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Phytoplankton net primary production in the Arctic has historically been constrained to a short, intense summer bloom that sustains fish, seabird, and marine mammal populations. However, climate change is altering Arctic phytoplankton bloom phenology. We use an ensemble of Earth system model simulations to isolate the impact of climate change on the timing, duration, and importance (relative contribution to total net primary production) of the bloom. Earlier blooms emerge across 71% of the Arctic Ocean by 2100, when blooms begin 34 days earlier and last 15 days longer than in 1970. Productivity is less concentrated in a single bloom in sub-Arctic seas and on Arctic inflow shelves by 2100, indicating that the bloom declines in importance. In contrast, bloom phenology and productivity exhibit only small changes by 2020. Our study demonstrates that anthropogenic climate change will greatly alter the timing and importance of the Arctic Ocean phytoplankton bloom by 2100. Anthropogenic climate change impacts Arctic Ocean phytoplankton phenology, resulting in phytoplankton blooms which start 34 days earlier and last 15 days longer in 2100 compared with 1970, according to an ensemble of Earth system model simulations.

In the Arctic Ocean, sea ice algae are responsible for a small but seasonally important pulse of primary production. Their persistence is threatened by the rapid loss of sea ice from the Arctic Ocean due to climate change, but this threat will be at least partially offset by the replacement of multiyear ice (MYI) with first-year ice (FYI). FYI is thinner and usually features a thinner snow cover than MYI, thus transmitting more light to support ice algal growth. We combined remote sensing, reanalysis data, and modeling products with a radiative transfer model to assess how the changing physical conditions in the Arctic altered the extent and duration of the bottom ice algal habitat over a 34-year period. Habitat was defined as areas where enough light penetrates to the bottom ice to support net positive photosynthesis. The Arctic shifted from 37% FYI in 1985 to 63% in 2018, as the 2.0 × 106 km2 increase in FYI extent outpaced the 0.6 × 106 km2 decrease in overall sea ice extent above the Arctic Circle. The proliferation of younger ice corresponded with a 0.08 m decade–1 decrease in average sea ice thickness and a 0.003 m decade–1 decrease in average snow depth. The end of the ice algal season, marked by the onset of warm summer air temperatures, moved slightly earlier, by 1.4 days decade–1. Our analysis indicates that ice algal habitat extent increased by 0.4 × 106 km2 decade–1, or from 48% to 66% of total sea ice extent. The average ice algal growing season also lengthened by 2.4 days and shifted earlier in the year. Together, these trends suggest that net primary production in Arctic sea ice increased during 1985–2018. The most dramatic changes were localized in the Central Basin and the Chukchi Sea and were driven primarily by the declining snow cover and the shift from MYI to FYI. Although the Arctic recently became more favorable to ice algae, we expect that this trend will not continue indefinitely, as a limited amount of MYI remains.

Under-ice (UI) phytoplankton blooms have been observed in most of the marginal seas of the Arctic Ocean and are often found to contribute substantially to total primary production. However, because remote sensing studies cannot directly measure UI blooms and limited in situ observations prevent analysis of their frequency across the region as a whole, their distribution has not been characterized across the Arctic Ocean. Here, we use remote sensing data to discern which parts of the seasonally ice-free Arctic Ocean demonstrate evidence of UI blooms and whether UI bloom frequency changed between 2003 and 2021. Results suggest that UI blooms were generated frequently, with evidence of UI blooms over nearly 40% of the observable seasonally ice-free Arctic Ocean, while marginal ice zone blooms covered 60% in any given year. However, while there was no significant change in the UI bloom area over the study period, there was a 7% decline in the proportion of UI area in the seasonal sea ice zone. This decline was driven largely by declines at lower latitudes, where sea ice retreats earlier in the year, and in the Chukchi Sea, where UI blooms were also most prevalent. Our analysis demonstrates the need for increased observational studies of UI blooms and their ecological and biogeochemical consequences throughout the Arctic Ocean.

