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Over the past three decades, assessments of the contemporary global carbon budget consistently report a strong net land carbon sink. Here, we review evidence supporting this paradigm and quantify the differences in global and Northern Hemisphere estimates of the net land sink derived from atmospheric inversion and satellite-derived vegetation biomass time series. Our analysis, combined with additional synthesis, supports a hypothesis that the net land sink is substantially weaker than commonly reported. At a global scale, our estimate of the net land carbon sink is 0.8 ± 0.7 petagrams of carbon per year from 2000 through 2019, nearly a factor of two lower than the Global Carbon Project estimate. With concurrent adjustments to ocean (+8%) and fossil fuel (−6%) fluxes, we develop a budget that partially reconciles key constraints provided by vegetation carbon, the north-south CO₂gradient, and O₂trends. We further outline potential modifications to models to improve agreement with a weaker land sink and describe several approaches for testing the hypothesis. 

ABSTRACT BackgroundClimate‐change‐induced shifts in the timing of leaf emergence during spring have been widely documented and have important ecological consequences. However, mechanistic knowledge regarding what controls the timing of spring leaf emergence is incomplete. Field‐based studies under natural conditions suggest that climate‐warming‐induced decreases in cold temperature accumulation (chilling) have expanded the dormancy duration or reduced the sensitivity of plants to warming temperatures (thermal forcing) during spring, thereby slowing the rate at which the timing of leaf emergence is shifting earlier in response to ongoing climate change. However, recent studies have argued that the apparent reductions in temperature sensitivity may arise from artefacts in the way that temperature sensitivity is calculated, while other studies based on statistical and mechanistic models specifically designed to quantify the role of chilling have shown conflicting results. MethodsWe analysed four commonly used combinations of phenology and temperature datasets obtained from remote sensing and ground observations to elucidate whether current model‐based approaches robustly quantify how chilling, in concert with thermal forcing, controls the timing of leaf emergence during spring under current climate conditions. ResultsWe show that widely used modeling approaches that are calibrated using field‐based observations misspecify the role of chilling under current climate conditions as a result of statistical artefacts inherent to the way that chilling is parameterised. Our results highlight the limitations of existing modelling approaches and observational data in quantifying how chilling affects the timing of spring leaf emergence and suggest that decreasing chilling arising from climate warming may not constrain near‐future shifts towards earlier leaf emergence in extra‐tropical ecosystems worldwide. 

Abstract Vegetation phenology is a key control on water, energy, and carbon fluxes in terrestrial ecosystems. Because vegetation canopies are heterogeneous, spatially explicit information related to seasonality in vegetation activity provides valuable information for studies that use eddy covariance measurements to study ecosystem function and land-atmosphere interactions. Here we present a land surface phenology (LSP) dataset derived at 3 m spatial resolution from PlanetScope imagery across a range of plant functional types and climates in North America. The dataset provides spatially explicit information related to the timing of phenophase changes such as the start, peak, and end of vegetation activity, along with vegetation index metrics and associated quality assurance flags for the growing seasons of 2017–2021 for 10 × 10 km windows centred over 104 eddy covariance towers at AmeriFlux and National Ecological Observatory Network (NEON) sites. These LSP data can be used to analyse processes controlling the seasonality of ecosystem-scale carbon, water, and energy fluxes, to evaluate predictions from land surface models, and to assess satellite-based LSP products.