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Increased plant growth under elevated carbon dioxide (CO₂) slows the pace of climate warming and underlies projections of terrestrial carbon (C) and climate dynamics. However, this important ecosystem service may be diminished by concurrent changes to vegetation carbon‐to‐nitrogen (C:N) ratios. Despite clear observational evidence of increasing foliar C:N under elevated CO₂, our understanding of potential ecological consequences of foliar stoichiometric flexibility is incomplete. Here, we illustrate that when we incorporated CO₂‐driven increases in foliar stoichiometry into the Community Land Model the projected land C sink decreased two‐fold by the end of the century compared to simulations with fixed foliar chemistry. Further, CO₂‐driven increases in foliar C:N profoundly altered Earth's hydrologic cycle, reducing evapotranspiration and increasing runoff, and reduced belowground N cycling rates. These findings underscore the urgency of further research to examine both the direct and indirect effects of changing foliar stoichiometry on soil N cycling and plant productivity.

Abstract. Global change research demands a convergence among academic disciplines to understand complex changes in Earth system function. Limitations related to data usability and computing infrastructure, however, present barriers to effective use of the research tools needed for this cross-disciplinary collaboration. To address these barriers, we created a computational platform that pairs meteorological data and site-level ecosystem characterizations from the National Ecological Observatory Network (NEON) with the Community Terrestrial System Model (CTSM) that is developed with university partners at the National Center for Atmospheric Research (NCAR). This NCAR–NEON system features a simplified user interface that facilitates access to and use of NEON observations and NCAR models. We present preliminary results that compare observed NEON fluxes with CTSM simulations and describe how the collaboration between NCAR and NEON that can be used by the global change research community improves both the data and model. Beyond datasets and computing, the NCAR–NEON system includes tutorials and visualization tools that facilitate interaction with observational and model datasets and further enable opportunities for teaching and research. By expanding access to data, models, and computing, cyberinfrastructure tools like the NCAR–NEON system will accelerate integration across ecology and climate science disciplines to advance understanding in Earth system science and global change.

Interannual variations in the flux of carbon dioxide (CO₂) between the land surface and the atmosphere are the dominant component of interannual variations in the atmospheric CO₂growth rate. Here, we investigate the potential to predict variations in these terrestrial carbon fluxes 1–10 years in advance using a novel set of retrospective decadal forecasts of an Earth system model. We demonstrate that globally-integrated net ecosystem production (NEP) exhibits high potential predictability for 2 years following forecast initialization. This predictability exceeds that from a persistence or uninitialized forecast conducted with the same Earth system model. The potential predictability in NEP derives mainly from high predictability in ecosystem respiration, which itself is driven by vegetation carbon and soil moisture initialization. Our findings unlock the potential to forecast the terrestrial ecosystem in a changing environment.

Earth system models (ESMs) rely on the calculation of canopy conductance in land surface models (LSMs) to quantify the partitioning of land surface energy, water, andCO₂fluxes. This is achieved by scaling stomatal conductance,g_w, determined from physiological models developed for leaves. Traditionally, models forg_whave been semi‐empirical, combining physiological functions with empirically determined calibration constants. More recently, optimization theory has been applied to modelg_winLSMs under the premise that it has a stronger grounding in physiological theory and might ultimately lead to improved predictive accuracy. However, this premise has not been thoroughly tested. Using original field data from contrasting forest systems, we compare a widely used empirical type and a more recently developed optimization‐typeg_wmodel, termedBBandMED, respectively. Overall, we find no difference between the two models when used to simulateg_wfrom photosynthesis data, or leaf gas exchange from a coupled photosynthesis‐conductance model, or gross primary productivity and evapotranspiration for aFLUXNETtower site with theCLM5 communityLSM. Field measurements reveal that the key fitted parameters forBBandMED,g_1Bandg_1M,exhibit strong species specificity in magnitude and sensitivity toCO₂, andCLM5 simulations reveal that failure to include this sensitivity can result in significant overestimates of evapotranspiration for high‐CO₂scenarios. Further, we show thatg_1Bandg_1Mcan be determined from meanc_i/c_a(ratio of leaf intercellular to ambientCO₂concentration). Applying this relationship withc_i/c_avalues derived from a leaf δ¹³C database, we obtain a global distribution ofg_1Bandg_1M, and these values correlate significantly with mean annual precipitation. This provides a new methodology for global parameterization of theBBandMEDmodels inLSMs, tied directly to leaf physiology but unconstrained by spatial boundaries separating designated biomes or plant functional types.