Core Ideas Harvest yield varies more across species than sugar content and accessibility. Harvest yield and sugar accessibility are both critical parameters for conversion. Near‐infrared/partial least square models are valuable for quickly evaluating biomass for bioconversion. Cool‐season (C3) perennial grasses have a long history of cultivation and use as animal forage. This study evaluated 15 cultivars of C3 grasses, when harvested in late June for increased biomass yield, as biofuel feedstocks using near‐ infrared spectroscopy (NIR) based partial least square (PLS) analysis. These grasses were grown near Iliff, CO, for three growing seasons (2009–2011). The carbohydrate composition and released carbohydrates (total glucose and xylose released from dilute acid pretreatment and enzymatic hydrolysis [EH]) were predicted for samples from the study using NIR/PLS. The results were analyzed from a biofuels perspective, where composition combined with harvest yield provided information on the carbohydrate yield available for biomass conversion processes, and released carbohydrate yield provided information on the accessibility of those carbohydrates to conversion methods. The range in harvest yields varied more among cultivars (2900 kg ha−1) than did the range in carbohydrate composition (56.0 g kg−1) or released carbohydrates (60.0 g kg−1). When comparing carbohydrate yield to released carbohydrate yield between cultivars, an efficiency as high as 87% release of available carbohydrates was obtained for pubescent wheatgrass [Thinopyrum intermedium (Host) Barkworth & D.R. Dewey ‘Mansaka’], with a low of 71% for hybrid wheatgrass [Elytrigia repens (L.) nevski ´ pseudoroegneria spicata (PURSH) A. Love ‘Newhy’]. Though hybrid wheatgrass had the lowest release efficiency, its high harvest yield resulted in release of more total carbohydrates than half the other cultivars analyzed. This suggested that harvest yield, carbohydrate release, and carbohydrate composition, togetherplay significant roles in biofuel feedstock evaluation.

Perennial grass mixtures established on Conservation Reserve Program (CRP) lands can be an important source of feedstock for bioenergy production. This study aimed to evaluate management practices for optimizing the quality of bioenergy feedstock and stand persistence of grass‐legume mixtures under diverse environments. A 5‐year field study (2008–2012) was conducted to assess the effects of two harvest timings (at anthesis vs after complete senescence) and three nitrogen (N) rates (0, 56, 112 kg N ha−1) on biomass chemical compositions (i.e., cell wall components, ash, volatiles, total carbon, and N contents) and the feedstock energy potential, examined by the theoretical ethanol yield (TEY) and the total TEY (i.e., the product of biomass yield and TEY, L ha−1), of cool‐season mixtures in Georgia and Missouri and a warm‐season mixture in Kansas. The canonical correlation analysis (CCA) was used to investigate the effect of vegetative species transitions on feedstock quality. Although environmental variations (mainly precipitation) greatly influenced the management effect on chemical compositions, the delayed harvest after senescence generally improved feedstock quality. In particular, the overall cell wall concentrations and TEY of the warm‐season mixtures increased by approximately 7%. Additional N supplies improved the total TEY of both mixtures by ~1.6–4.2 L ha−1 per 1.0 kg N ha−1 input but likely lowered the feedstock quality, particularly for the cool‐season mixture. The cell wall concentrations of cool‐season mixture reduced by approximately 3%–6%. The CCA results indicated that the increased legume compositions (under low N input) likely enhanced lignin but reduced ash concentrations. This field research demonstrated that with proper management, grass‐legume mixtures on CRP lands can provide high‐quality feedstock for bioenergy productions. Perennial grass‐legume mixtures have a great potential for improving ecosystem services in marginal lands (an ideal polyculture production system) and providing renewable feedstock for biofuel productions, simultaneously. We achieved these goals via the implementation of a sustainable and quality bioenergy feedstock supply by optimizing the harvest and nitrogen management practices. This study also used a sophisticated multivariate analysis (Canonical correlation analysis, CCA) to investigate the effect of vegetative species transitions on feedstock quality, considered as a powerful tool for predicting bioenergy yields via a cost‐effective assessment or scout technology (e.g., remote‐sensing techniques) in the future.

