Symbiotic nitrogen fixation is the greatest potential input of nitrogen into terrestrial ecosystems. As a result, nitrogen fixation is critical to the functioning of the land carbon sink and its capacity to offset anthropogenic CO2 emissions and climate change. However, our understanding of the controls over nitrogen fixation rates and nitrogen fixing tree abundance is limited, resulting in paradoxes such as the relative absence of nitrogen fixing trees at high latitudes (where nitrogen is most limiting and it seems that nitrogen fixation should be most beneficial) and tropical forest nitrogen saturation, a mechanistically poor representation of nitrogen fixation in terrestrial biosphere models, and incomplete theory for variation in the successional trajectories of nitrogen fixing trees. This dissertation consists of four chapters that examine the drivers of symbiotic nitrogen fixation rates and the abundance of nitrogen fixing trees as they pertain to latitude, climate, and nitrogen fixation strategies. In chapter 1, I develop a method to measure coupled nitrogen fixation and plant carbon exchange in real-time, non-destructively, continuously, and at the whole plant scale. This permits a study of the controls of nitrogen fixation rates over timescales that range from seconds to months. In chapter 2 and 3, I apply the method developed in chapter 1 to determine the temperature response of nitrogen fixation rates and the timescales over which nitrogen fixation is regulated. For chapter 2 and 3, I grew nitrogen fixing tree species of tropical and temperate origin and representing the two types of nitrogen fixing symbioses (rhizobial and actinorhizal) across a 10 °C gradient of growing temperatures. In chapter 2, I show that nitrogen fixation depends on growing temperature and geographic origin and peaks at 30-38 °C, which is 5-13 °C higher than previous estimates based on other nitrogen fixing symbioses and 3-7 °C higher than net photosynthesis. These findings have direct implications for how nitrogen fixation is represented in terrestrial biosphere models and are in direct contrast to terrestrial biosphere model predictions of a decline in tropical nitrogen fixation with warming associated with climate change. In chapter 3, I show that nitrogen fixation takes 1-3 weeks to be down-regulated by 50% following an alleviation of nitrogen limitation, 1-5 weeks to be up-regulated by 50% following the initiation of nitrogen fixation when nitrogen becomes limiting, and up to 4 months for nitrogen fixation to start following a drastic reduction in soil nitrogen supply. Theory says that time-lags in regulating nitrogen fixation start becoming important for plant competition and losses of available nitrogen from ecosystems if they are between 1 day and 1 week. Thus, time-lags on the order of multiple weeks are a significant cost of a facultative nitrogen fixation strategy and resolve the tropical nitrogen forest nitrogen paradox characterized by high losses of available nitrogen at the ecosystem scale in spite of down-regulation of nitrogen fixation at the individual scale. In chapter 4, I show that nitrogen fixing tree abundance is bimodal in all regions of the contiguous United States except the Northeast and that founder effects can explain this pattern and the persistence of nitrogen fixing trees in old forests. Using theory, I show that founder effects are most probable at intermediate soil nitrogen supply, when nitrogen fixers have a high relative capacity to uptake available nitrogen, and when nitrogen fixing trees are facultative in their nitrogen fixation strategy. These chapters provide a new tool for studying nitrogen fixation, critical data for improving terrestrial biosphere models and our understanding of how nitrogen fixation and nitrogen cycling varies across latitude and how it will change with climate change, and new theory for the successional trajectories of nitrogen fixers.

