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Abstract Pigment dynamics in temperate evergreen forests remain poorly characterized, despite their year-round photosynthetic activity and importance for carbon cycling. Developing rapid, nondestructive methods to estimate pigment composition enables high-throughput assessment of plant acclimation states. In this study, we investigate the seasonality of eight chlorophyll and carotenoid pigments and hyperspectral reflectance data collected at both the needle (400–2400 nm) and canopy (420–850 nm) scales in Pinus palustris (longleaf pine) at the Ordway Swisher Biological Station in north-central Florida, USA. Needle spectra were obtained at three distinct times throughout the year, while tower-based spectra were collected continuously over a nine-month period. Seasonal trends in photoprotective pigments (e.g., lutein and xanthophylls) and photosynthetic pigments (e.g., chlorophylls) aligned closely with seasonal changes in photosynthetically active radiation and gross primary productivity. To track inter-tree and seasonal variability in pigment pools with hyperspectral reflectance data, we used correlation analyses and ridge regression models. Ridge regression models using the full hyperspectral range outperformed predictions using standard linear regression with specific wavelengths in a normalized difference index fashion. Ridge regression successfully predicted all pigment pools (R2 > 0.5) with comparable accuracy at both the needle and canopy scales. The models performed best for lutein, neoxanthin, antheraxanthin, and chlorophyll a and b - which had greater inter-tree and seasonal variation - and achieved moderate accuracy for violaxanthin, alpha-carotene, and beta-carotene. These results provide a foundation for scaling biochemical traits from ground-based sensors to airborne and satellite platforms, particularly in ecosystems with subtle changes in pigment dynamics. 

Modeling of atmosphere–snow exchange provides insight into fundamental processes driving pollutant deposition. Gas properties, such as solubility and stickiness to ice, influence the role of the snowpack as a trace gas reservoir and chemical reactor. 

The Alaskan Layered Pollution And Chemical Analysis (ALPACA)-2022 field study (https://alpaca.community.uaf.edu/alpaca-field-study/) investigated air pollution under dark and cold conditions in Fairbanks, Alaska in January and February, 2022. One of the main motivations for ALPACA was to understand how temperature inversions trap pollutants at the surface. This was studied by using University of California, Los Angeles (UCLA) long-path Differential Optical Absorption Spectroscopy instrument (LP-DOAS) to probe the Fairbanks atmosphere from 12 meters(m) – 191m altitude and yield information on the vertical distribution of various trace gases / pollutants. The dataset contains the open atmosphere LP-DOAS measurement of Ozone (O3), Sulfur Dioxide (SO2), Nitrogen Dioxide (NO2), Formaldehyde (HCHO), and Nitrous Acid (HONO) on four different light paths over wintertime downtown Fairbanks, Alaska (AK) during ALPACA. The four light paths cover the following altitude intervals: 12-17m, 17-73m, 17-115m, and 17-191m. The ReadMe file included in the data set provides exact coordinates, length, and altitude intervals for the four light paths. 

Abstract We investigated how various sources contributed to observations of over 40 trace gas and particulate species in a typical Fairbanks residential neighborhood during the Alaskan Layered Pollution and Chemical Analysis campaign in January–February 2022. Aromatic volatile organic compounds (VOCs) accounted for ∼50% of measured VOCs (molar ratio), while methanol and ethanol accounted for ∼34%. The total wintertime VOC burden and contribution from aromatics were much higher than other US urban areas. Based on diel cycles and positive matrix factorization (PMF) analyses, we find traffic was the largest source of NO, CO, black carbon, and aromatic VOCs. Formic and acetic acid, hydroxyacetone, furanoids, and other VOCs were primarily attributed to residential wood combustion (RWC). Formaldehyde was one of several VOCs featuring significant contributions from multiple sources: RWC (∼35%), aging (∼30%), traffic (∼21%), and heating oil combustion (HO, ∼14%). PMF solutions assigned primary fine particulate matter to RWC (10%–30%), traffic (25%–40%), and HO (30%–60%), the latter likely reflecting high sulfur emissions from older furnaces and fast secondary chemistry. Despite cold and dark conditions, secondary processes impacted many trace gas and particle species' budget by ±10%–20% and more in some cases. Transport of O₃‐rich regional air into Fairbanks contributed to aging, specifically NO₃radical formation. This work highlights a long‐term trend observed in Fairbanks: increasing traffic and decreasing RWC relative contributions as total pollution decreases. Fairbanks exports a relatively fresh pollutant mixture to the regional arctic, the fate of which warrants future study. 

