Dr. Erik SolbergTromsø, NorwayLive

PolarFlux Carbon Observatory

An open ocean-atmosphere carbon flux observatory advancing transparent climate measurement in the high latitudes.

Health & BiotechnologyOpen ScienceCarbon Flux

About this project

PolarFlux presents a strong, science-driven observatory with open-data intent and proven sensor platforms (buoys and gliders) that make high-latitude air–sea CO2 flux measurement feasible and valuable. Key gaps remain in documented QA/QC, formal governance, community engagement, and explicit additionality claims — strengths that support verification exist, but procedural and transparency specifics must be clarified to reach top-tier integrity.

AI summary by the Ozeaon Climate Intelligence System

Why this integrity score?

The OCIS Integrity Report below explains the reasoning behind the headline score — dimension by dimension — and is readable in full here.

Score Reasoning — Dimensions

The headline score is a weighted sum of six integrity dimensions assessed by the Ozeaon Climate Intelligence System.

Scientific Rigor20% weight
85
Uses established autonomous platforms and targets direct air–sea CO2 flux measurement but lacks explicit citation of validation against peer‑reviewed flux methods and uncertainty budgets.
Open Science15% weight
80
Proposal commits to publishing calibrated open datasets which strongly supports openness, but does not detail repositories, metadata standards, or open-code pipelines.
Measurability (dMRV)20% weight
78
Continuous sensor streams from buoys and gliders enable digital MRV but the proposal lacks a complete dMRV pipeline description (QA/QC, uncertainty propagation, traceable provenance).
Additionality & Impact15% weight
70
Provides novel high-latitude coverage that can underpin CO2 removal verification, but the proposal does not demonstrate how it produces climate additionality relative to existing observing networks or policy baselines.
Transparency & Governance15% weight
75
Project intent to publish data is transparent, yet ownership, funding flows, long‑term maintenance responsibilities, and third‑party oversight are not fully specified.
Community & Equity15% weight
70
Technical capacity and open data benefit the broad research community, but there is no description of local stakeholder engagement, benefit sharing, or social safeguards in high-latitude regions.

Strengths

Focused on direct air–sea CO2 flux measurements in under-sampled high latitudes using proven autonomous buoys and gliders.
Commitment to publishing calibrated open datasets — foundational for reproducibility and third‑party verification.
Instrumentation and continuous sampling design are well suited to create high temporal-resolution observations needed for flux estimation and dMRV use cases.

Risks & Gaps

Insufficient detail on QA/QC, calibration protocols, and uncertainty quantification could undermine the credibility of flux estimates for verification.
Lack of explicit governance, funding continuity, and third‑party oversight raises risk to long-term data availability and impartiality.
No clear articulation of additionality: how PolarFlux complements or exceeds existing observing networks (e.g., SOCAT, GO‑SHIP, Argo) and how it leads to verifiable climate benefit.

dMRV Recommendation

Tier

Tier 2 — Operational dMRV

Cadence

Continuous streaming where feasible with quarterly public dataset releases, immediate flagging of critical events (sensor failures) and an annual peer-reviewed synthesis report that includes uncertainty budgets and validation results.

Recommended

Yes

PolarFlux already operates the hardware and intends open data publication, which supports an operational dMRV approach. A Tier 2 recommendation (operational) fits because continuous digital streams enable MRV workflows, but the project must add formal QA/QC, provenance, validation, and governance to meet robust verification requirements for mCDR and regulatory confidence.

Autonomous buoy and glider pCO2/pH/temperature/salinity sensors with regular in-situ calibration using certified reference materialsAutomated QA/QC pipelines with provenance tracking and versioned datasets (e.g., using Git + containerized processing)Flux estimation algorithms (e.g., bulk formulae with gas transfer parameterizations, and where possible, eddy covariance estimates or cross-calibration against eddy fluxes)Validation via shipboard intercomparisons and SOCAT-compatible data submissions

About this project

Generating OCIS summary & integrity report…