Switchgrass (Panicum virgatum L.) is a promising feedstock for bioenergy and bioproducts; however, its inherent variability in chemical attributes creates challenges for uniform conversion efficiencies and product quality. It is necessary to understand the range of variation and factors (i.e., field management, environmental) influencing chemical attributes for process improvement and risk assessment. The objectives of this study were to (1) examine the impact of nitrogen fertilizer application rate, year, and location on switchgrass chemical attributes, (2) examine the relationships among chemical attributes, weather and soil data, and (3) develop models to predict chemical attributes using environmental factors. Switchgrass samples from a field study spanning four locations including upland cultivars, one location including a lowland cultivar, and between three and six harvest years were assessed for glucan, xylan, lignin, volatiles, carbon, nitrogen, and ash concentrations. Using variance estimation, location/cultivar, nitrogen application rate, and year explained 65%–96% of the variation for switchgrass chemical attributes. Location/cultivar × year interaction was a significant factor for all chemical attributes indicating environmental‐based influences. Nitrogen rate was less influential. Production variables and environmental conditions occurring during the switchgrass field trials were used to successfully predict chemical attributes using linear regression models. Upland switchgrass results highlight the complexity in plant responses to growing conditions because all production and environmental variables had strong relationships with one or more chemical attributes. Lowland switchgrass was limited to observations of year‐to‐year environmental variability and nitrogen application rate. All explanatory variable categories were important for lowland switchgrass models but stand age and precipitation relationships were particularly strong. The relationships found in this study can be used to understand spatial and temporal variation in switchgrass chemical attributes. The ability to predict chemical attributes critical for conversion processes in a geospatial/temporal manner would provide state‐of‐the‐art knowledge for risk assessment in the bioenergy and bioproducts industry. Switchgrass is a promising feedstock for bioenergy and bioproducts. Chemical attributes were assessed for switchgrass from a field study spanning five locations and up to six harvest years. Production variables and environmental conditions occurring during the switchgrass field trials were used to successfully predict chemical attributes using linear regression models. The relationships found in this study can be used to understand spatial and temporal variation in switchgrass chemical attributes. The ability to predict chemical attributes critical for conversion processes in a geospatial/temporal manner would provide state‐of‐the‐art knowledge for risk assessment in the bioenergy and bioproducts industry.

The performance of a conventional laboratory near-infrared (NIR) spectrometer and two NIR spectrometer prototypes (a Texas Instruments NIRSCAN Nano evaluation model (EVM) and an InnoSpectra NIR-M-R2 spectrometer) are compared by collecting reflectance spectra of 270 well-characterized herbaceous biomass samples, building calibration models using the partial least squares (PLS-2) algorithm to predict five constituents of the samples from the reflectance spectra, and comparing the resulting model statistics. The prediction models developed using spectra from the Foss XDS spectrometer were slightly better than the prediction models developed using spectra from either the TI NIRSCAN Nano EVM and the InnoSpectra NIR-M-R2 as measured by the root mean square error (RMSECV) and the correlation coefficient (R2_cv) for “leave-one-out” cross-validation (CV). The models built from the two prototype units were not statistically significantly different from each other (p = 0.05). The Foss spectrometer has a larger wavelength range (400–2500 nm) compared with the two prototypes (900–1700 nm). When the spectra from the Foss XDS spectrometer were truncated so their wavelength range matched the wavelength range of the two prototype units, the resulting model was not statistically significantly different from the models from either prototype.

The Arctic Ocean has changed substantially because of climate change. The loss of sea ice extent and thickness has increased light availability in the surface ocean during the ice-covered portion of the year. Sea ice loss has also been a factor in the observed increases in sea surface temperatures and likely impacts surface ocean nutrient inventories. These changing environmental conditions have substantially altered patterns of phytoplankton net primary production (NPP) across the Arctic Ocean. While NPP in the Arctic Ocean was previously considered insubstantial until the time of sea ice breakup and retreat, the observation of massive under-ice (UI) phytoplankton blooms in many of the Arctic seas reveals that the largest pulse of NPP may be produced prior to sea ice retreat. However, estimating how much NPP is generated during the UI part of the year is challenging, as satellite observations are hampered by sea ice cover and very few field campaigns have targeted UI blooms for study.This thesis uses a combination of laboratory experiments, biogeochemical modeling, and an analysis of satellite remote sensing data to better understand how the magnitude and spatial frequency of UI phytoplankton blooms has changed over time in the Arctic Ocean, as well as to assess the likely biogeochemical consequences of these blooms. In Chapter 2, I present a one-dimensional ecosystem model (CAOS-GO), which I used to evaluate the magnitude of UI phytoplankton blooms in the northern Chukchi Sea (72°N) between 1988 and 2018. UI blooms were produced in all but four years over that period, accounted for half of total annual NPP, and were the primary drivers of interannual variability in NPP. Further, I found that years with large UI blooms had reduced rates of zooplankton grazing, leading to an intensification of the mismatch between phytoplankton and zooplankton populations.In Chapter 3, I used the same model configuration to investigate the role of UI bloom variability in controlling sedimentary processes in the northern Chukchi Sea. I found that, as total annual NPP increased from 1988 to 2018, there were increases in particle export to the benthos, nitrification in the water column and the sediments, and sedimentary denitrification. These increases in particle export to the benthos and denitrification were driven by higher rates of NPP early in the year (JanuaryJune) and were highest in years where under-ice blooms dominate, indicating the importance of UI NPP as drivers of these biogeochemical consequences. Additionally, I tested the system’s sensitivity to added N, finding that, if N supply in the region increased, 30% of the added N would subsequently be lost to denitrification.I subsequently deployed this model in the southern Chukchi Sea (68°N) to understand latitudinal di↵erences in UI bloom importance across the region (Chapter 4). I found that UI blooms were far less important contributors to total NPP in the southern Chukchi Sea. Further, I found that their importance was waning over time; NPP generated in the UI period from 2013-2018 was only 34% of the 1988-1993 mean.