Abstract The seasonal timing and magnitude of photosynthesis in evergreen needleleaf forests (ENFs) has major implications for the carbon cycle and is increasingly sensitive to changing climate. Earlier spring photosynthesis can increase carbon uptake over the growing season or cause early water reserve depletion that leads to premature cessation and increased carbon loss. Determining the start and the end of the growing season in ENFs is challenging due to a lack of field measurements and difficulty in interpreting satellite data, which are impacted by snow and cloud cover, and the pervasive “greenness” of these systems. We combine continuous needle‐scale chlorophyll fluorescence measurements with tower‐based remote sensing and gross primary productivity (GPP) estimates at three ENF sites across a latitudinal gradient (Colorado, Saskatchewan, Alaska) to link physiological changes with remote sensing signals during transition seasons. We derive a theoretical framework for observations of solar‐induced chlorophyll fluorescence (SIF) and solar intensity‐normalized SIF (SIF_relative) under snow‐covered conditions, and show decreased sensitivity compared with reflectance data (~20% reduction in measured SIF vs. ~60% reduction in near‐infrared vegetation index [NIRv] under 50% snow cover). Needle‐scale fluorescence and photochemistry strongly correlated (r² = 0.74 in Colorado, 0.70 in Alaska) and showed good agreement on the timing and magnitude of seasonal transitions. We demonstrate that this can be scaled to the site level with tower‐based estimates of LUE_Pand SIF_relativewhich were well correlated across all sites (r² = 0.70 in Colorado, 0.53 in Saskatchewan, 0.49 in Alaska). These independent, temporally continuous datasets confirm an increase in physiological activity prior to snowmelt across all three evergreen forests. This suggests that data‐driven and process‐based carbon cycle models which assume negligible physiological activity prior to snowmelt are inherently flawed, and underscores the utility of SIF data for tracking phenological events. Our research probes the spectral biology of evergreen forests and highlights spectral methods that can be applied in other ecosystems. 

Abstract. Subarctic cities notoriously experience severe winter pollution episodes with fine particle (PM2.5) concentrations above 35 µg m−3, the US Environmental Protection Agency (EPA) 24 h standard. While winter sources of primary particles in Fairbanks, Alaska, have been studied, the chemistry driving secondary particle formation is elusive. Biomass burning is a major source of wintertime primary particles, making the PM2.5 rich in light-absorbing brown carbon (BrC). When BrC absorbs sunlight, it produces photooxidants – reactive species potentially important for secondary sulfate and secondary organic aerosol formation – yet photooxidant measurements in high-latitude PM2.5 remain scarce. During the winter of 2022 Alaskan Layered Pollution And Chemical Analysis (ALPACA) field campaign in Fairbanks, we collected PM filters, extracted the filters into water, and exposed the extracts to simulated sunlight to characterize the production of three photooxidants: oxidizing triplet excited states of BrC, singlet molecular oxygen, and hydroxyl radical. Next, we used our measurements to model photooxidant production in highly concentrated aerosol liquid water. While conventional wisdom indicates photochemistry is limited during high-latitude winters, we find that BrC photochemistry is significant: we predict high triplet and singlet oxygen daytime particle concentrations up to 2×10-12 and 3×10-11 M, respectively, with moderate hydroxyl radical concentrations up to 5×10-15 M. Although our modeling predicts that triplets account for 0.4 %–10 % of daytime secondary sulfate formation, particle photochemistry cumulatively dominates, generating 76 % of daytime secondary sulfate formation, largely due to in-particle hydrogen peroxide, which contributes 25 %–54 %. Finally, we estimate triplet production rates year-round, revealing the highest rates in late winter when Fairbanks experiences severe pollution and in summer when wildfires generate BrC. 

