Large quantities of carbon are stored in Yedoma permafrost. When temperatures rise, its high ice content is a catalyst for rapid degradation, which in turn may cause the release of large quantities of carbon. 40 % to 70 % of the radiative forcing from this release is expected to be in the form of CH₄. In this observing system simulation experiment, we examined the capabilities of three atmospheric GHG monitoring platforms i.e. tall towers, and the TROPOMI and MERLIN satellite instruments, to detect changes in CH₄ release from increased Yedoma thaw. A set of environments are simulated with the GEOS-5 model: one representing a “natural” emission case as the reference, a second featuring enhanced CH₄ release from Yedoma soils. From within these modelled environments, synthetic measurements are generated following best in situ practices and realistic error characterizations.

For the satellites we find the lowest detection limits when aggregating measurements over a 112 d period, at Yedoma fluxes of 144 % to 367 % of current conditions. These factors are up to 1.2 times higher when taking transport modelling uncertainties into account. The tall tower network shows a wide range of detection lower limits, the lowest at only 107 % of current fluxes, but has considerably higher lower detection limits when factoring in transport modelling errors. Overall, the individual systems appear to lack the ability to detect and attribute small changes in Yedoma CH₄ fluxes, and would either need to be used in combination or require a considerable time to detect changes under higher emission scenarios.

Ecosystems at high latitudes are changing rapidly in response to climate change. To understand changes in carbon fluxes across seasonal to multi-decadal timescales, long-term in situ measurements from eddy covariance networks are needed. However, there are large spatiotemporal gaps in the high-latitude eddy covariance network. Here we used the relative extrapolation error index in machine learning-based upscaled gross primary production as a measure of network representativeness and as the basis for a network optimization. We show that the relative extrapolation error index has steadily decreased from 2001 to 2020, suggesting diminishing upscaling errors. In experiments where we limit site activity by either setting a maximum duration or by ending measurements at a fixed time those errors increase significantly, in some cases setting the network status back more than a decade. Our experiments also show that with equal site activity across different theoretical network setups, a more spread out design with shorter-term measurements functions better in terms of larger-scale representativeness than a network with fewer long-term towers. We developed a method to select optimized site additions for a network extension, which blends an objective modeling approach with expert knowledge. This method greatly outperforms an unguided network extension and can compensate for suboptimal human choices. For the Canadian Arctic we show several optimization scenarios and find that especially the Canadian high Arctic and north east tundra benefit greatly from addition sites. Overall, it is important to keep sites active and where possible make the extra investment to survey new strategic locations.