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Abstract Global warming increases ecosystem respiration (ER), creating a positive carbon-climate feedback. Thermal acclimation, the direct responses of biological communities to reduce the effects of temperature changes on respiration rates, is a critical mechanism that compensates for warming-induced ER increases and dampens this positive feedback. However, the extent and effects of this mechanism across diverse ecosystems remain unclear. By analyzing CO2 flux data from 93 eddy covariance sites worldwide, we observed thermal acclimation at 84 % of the sites. If sustained, thermal acclimation could reduce projected warming-induced nighttime ER increases by at least 25 % across most climate zones by 2041-2060. Strong thermal acclimation is particularly evident in ecosystems at high elevation, with low-carbon-content soils, and within tundra, semi-arid, and warm-summer Mediterranean climates, supporting the hypothesis that extreme environments favor the evolution of greater acclimation potential. Moreover, ecosystems with dense vegetation and high productivity such as humid tropical and subtropical forests generally exhibit strong thermal acclimation, suggesting that regions with substantial CO2 uptake may continue to serve as strong carbon sinks. Conversely, some ecosystems in cold continental climates show signs of enhancing thermal responses, the opposite of thermal acclimation, which could exacerbate carbon losses as climate warms. Our study underscores the widespread yet climate-specific patterns of thermal acclimation in global terrestrial ER, emphasizing the need to incorporate these patterns into Earth System Models for more accurate carbon-climate feedback projections. 

Leaf angle distribution (LAD) measurements were made during the growing season in 2021 at the Harvard Forest in Petersham, MA, USA, and in 2022 at the Thompson Farm Earth Systems Observatory in Durham, NH, USA. At both sites, a level-calibrated digital angle tool was used to measure LAD in upper canopy foliage of common northeastern temperate tree species accessed using a mobile canopy lift. Additionally, at Thompson Farm, measurements were made at multiple heights to characterize differences of LAD in high, middle, and low canopy positions. Here, we have published those measurements, including a summary table of species average leaf angles and calculated parameters for fitted beta distributions. Processing scripts can be made available upon request to the authors. Additionally, leaf chemical, physical, structure, optical and physiological traits have been measured at these site as well as canopy scale measures of structure and UAV-based spectral, thermal, and lidar imagery. 

This dataset is a compilation of leaf trait measurements for tree species in the northeastern United States collected between 2017 and 2022 by the Terrestrial Ecosystems Analysis Lab at the University of New Hampshire. Currently, this dataset contains 1351 samples, including 18 chemical, physical and structural traits collected across 25 different tree species. Traits include stable isotopes for carbon (C) and nitrogen (N), percent C and N, C:N ratio, total chlorophyll (chl), chl a, chl b, chl a:b ratio, leaf mass per area, average leaf dry mass, average leaf area, length, and width, leaf water content, average petiole length and petiole dry mass, and petiole water content. Traits have been measured at plots spanning a wide range of latitude, longitude, elevation, and forest types. A simple table containing these plot descriptions have been included. Leaf physiological and optical traits have been measured concurrently on many of these samples and published separately. 

LiDAR data were acquired over the footprint of the flux tower and established long-term study plots at Thompson Farm Observatory, Durham, NH during the growing season. Data were acquired using a LiVox Avia lidar sensor on a Green Valley International LiAirV70 payload. The LiVox Avia is a triple echo 905 nm lidar sensor with a non-repetitive circular scanning pattern that can retrieve ~700,000 returns per second. The sensor payload was flown on board a DJI M300 at an altitude of ~65 m above ground level in a double grid pattern with ~32 m flight line spacing, yielding a return density across the sampling area >500 points per square meter. Returns were georeferenced to WGS84 UTM Zone 19N coordinates with heights above ellipsoid using Green Valley International’s LiGeoreference software with automatic boresight calibration. Outliers were removed, then flight line point clouds were merged. Returns were classified as ground and non-ground returns using Green Valley International’s Lidar360 software and output as LAS (v 1.4) data sets. LAS files were subsequently tiled for publication. 

Orthorectified flight line hyperspectral cubes retiled for publication. Collectively, the tiled hyperspectral cubes cover the footprint of the flux tower and established long-term study plots at Thompson Farm Observatory, Durham, NH. Data were acquired using a Headwall Photonics, Inc. Nano VNIR hyperspectral line scanning imager with 273 bands from 400-1000 nm. The sensor was flown on board a DJI M600 hexacopter at an altitude of ~80 m above the forest canopy, yielding ~6 cm GSD. Flight lines were converted from raw sensor observations to upwelling radiance a using a vendor-supplied radiometric calibration file for the sensor, then converted to reflectance using a calibration tarp with known reflectance. Finally, cubes were orthorectified using a 1m DSM in Headwall’s SpectralView software, mosaicked to individual flight line cubes, then subsequently tiled for publication. 

