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Abstract Methane dynamics within salt marshes are complex because vegetation types, temperature, oscillating water levels, and changes in salinity and redox conditions influence CH₄production, consumption, oxidation, and emissions. These non‐linear and complex interactions among variables affect the traditionally expected functional relationships and present challenges for interpreting and developing process‐based models. We employed empirical dynamic modeling (EDM) and convergent cross mapping (CCM) as a novel approach for characterizing seasonal/multiday and diurnal CH₄dynamics by inferring causal variables, lags, and interconnections among multiple biophysical variables within a temperate salt marsh using 5 years of eddy covariance data. EDM/CCM is a nonparametric approach capable of quantifying the coupling between variables while determining time scales where variable interactions are the most relevant. We found that gross primary productivity, tidal creek dissolved oxygen, and temperature were important for seasonal/multiday dynamics (rho = 0.73–0.80), while water level was most important for diurnal dynamics during both the growing and dormancy phenoperiods (rho = 0.72 and 0.56, respectively). Lags for the top‐ranked variables (i.e., gross primary productivity, dissolved oxygen, temperature, water level) occurred between 1 and 5 weeks at the seasonal scale and 1–24 hr at the diurnal scale. The EDM had high prediction capabilities for intra‐/inter‐seasonal patterns and annual CH₄sums but had limitations in representing large, infrequent fluxes. Results highlight the importance of non‐linearity, drivers, lag times, and interconnections among multiple biophysical variables that regulate CH₄fluxes in tidal wetlands. This research introduces a novel approach to examining CH₄fluxes, which will aid in evaluating current paradigms in wetlands and other ecosystems. 

Environmental observation networks, such as AmeriFlux, are foundational for monitoring ecosystem response to climate change, management practices, and natural disturbances; however, their effectiveness depends on their representativeness for the regions or continents. We proposed an empirical, time series approach to quantify the similarity of ecosystem fluxes across AmeriFlux sites. We extracted the diel and seasonal characteristics (i.e., amplitudes, phases) from carbon dioxide, water vapor, energy, and momentum fluxes, which reflect the effects of climate, plant phenology, and ecophysiology on the observations, and explored the potential aggregations of AmeriFlux sites through hierarchical clustering. While net radiation and temperature showed latitudinal clustering as expected, flux variables revealed a more uneven clustering with many small (number of sites < 5), unique groups and a few large (> 100) to intermediate (15–70) groups, highlighting the significant ecological regulations of ecosystem fluxes. Many identified unique groups were from under-sampled ecoregions and biome types of the International Geosphere-Biosphere Programme (IGBP), with distinct flux dynamics compared to the rest of the network. At the finer spatial scale, local topography, disturbance, management, edaphic, and hydrological regimes further enlarge the difference in flux dynamics within the groups. Nonetheless, our clustering approach is a data-driven method to interpret the AmeriFlux network, informing future cross-site syntheses, upscaling, and model-data benchmarking research. Finally, we highlighted the unique and underrepresented sites in the AmeriFlux network, which were found mainly in Hawaii and Latin America, mountains, and at under-sampled IGBP types (e.g., urban, open water), motivating the incorporation of new/unregistered sites from these groups.