IPCC förespråkar i en rapport från 2018 att kolsänkor måste få en tydligare plats i samhället om vi ska lyckas nå klimatmålen. Grönstruktur är en typ av objekt som passivt tar upp koldioxid genom fotosyntes. Genom att nyttja detta i ett planeringsskede skapas ett verktyg som går att använda för att minska halten koldioxid i atmosfären. Däremot, så värderas ej de urbana grönytor som finns idag med avseende på detta, dock finns det många andra viktiga funktioner de uppfyller inom ramen för ekologisk hållbarhet. Detta examensarbete söker att besvara frågan hur det går att implementera kolsänkor vid värdering av grönytor samtidigt som den existerande värderingen för ekologisk hållbarhet bibehålls. Arbetet är utformat med grund ifrån litteraturstudie. Där ingår även några utvalda modeller som värderar grönytor eller behandlar koldioxid på något sätt. Dessa är Grönytefaktor, Citylab, Miljökonsekvensbeskrivning, BREEAM-SE, Miljöbyggnad, och Trafikverkets Klimatkalkyl. Som ett komplement har tre intervjuer utförts med relevanta praktiker för att ge värdefulla infallsvinklar och diskussionsunderlag. Därtill har även en workshop utförts av en tvärvetenskaplig samling aktörer, med samma huvudsakliga syfte som intervjuerna. Alla modeller har sina styrkor och brister. Med hänsyn till syftet för arbetet är vissa mer relevanta än andra. Exempelvis fungerar Grönytefaktor som ett verktyg för att kvantifiera ekologisk hållbarhet, men misslyckas med att motverka eventuella underliggande problem. Citylab är en av de mer kompletta guiderna, och Miljökonsekvensbeskrivning har visserligen lagstadgat stöd i viss mån, men saknar tydliga riktlinjer för implementeringen. Klimatkalkylen används främst för transportinfrastruktur, men den livscykelmetodik som modellen är baserad på kan ge bra indikationer på hur kolsänkekvalitéer kan beräknas. Livscykelmetodiken får även stöd från BREEAM-SE och Miljöbyggnad, samt att driftfasen för en grönyta måste beaktas eftersom det är där värdet för en kolsänka skapas. Det finns tyvärr inga tydliga svar på vilken väg som är bäst att gå. Det krävs mer forskning på området med en tydligare målbild med vad som ska uppnås, vem som ska använda modellen, samt vidare forskning om till exempel hur stor kolsänkekapacitet olika typer av grönstruktur har. Det finns dock visst stöd för att Citylab i kombination med Grönytefaktor kan vara en lämplig väg att gå, men först och främst krävs det mer forskning kring grönytors potential som kolsänka och vilken kapacitet de kan bidra med. / IPCC advocates in a report from 2018 that carbon sinks must be given a clearer role in society if we are to reach the climate change goals. Green structure is a type of object that passively can absorb carbon dioxide through photosynthesis. By using this in a planning stage, a tool is created that can be used as a way to bring down the levels of carbon dioxide in the atmosphere. The urban green areas that exists today are not being valuated with regards to this, although there are many other important functions they fulfill within the framework of ecological sustainability. The master thesis aims to answer the question how carbon sinks can be implemented in the valuation process of green areas simultaneously with ecological sustainability. The foundation of the work is made in the form of a literature study. Included in this are a few selected models that valuates green areas or carbon dioxide in some way. These are Grönytefaktor, Citylab, Miljökonsekvensbeskrivning, BREEAM-SE, Miljöbyggnad, och Trafikverkets Klimatkalkyl. As a complementary method, three interviews have been conducted with relevant practicians to provide valuable approach angles and discussion material. In addition to this, a workshop has been conducted with a gathering of interdisciplinary actors, with the same purpose as the interviews. All models have their own strengths and weaknesses. With regards to the aim of the work, some are more relevant than others. For instance, Grönytefaktor works as a good tool to quantify ecological sustainability but fail to counteract any underlying problems. Citylab is one of the more complete guides, and Miljökonsekvensbeskrivning has statutory support, but fails to include clear guidelines for implementation. Klimatkalkylen is primarily used for transport infrastructure, but its life-cycle methodology that it is based upon could give good indications for how carbon sink qualities can be calculated. The life-cycle methodology is also given support from BREEAM-SE and Miljöbyggnad, as well as the operating phase of a green area must be included since that is when the value of a carbon sink is created. Unfortunately, there are no clear answers which way is the best to go. More research is needed within the field where a clearer purpose of what is to be achieved, whom is supposed to use it, as well as other types of research such as how great the carbon sink capacity different types of green structure inhibits. However, there are certain arguments for using Citylab in combination with Grönytefaktor, but first and foremost there is a need for more research about green areas potential as a carbon sink and with what capacity they can contribute.

