Long‐running eddy covariance flux towers provide insights into how the terrestrial carbon cycle operates over multiple timescales. Here, we evaluated variation in net ecosystem exchange (NEE) of carbon dioxide (CO₂) across the Chequamegon Ecosystem‐Atmosphere Study AmeriFlux core site cluster in the upper Great Lakes region of the USA from 1997 to 2020. The tower network included two mature hardwood forests with differing management regimes (US‐WCr and US‐Syv), two fen wetlands with varying levels of canopy sheltering and vegetation (US‐Los and US‐ALQ), and a very tall (400 m) landscape‐level tower (US‐PFa). Together, they provided over 70 site‐years of observations. The 19‐tower Chequamegon Heterogenous Ecosystem Energy‐balance Study Enabled by a High‐density Extensive Array of Detectors 2019 campaign centered around US‐PFa provided additional information on the spatial variation of NEE. Decadal variability was present in all long‐term sites, but cross‐site coherence in interannual NEE in the earlier part of the record became weaker with time as non‐climatic factors such as local disturbances likely dominated flux time series. Average decadal NEE at the tall tower transitioned from carbon source to sink to near neutral over 24 years. Respiration had a greater effect than photosynthesis on driving variations in NEE at all sites. Declining snowfall offset potential increases in assimilation from warmer springs, as less‐insulated soils delayed start of spring green‐up. Higher CO₂ increased maximum net assimilation parameters but not total gross primary productivity. Stand‐scale sites were larger net sinks than the landscape tower. Clustered, long‐term carbon flux observations provide value for understanding the diverse links between carbon and climate and the challenges of upscaling these responses across space.

Livestock agriculture accounts for ∼15% of global anthropogenic greenhouse gas (GHG) emissions. Recently, natural climate solutions (NCS) have been identified to mitigate farm‐scale GHG emissions. Nevertheless, their impacts are difficult to quantify due to farm spatial heterogeneity and effort required to measure changes in carbon stocks. Remote sensing (RS) models are difficult to parameterize for heterogeneous agricultural landscapes. Eddy covariance (EC) in combination with novel techniques that quantitatively match source area variations could help update such vegetation‐specific parameters while accounting for pronounced heterogeneity. We evaluate a plant physiological parameter, the maximum quantum yield (MQY), which is commonly used to calculate gross and net primary productivity in RS applications. RS models often rely on spatially invariable MQY, which leads to inconsistencies between RS and EC models. We evaluate if EC data improve RS models by updating crop specific MQYs to quantify agricultural GHG mitigation potentials. We assessed how farm harvest compared to annual sums of (a) RS without improvements, (b) EC results, and (c) EC‐RS models. We then estimated emissions to calculate the annual GHG balance, including mitigation through plant carbon uptake. Our results indicate that EC‐RS models significantly improved the prediction of crop yields. The EC model captures diurnal variations in carbon dynamics in contrast to RS models based on input limitations. A net zero GHG balance indicated that perennial vegetation mitigated over 60% of emissions while comprising 40% of the landscape. We conclude that the combination of RS and EC can improve the quantification of NCS in agroecosystems.

Drought can affect forest structure and function at various spatial and temporal scales. Forest response and recovery from drought may be a result of position within landscape. Longleaf pine forests in the United States have been observed to reduce their carbon sequestration capacity during drought. We collected eddy covariance data at the ends of an edaphic longleaf pine gradient (xeric and mesic sites) over seven years; two years of normal rainfall were followed by 2.5 years of drought, then 2.5 years of normal or slightly above-average rainfall. Drought played a significant role in reducing the physiological capacity of the sites and was compounded when prescribed fire occurred during the same periods. The mesic site has a 40% greater basal area then the xeric site, which accounts for its larger sequestration capacity; however, both sites show the same range of variance in fluxes over the course of the study. Following drought, both sites became carbon sinks. However, the xeric site had a longer carry-over effect and never returned to pre-drought function. Although this study encompassed seven years, we argue that longer studies with greater spatial variance must be undertaken to develop a more comprehensive understanding of forest response to changing climate.

Dairy farms are predominantly carbon sources, due to high livestock emissions from enteric fermentation and manure. Integrated crop–livestock systems (ICLSs) have the potential to offset these greenhouse gas (GHG) emissions, as recycling products within the farm boundaries is prioritized. Here, we quantify seasonal and annual greenhouse gas budgets of an ICLS dairy farm in Wisconsin USA using satellite remote sensing to estimate vegetation net primary productivity (NPP) and Intergovernmental Panel on Climate Change (IPCC) guidelines to calculate farm emissions. Remotely sensed annual vegetation NPP correlated well with farm harvest NPP (R² = 0.9). As a whole, the farm was a large carbon sink, owing to natural vegetation carbon sinks and harvest products staying within the farm boundaries. Dairy cows accounted for 80% of all emissions as their feed intake dominated farm feed supply. Manure emissions (15%) were low because manure spreading was frequent throughout the year. In combination with soil conservation practices, ICLS farming provides a sustainable means of producing nutritionally valuable food while contributing to sequestration of atmospheric CO₂. Here, we introduce a simple and cost-efficient way to quantify whole-farm GHG budgets, which can be used by farmers to understand their carbon footprint, and therefore may encourage management strategies to improve agricultural sustainability.

Climate change is intensifying the hydrologic cycle and altering ecosystem function, including water flux to the atmosphere through evapotranspiration (ET). ET is made up of evaporation (E) via non‐stomatal surfaces, and transpiration (T) through plant stomata which are impacted by global changes in different ways. E and T are difficult to measure independently at the ecosystem scale, especially across multiple sites that represent different land use and land management strategies. To address this gap in understanding, we applied flux variance similarity (FVS) to quantify how E and T differ across 13 different ecosystems measured using eddy covariance in a 10 × 10 km area from the CHEESEHEAD19 experiment in northern Wisconsin, USA. The study sites included eight forests with a large deciduous broadleaf component, three evergreen needleleaf forests, and two wetlands. Average T/ET for the study period averaged nearly 52% in forested sites and 45% in wetlands, with larger values after excluding periods following rain events when evaporation from canopy interception may be expected. A dominance analysis revealed that environmental variables explained on average 69% of the variance of half‐hourly T, which decreased from summer to autumn. Deciduous and evergreen forests showed similar E trajectories over time despite differences in vegetation phenology, and vapor pressure deficit explained some 13% of the variance E in wetlands but only 5% or less in forests. Retrieval of E and T within a dense network of flux towers lends confidence that FVS is a promising approach for comparing ecosystem hydrology across multiple sites to improve our process‐based understanding of ecosystem water fluxes.