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Forests ecosystems cover about 30% of the Earth’s land surface, corresponding to an area of roughly 42 million km2 globally. Forests play an important role in the global carbon cycle by exchanging carbon dioxide (CO2) with the atmosphere. Annually, forests act to effectively sequester large amounts of anthropogenically-emitted CO2 from the atmosphere through photosynthetic processes. Through the unparalleled increase of CO2 emissions over the past century and the subsequent climatic inconsistencies due to global climate change, the carbon sink-capacity of the world’s forests remains uncertain. Furthermore, since increasing temperatures have been shown to extend the vegetative growing season in forests, phenological responses to this change are of particular interest. In an effort to effectively assess the future carbon sequestration potential of forests, a better understanding of the climatic controls on phenology, and its influence on carbon processes, is needed. The eddy covariance (EC) technique is a stand-level, in-situ, method used widely to assess the net CO2 exchange across the canopy-atmosphere interface. Together with meteorological data, the sequestration of CO2 and the subsequent ecosystem productivity can be quantified over various time scales (half-hours to decades). This dissertation reports results from field observations of EC measured fluxes that were used to study the climatic impacts on forest phenology and the resulting carbon dynamics in southern Ontario, Canada. The study sites, part of the Turkey Point Observatory, consisted of two similarly-aged, temperate, North American forests growing under similar climatic and edaphic conditions: the 80-year old (in 2019) white pine plantation (coniferous evergreen) and 90+ year-old, naturally-regenerated, white oak (deciduous broadleaf) forest. These forests were studied from 2012 to 2017, using the EC technique, digital phenological cameras, and remote-sensing measurements. At the deciduous broadleaf forest, mid-summer (July and August) meteorological conditions were the key period in determining the annual carbon sink-strength of the site, acting to regulate the interannual variability in carbon uptake. The forest experienced higher net ecosystem productivity (+NEP; carbon sink) when soil temperatures ranged from 15 to 20°C and vapor pressure deficit was 0.7 and 1.2 kPa. From 2012 to 2016, the forest remained a net annual sink, with mean NEP of 206 ± 92 g C m-2 yr-1, similar to that of other North American deciduous forests. Spring and autumn phenological transition dates were calculated for each year (2012 to 2017) from measured EC data and digital camera greenness indices. The timing of spring and autumn transition dates were impacted by seasonal changes in air temperature and other meteorological variables. Contrary to past studies, an earlier growing season start did not equate to increased annual carbon uptake. In autumn, a later end to the deciduous forest growing season negatively impacted the net carbon uptake of the forest, as ecosystem respiration (RE) outweighed the gains of photosynthesis. The digital camera indices failed to capture the peak dates of photosynthesis, but accurately measured the spring and autumn transition dates, which may be useful in future remote sensing applications. A comparison of the two forests from 2012 to 2017 found the coniferous forest to have higher but more variable annual NEP (218 ± 109 g C m-2 yr-1) compared to that of the deciduous broadleaf forest (200 ± 83 g C m-2 yr-1). Similarly, the mean annual evapotranspiration (ET) was higher (442 ± 33 mm yr-1) at the coniferous forest compared to that of the broadleaf forest (388 ± 34 mm yr-1). The greatest difference between years resulted from the response to heat and drought. During drought years, deciduous carbon and water fluxes were less sensitive to changes in temperature or water availability compared to the evergreen forest. Carotenoid sensitive vegetative indices and the red-edge chlorophyll index were shown to effectively capture seasonal changes in photosynthesis phenology within both forests via proximal remote sensing measurements during the 2016 growing season. Satellite vegetative indices were highly correlated to EC photosynthesis, but significant interannual variability resulted from either meteorological inputs or the heterogeneous landscapes of the agriculturally-dominated study area. This dissertation improved our understanding of the dynamics of carbon exchange within the northeastern North American deciduous forest ecozone, through the examination of climatic variability and its impact on carbon and phenology. This dissertation also contributed to efforts being made to better evaluate the impact of species composition on carbon dynamics in geographically similar forests. Moreover, the use of the digital phenological camera observations and remote sensing techniques to complement and better understand the fluxes observed with the EC method was innovative and may help other researchers in future studies. Dissertation Doctor of Philosophy (PhD)

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible., Scientific Data, 7, ISSN:2052-4463