LiDAR data were acquired over the footprint of the flux tower and established long-term study plots at Thompson Farm Observatory, Durham, NH during leaf-off conditions in November 2022. Data were acquired using a LiVox Avia lidar sensor on a Green Valley International LiAirV70 payload. The LiVox Avia is a triple echo 905 nm lidar sensor with a non-repetitive circular scanning pattern that can retrieve ~700,000 returns per second. The sensor payload was flown on board a DJI M300 at an altitude of ~65 m above ground level in a double grid pattern with ~32 m flight line spacing, yielding a return density across the sampling area >500 points per square meter. Returns were georeferenced to WGS84 UTM Zone 19N coordinates with heights above ellipsoid using Green Valley International’s LiGeoreference software with automatic boresight calibration. Outliers were removed, then flight line point clouds were merged. Returns were classified as ground and non-ground returns using Green Valley International’s Lidar360 software and output as LAS (v 1.4) data sets. LAS files were subsequently tiled for publication. 

Leaf temperature measurements were collected during the summer of 2020 within forested areas at the Thompson Farm Earth Systems Observatory in Durham, New Hampshire, USA. Located within the property is a registered Ameriflux site, Thompson Farm Forest (US-TFF), as well as experimental throughfall exclusion plots that are part of DroughtNet (experiment running since 2015). Leaf temperature measurements were made within the footprint of the eddy covariance flux tower as well as within both control and throughfall exclusion treatment plots. Upper canopy foliage was accessed using a bucket lift and in situ measurements made using a handheld thermal IR sensor. All data were paired with concurrent meteorological measurements from US-TFF or data from a co-located NOAA CRN station (NH Durham 2 SSW). Additionally, leaf chemical, physical, structure, and physiological traits have been measured at this site as well as canopy scale measures of structure and UAV-based spectral, thermal, and lidar imagery. Specific to this leaf temperature dataset, leaf-level light, temperature, and vpd photosynthetic response curves were measured. 

Abstract We examined the seasonality of photosynthesis in 46 evergreen needleleaf (evergreen needleleaf forests (ENF)) and deciduous broadleaf (deciduous broadleaf forests (DBF)) forests across North America and Eurasia. We quantified the onset and end (Start_GPPand End_GPP) of photosynthesis in spring and autumn based on the response of net ecosystem exchange of CO₂to sunlight. To test the hypothesis that snowmelt is required for photosynthesis to begin, these were compared with end of snowmelt derived from soil temperature. ENF forests achieved 10% of summer photosynthetic capacity ∼3 weeks before end of snowmelt, while DBF forests achieved that capacity ∼4 weeks afterward. DBF forests increased photosynthetic capacity in spring faster (1.95% d⁻¹) than ENF (1.10% d⁻¹), and their active season length (End_GPP–Start_GPP) was ∼50 days shorter. We hypothesized that warming has influenced timing of the photosynthesis season. We found minimal evidence for long‐term change in Start_GPP, End_GPP, or air temperature, but their interannual anomalies were significantly correlated. Warmer weather was associated with earlier Start_GPP(1.3–2.5 days °C⁻¹) or later End_GPP(1.5–1.8 days °C⁻¹, depending on forest type and month). Finally, we tested whether existing phenological models could predict Start_GPPand End_GPP. For ENF forests, air temperature‐ and daylength‐based models provided best predictions for Start_GPP, while a chilling‐degree‐day model was best for End_GPP. The root mean square errors (RMSE) between predicted and observed Start_GPPand End_GPPwere 11.7 and 11.3 days, respectively. For DBF forests, temperature‐ and daylength‐based models yielded the best results (RMSE 6.3 and 10.5 days). 

Environmental observation networks, such as AmeriFlux, are foundational for monitoring ecosystem response to climate change, management practices, and natural disturbances; however, their effectiveness depends on their representativeness for the regions or continents. We proposed an empirical, time series approach to quantify the similarity of ecosystem fluxes across AmeriFlux sites. We extracted the diel and seasonal characteristics (i.e., amplitudes, phases) from carbon dioxide, water vapor, energy, and momentum fluxes, which reflect the effects of climate, plant phenology, and ecophysiology on the observations, and explored the potential aggregations of AmeriFlux sites through hierarchical clustering. While net radiation and temperature showed latitudinal clustering as expected, flux variables revealed a more uneven clustering with many small (number of sites < 5), unique groups and a few large (> 100) to intermediate (15–70) groups, highlighting the significant ecological regulations of ecosystem fluxes. Many identified unique groups were from under-sampled ecoregions and biome types of the International Geosphere-Biosphere Programme (IGBP), with distinct flux dynamics compared to the rest of the network. At the finer spatial scale, local topography, disturbance, management, edaphic, and hydrological regimes further enlarge the difference in flux dynamics within the groups. Nonetheless, our clustering approach is a data-driven method to interpret the AmeriFlux network, informing future cross-site syntheses, upscaling, and model-data benchmarking research. Finally, we highlighted the unique and underrepresented sites in the AmeriFlux network, which were found mainly in Hawaii and Latin America, mountains, and at under- sampled IGBP types (e.g., urban, open water), motivating the incorporation of new/unregistered sites from these groups.