctic. While not all programs may be affected by proposed budget cuts, our assessment shows that potential AON degradations could have broader impacts, for instance, impeding flooding predictions using in situ gauges, icebreaker navigation using sea ice data, or community adaptation plans using ecological data. Additionally, a lack of adequate monitoring information could prevent regulatory bodies from meeting legal mandates (e.g., Arctic Geese). Having robust assessments of how observing programs generate societal benefit by supporting baseline understanding and decision making can help countries and organizations respond strategically to changing investments.
Conclusions
The Arctic Report Card exemplifies the vital societal relevance of sustained, high-quality, internationally coordinated observations of Arctic change. Its annual summaries not only deepen conceptual understanding of a rapidly transforming Arctic region but also provide essential context for informed decision-making at regional, national, and global scales, as illustrated by this year’s societal impact summary featured in the Glaciers and Ice Caps essay.
In the absence of a formal AON inventory, the ARC provides a meaningful way to evaluate relevant aspects of the AON’s fitness; this essay details the strengths, gaps, and risks related to this. Yet, as highlighted in 2020 (Starkweather et al. 2020) and reaffirmed here, the ARC focuses on topics where the AON is already robust enough to support strong scientific analysis. There are other assessment-worthy topics not reflected here that would, if included, likely degrade the AONs overall performance rating for assessments, so the AON performance assessed in regard to these ARC topics should be considered an upper estimate.
The AON concept emerged to link sustained efforts, advance innovation, and foster effective partnerships to ensure that observing systems improve in response to societal needs. Sustaining and advancing the AON will require consistent investment, international collaboration, and continued application of fitness testing tools such as BENEFIT assessment. These efforts are critical to ensure that Arctic observing systems deliver lasting value to science, policy, and society.
Methods and data
ARC essay authors provided structured feedback on the connection between the AON’s strengths, gaps, and risks and their ARC essay. Ratings included assessing connection to SBAs (see Strengths section) as well as listing contributing data sets and their respective observing system inputs. Standard rubrics guided the authors through quantitative ratings of performance (scaled from 0-100, with 76-90 equating to “fully satisfactory”) and criticality; authors also provided rating rationales. All essay authors for the 2021-24 ARC were invited to participate; 15 responded. Some completed all aspects of the assessment, while others responded only to portions of the assessment, and others opted out.
This essay’s authors used Notebook LM (NLM), which is an AI powered by Google’s Gemini 1.5 Pro large language model. NLM uses only the sources provided for synthesis, which included each participating author’s most recent ARC essay and the SBA framework. The query asked NLM to identify up to three of the most relevant SBAs and up to five key objectives per societal benefit area. Each choice was first reviewed by this essay’s authors; ARC essay authors were then invited to review and amend the choices. Several authors amended the initial choices, and others concurred with the AI’s selection.
Assessment data are encoded in the BENEFIT tool and is accessible via [usaon.org].
Acknowledgments
S. Starkweather is grant-funded by NOAA’s Global Ocean Monitoring and Observing Program/Arctic Research Program. M. B. Armstrong’s work is supported by NOAA’s Global Ocean Monitoring and Observing Program/Arctic Research Program and Virginia Sea Grant. H. Shapiro is supported by UIC Science on the IARPC Secretariat team; funding is provided by the National Science Foundation.
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Glossary of acronyms
Observing systems, including satellites and sensors, and data products:
AIS – Automatic Identification System
AVHRR – Advanced Very High Resolution Radiometer
AVHRR GIMMS-3g – Advanced Very High Resolution Radiometer Global Inventory Modeling and Mapping Studies-3rd Generation
CryoSat-2 – Cryospheric Satellite-2
ERA5 – European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis, version 5
DMSP SSMI/SSMIS – Defense Meteorological Satellite Program Special Sensor Microwave/Imager and Special Sensor Microwave Imager/Sounder
GMASI – Global Multisensor Automated satellite-based Snow and Ice Mapping System
GNCNm – Global Historical Climatology Network monthly
GPCC – Global Precipitation Climatology Centre
ICESat-2 – Ice, Cloud, and land Elevation Satellite 2
MODIS – Moderate Resolution Imaging Spectroradiometer
MSWEP – Multi-Source Weighted-Ensemble Precipitation
NIC IMS – National Ice Center Interactive Multisensor Snow and Ice Mapping System
OISST – Optimum Interpolation Sea Surface Temperature
PACE – Plankton, Aerosol, Cloud, ocean Ecosystem (satellite)
SMOS – Soil Moisture and Ocean Salinity (satellite)
SSMIS – Special Sensor Microwave Imager/Sounder
VIIRS – Visible Infrared Imaging Radiometer Suite
Organizations and agencies:
A-BON – Arctic Beaver Observation Network
Arctic PASSION project – Pan-Arctic Observing System of Systems: Implementing Observations for Societal Needs
CBMP – Circumpolar Biodiversity Monitoring Program
EU-PolarNet – European Union PolarNetwork
DOI – Department of the Interior
INTAROS – Integrated Arctic Observation System
NASA – National Aeronautics and Space Administration
NOAA – National Oceanic and Atmospheric Administration
NSF – National Science Foundation
USFWS – United States Fish and Wildlife Service
USGS – United States Geological Survey
November 23, 2025