John Brown University

We propose a novel optimal transport-based version of the Generalized Method of Moment (GMM). Instead of handling overidentification by reweighting the data to satisfy the moment conditions (as in Generalized Empirical Likelihood methods), this method proceeds by allowing for errors in the variables of the least mean-square magnitude necessary to simultaneously satisfy all moment conditions. This approach, based on the notions of optimal transport and Wasserstein metric, aims to address the problem of assigning a logical interpretation to GMM results even when overidentification tests reject the null, a situation that cannot always be avoided in applications. We illustrate the method by revisiting Duranton, Morrow and Turner's (2014) study of the relationship between a city's exports and the extent of its transportation infrastructure. Our results corroborate theirs under weaker assumptions and provide insight into the error structure of the variables.

Biomass burning is a major global source of atmospheric ammonia (NH3), significantly influencing air quality, aerosol formation, and nitrogen cycling. Nitrogen isotope composition (delta 15N) of NH3 has been proposed as a powerful tool for source apportionment, yet values for several emission sources remain poorly constrained. This study presents the first field-based delta 15N of total reduced inorganic nitrogen (NH x = NH3 + pNH4) measurements from fresh and aged biomass-burning plumes, collected during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign in the western United States during summer 2019. The NH x concentrations were strongly correlated with carbon monoxide (CO) and fine particulate matter (PM2.5), reflecting elevated emissions during smoldering conditions. The delta 15N(NH x ) ranged from -9.1 parts per thousand to 2.1 parts per thousand (x +/- sigma: -3.3 +/- 2.9 parts per thousand; n = 16). Using a Keeling plot approach, we derived a representative biomass-burning delta 15N(NH3) value of -4.7 +/- 1.3 parts per thousand, that integrates measurements across the sampled biomass burning events, while also accounting for background NH x influences. This field-based isotopic signature is clearly distinct from agricultural and vehicular sources and substantially lower than the +12 parts per thousand value commonly assumed for biomass burning in delta 15N-based source apportionment studies. Overall, this work improves our ability to track NH3 emissions using novel isotopic constraints. Field-based nitrogen isotope measurements of ammonia emissions from biomass burning reveal distinct isotopic signatures, enabling improved source apportionment and nitrogen cycling insights.

Implicit adaptation recalibrates movements based on sensory prediction errors. It is often characterized as automatic and resource-independent, suggesting that it is insulated from cognitive influence. Here, we asked whether implicit adaptation is sensitive to goal-directed attentional demands imposed by a concurrent visual task. Across two experiments, we used clamped visual feedback to measure implicit adaptation while human adults monitored a rapidly changing visual stream for targets. In Experiment 1, participants performing the visual task showed modest early enhancement in implicit adaptation relative to a single-task control condition. In Experiment 2, adding response-contingent feedback to the visual task led to stronger and more sustained enhancement. Visual task accuracy and implicit adaptation were uncorrelated, arguing against resource competition. Model-based analyses revealed elevated error sensitivity under dual-task conditions, with individual differences reflecting an inverse relationship between error sensitivity and retention. These patterns are compatible with arousal-mediated modulation of cerebellar error processing and hierarchical models of cerebellar learning. These findings suggest that implicit adaptation is automatic but not autonomous: while it operates outside voluntary control, it appears open to the physiological states in which errors are experienced.

Abstract Submesoscale (1–10 km) vertical velocities at fronts enhance the exchange of particulate organic carbon (POC) and heat between the surface ocean and depth. While POC and temperature can be readily assessed in situ, direct quantification of flux remains uncertain due to the observational challenges of measuring submesoscale vertical velocities in the upper ocean. Here, we provided novel estimates of submesoscale‐driven POC flux by pairing airborne‐derived vertical velocities (NASA DopplerScatt) with POC estimates from satellite ocean color (Sentinel‐3 OLCI) in a persistent upwelling frontal structure in the central California Current System. Instantaneous, advective fluxes of POC and heat reached up to (1,000 mg C ) and (1,000 W ) at the front, both upward and downward. When integrated over sufficient spatiotemporal scales to achieve a negligible total volume flux, the turbulent flux, or net flux, can be estimated. Submesoscale vertical velocities made significant contributions to net downward POC flux ( mg C ) and upward heat flux ( W ). Spatial distributions of instantaneous fluxes, surface kinematics, and ship‐based, high‐resolution bio‐optical and hydrographic profiles indicated that POC flux below the mixed layer was driven by frontal overturning and filament subduction. This study contributes to the understanding of how submesoscale processes modify carbon export in the highly productive California Current System. This method can be expanded to other regions to further understand the influence of submesoscale‐driven flux on the marine carbon cycle.