Cool-season (C3) perennial grasses have a long history of cultivation and use as animal forage. This study evaluated 15 cultivars of C3 grasses, when harvested in late June for increased biomass yield, as biofuel feedstocks using near- infrared spectroscopy (NIR) based partial least square (PLS) analysis. These grasses were grown near Iliff, CO, for three growing seasons (2009-2011). The carbohydrate composition and released carbohydrates (total glucose and xylose released from dilute acid pretreatment and enzymatic hydrolysis [EH]) were predicted for samples from the study using NIR/PLS. The results were analyzed from a biofuels perspective, where composition combined with harvest yield provided information on the carbohydrate yield available for biomass conversion processes, and released carbohydrate yield provided information on the accessibility of those carbohydrates to conversion methods. The range in harvest yields varied more among cultivars (2900 kg ha-1) than did the range in carbohydrate composition (56.0 g kg-1) or released carbohydrates (60.0 g kg-1). When comparing carbohydrate yield to released carbohydrate yield between cultivars, an efficiency as high as 87% release of available carbohydrates was obtained for pubescent wheatgrass [Thinopyrum intermedium (Host) Barkworth & D.R. Dewey 'Mansaka'], with a low of 71% for hybrid wheatgrass [Elytrigia repens (L.) nevski pseudoroegneria spicata (PURSH) A. Love 'Newhy']. Though hybrid wheatgrass had the lowest release efficiency, its high harvest yield resulted in release of more total carbohydrates than half the other cultivars analyzed. Furthermore, this suggested that harvest yield, carbohydrate release, and carbohydrate composition, together play significant roles in biofuel feedstock evaluation.

Background In an effort to find economical, carbon-neutral transportation fuels, biomass feedstock compositional analysis methods are used to monitor, compare, and improve biofuel conversion processes. These methods are empirical, and the analytical variability seen in the feedstock compositional data propagates into variability in the conversion yields, component balances, mass balances, and ultimately the minimum ethanol selling price (MESP). We report the average composition and standard deviations of 119 individually extracted National Institute of Standards and Technology (NIST) bagasse [Reference Material (RM) 8491] run by seven analysts over 7 years. Two additional datasets, using bulk-extracted bagasse (containing 58 and 291 replicates each), were examined to separate out the effects of batch, analyst, sugar recovery standard calculation method, and extractions from the total analytical variability seen in the individually extracted dataset. We believe this is the world's largest NIST bagasse compositional analysis dataset and it provides unique insight into the long-term analytical variability. Understanding the long-term variability of the feedstock analysis will help determine the minimum difference that can be detected in yield, mass balance, and efficiency calculations. Results The long-term data show consistent bagasse component values through time and by different analysts. This suggests that the standard compositional analysis methods were performed consistently and that the bagasse RM itself remained unchanged during this time period. The long-term variability seen here is generally higher than short-term variabilities. It is worth noting that the effect of short-term or long-term feedstock compositional variability on MESP is small, about $0.03 per gallon. Conclusions The long-term analysis variabilities reported here are plausible minimum values for these methods, though not necessarily average or expected variabilities. We must emphasize the importance of training and good analytical procedures needed to generate this data. When combined with a robust QA/QC oversight protocol, these empirical methods can be relied upon to generate high-quality data over a long period of time.