Abstract Snowpack emissions are recognized as an important source of gas‐phase reactive bromine in the Arctic and are necessary to explain ozone depletion events in spring caused by the catalytic destruction of ozone by halogen radicals. Quantifying bromine emissions from snowpack is essential for interpretation of ice‐core bromine. We present ice‐core bromine records since the pre‐industrial (1750 CE) from six Arctic locations and examine potential post‐depositional loss of snowpack bromine using a global chemical transport model. Trend analysis of the ice‐core records shows that only the high‐latitude coastal Akademii Nauk (AN) ice core from the Russian Arctic preserves significant trends since pre‐industrial times that are consistent with trends in sea ice extent and anthropogenic emissions from source regions. Model simulations suggest that recycling of reactive bromine on the snow skin layer (top 1 mm) results in 9–17% loss of deposited bromine across all six ice‐core locations. Reactive bromine production from below the snow skin layer and within the snow photic zone is potentially more important, but the magnitude of this source is uncertain. Model simulations suggest that the AN core is most likely to preserve an atmospheric signal compared to five Greenland ice cores due to its high latitude location combined with a relatively high snow accumulation rate. Understanding the sources and amount of photochemically reactive snow bromide in the snow photic zone throughout the sunlit period in the high Arctic is essential for interpreting ice‐core bromine, and warrants further lab studies and field observations at inland locations. 

Abstract Evergreen needleleaf forests (ENFs) play a sizable role in the global carbon cycle, but the biological and physical controls on ENF carbon cycle feedback loops are poorly understood and difficult to measure. To address this challenge, a growing appreciation for the stress physiology of photosynthesis has inspired emerging techniques designed to detect ENF photosynthetic activity with optical signals. This Overview summarizes how fundamental plant biological and biophysical processes control the fate of photons from leaf to globe, ultimately enabling remote estimates of ENF photosynthesis. We demonstrate this using data across four ENF sites spanning a broad range of environmental conditions and link leaf- and stand-scale observations of photosynthesis (i.e., needle biochemistry and flux towers) with tower- and satellite-based remote sensing. The multidisciplinary nature of this work can serve as a model for the coordination and integration of observations made at multiple scales. 

Abstract Remote sensing is a powerful tool for understanding and scaling measurements of plant carbon uptake via photosynthesis, gross primary productivity (GPP), across space and time. The success of remote sensing measurements can be attributed to their ability to capture valuable information on plant structure (physical) and function (physiological), both of which impact GPP. However, no single remote sensing measure provides a universal constraint on GPP and the relationships between remote sensing measurements and GPP are often site specific, thereby limiting broader usefulness and neglecting important nuances in these signals. Improvements must be made in how we connect remotely sensed measurements to GPP, particularly in boreal ecosystems which have been traditionally challenging to study with remote sensing. In this paper we improve GPP prediction by using random forest models as a quantitative framework that incorporates physical and physiological information provided by solar-induced fluorescence (SIF) and vegetation indices (VIs). We analyze 2.5 years of tower-based remote sensing data (SIF and VIs) across two field locations at the northern and southern ends of the North American boreal forest. We find (a) remotely sensed products contain information relevant for understanding GPP dynamics, (b) random forest models capture quantitative SIF, GPP, and light availability relationships, and (c) combining SIF and VIs in a random forest model outperforms traditional parameterizations of GPP based on SIF alone. Our new method for predicting GPP based on SIF and VIs improves our ability to quantify terrestrial carbon exchange in boreal ecosystems and has the potential for applications in other biomes.