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Abstract As a warmer climate enables an increase in atmospheric humidity, extreme precipitation events have become more frequent in the Northeastern United States. Understanding the impact of evolving precipitation patterns is critical to understanding water cycling in temperate forests and moisture coupling between the atmosphere and land surface. Although the role of soil moisture in evapotranspiration has been extensively studied, few have analyzed the role of soil texture in determining ecosystem‐atmosphere feedbacks. In this study, we utilized long term data associated with ecosystem water fluxes to deduce the strength of land‐atmosphere coupling at Harvard Forest, Petersham, MA, USA. We found a 1.5% increase in heavy precipitation contribution per decade where high‐intensity events compose upwards of 42% of total yearly precipitation in 2023. Intensifying precipitation trends were found in conjunction with a long‐term soil drying at the Harvard Forest despite no significant increase in evapotranspiration over 32 years. This suggests that soil water holding capacity is a key mediating variable controlling the supply of water to ecosystems and the atmosphere. We found that these land surface changes directly impacted the lifted condensation level (LCL) height over Harvard Forest which was found to be increasing at a rate of 6.62 m per year while atmospheric boundary layer (ABL) heights have fallen at a modest rate of 1.76 m per year. This has amplified dry feedbacks between the land surface and the atmosphere such that 80% of observed summers ending in a water deficit also had an anomalously low soil water content in the spring. 

Nitrogen (N) is a critical element in many ecological and biogeochemical processes in forest ecosystems. Cycling of N is sensitive to changes in climate, atmospheric carbon dioxide (CO2) concentrations, and air pollution. Streamwater nitrate draining a forested ecosystem can indicate how an ecosystem is responding to these changes. We observed a pulse in streamwater nitrate concentration and export at a long-term forest research site in eastern North America that resulted in a 10-fold increase in nitrate export compared to observations over the prior decade. The pulse in streamwater nitrate occurred in a reference catchment in the 2013 water year, but was not associated with a distinct disturbance event. We analyzed a suite of environmental variables to explore possible causes. The correlation between each environmental variable and streamwater nitrate concentration was consistently higher when we accounted for the antecedent conditions of the variable prior to a given streamwater observation. In most cases, the optimal antecedent period exceeded two years. We assessed the most important variables for predicting streamwater nitrate concentration by training a machine learning model to predict streamwater nitrate concentration in the years preceding and during the streamwater nitrate pulse. The results of the correlation and machine learning analyses suggest that the pulsed increase in streamwater nitrate resulted from both (1) decreased plant uptake due to lower terrestrial gross primary production, possibly due to increased soil frost or reduced solar radiation or both; and (2) increased net N mineralization and nitrification due to warm temperatures from 2010 to 2013. Additionally, variables associated with hydrological transport of nitrate, such as maximum stream discharge, emerged as important, suggesting that hydrology played a role in the pulse. Overall, our analyses indicate that the streamwater nitrate pulse was caused by a combination of factors that occurred in the years prior to the pulse, not a single disturbance event.