The global ocean currently absorbs over a third of anthropogenic carbon dioxide (CO₂) emissions, slowing down the growth of atmospheric CO₂, and thus moderating climate change. However, there is significant variability in the strength of the ocean carbon sink on interannual to decadal timescales. There are also uncertainties in the ocean carbon sink, a source of which lies in the coastal ocean. Coastal carbon fluxes are globally relevant and highly variable, but due to the paucity of observations, the coastal ocean remains largely unconstrained. Quantifying and understanding the variability of the ocean carbon sink, and constraining its uncertainties, is essential for supporting climate policy and predicting how the ocean will continue to moderate climate change in the future. This is challenging due to the complex physical and biogeochemical processes in the ocean, as well as the limited observations of ocean carbon. The goal of this thesis is to contribute to the understanding of the ocean carbon cycle and its variability with observations of CO₂ fluxes in the coastal ocean (Chapter 2), a multi-model study of surface carbon interannual variability (Chapter 3), and a mechanistic investigation of decadal variability of air-sea CO₂ fluxes in the global ocean (Chapter 4). (Chapter 2) Jamaica Bay is a hypereutrophic coastal urban estuary within the land-ocean aquatic continuum. Anthropogenic perturbations to the carbon cycle of the continuum are often excluded from global carbon budgets. Studies have shown that not accounting for the lateral transport of anthropogenic carbon through the continuum can lead to an overestimation of land carbon sinks and an underestimation of ocean carbon sinks. In this study, we used the direct covariance method to make direct estimates of CO₂ fluxes in Jamaica Bay. Over a 587-day observational study, Jamaica Bay emitted CO₂ to the atmosphere at an average rate of 130 gC/m2/yr. However, we find that the waters within the estuary are a strong CO₂ sink (-170 gC/m2/yr). Thus, on average, air-water CO₂ fluxes damp estuary emissions. We find that the water CO₂ sink is strongest in the summer due to the growth of intense algal blooms which likely drawdown CO₂ via photosynthesis. Although the direction of air-water CO₂ flux is ultimately a function of surface carbon concentrations, we find that in the summer, sea-breeze is a near-daily forcing agent for air-water CO₂ fluxes, contributing up to 43% of the mean summer water CO₂ sink rate. (Chapter 3) The El Nino-Southern Oscillation (ENSO) in the equatorial Pacific is the dominant mode of global air-sea CO₂ flux interannual variability (IAV). Air-sea CO2 fluxes are driven by the difference between atmospheric and surface ocean pCO₂, with variability of the latter driving flux variability. Previous studies found that models in Coupled Model Intercomparison Project Phase 5 (CMIP5) failed to reproduce the observed ENSO-related pattern of CO₂ fluxes and had weak pCO₂ IAV, which were explained by both weak upwelling IAV and weak mean vertical DIC gradients. We assess whether the latest generation of CMIP6 models can reproduce equatorial Pacific pCO₂ IAV by validating models against observations-based data products. We decompose pCO₂ IAV into thermally and non-thermally driven anomalies to examine the balance between these competing anomalies, which explain the total pCO₂ IAV. The majority of CMIP6 models underestimate pCO₂ IAV, while they overestimate SST IAV. Insufficient compensation of non-thermal pCO₂ to thermal pCO₂ IAV in models results in weak total pCO₂ IAV. We compare the relative strengths of the vertical transport of temperature and DIC and evaluate their contributions to thermal and non-thermal pCO₂ anomalies. Model-to-observations-based product comparisons reveal that modeled mean vertical DIC gradients are biased weak relative to their mean vertical temperature gradients, but upwelling acting on these gradients is insufficient to explain the relative magnitudes of thermal and non-thermal pCO₂ anomalies. (Chapter 4) The ocean carbon sink has absorbed about 25% of anthropogenic emissions, thus mitigating the effects of climate change. Over time, the ocean carbon sink has grown almost proportionally with the growth of atmospheric CO₂ concentrations. However, natural variability in the ocean carbon sink combined with large uncertainties, makes it hard to distinguish changes in the ocean sink due to natural variability versus the forced-trend. Thus, there is a need to understand and quantify the variability in the ocean carbon sink. Using the LDEO-Hybrid Physics Data product (1959-2020), we assess the decadal variability of global air-sea CO₂ fluxes. Here, we compare regional contributions to the decadal variability of the global ocean carbon sink and evaluate global patterns of decadal changes to elucidate the mechanisms that drive the dominant mode of global air-sea CO₂ flux decadal variability. We find that the dominant mode of decadal air-sea CO₂ flux variability exhibits strong synchronous signals over the tropical Pacific and Southern Ocean. We suggest that the synchronicity between the tropical Pacific and the Southern Ocean is modulated by the Pacific Decadal Oscillation (PDO) index, which is connected to the Multivariate ENSO Index (MEI). The composite patterns over the tropical Pacific can be explained by ENSO-like mechanisms operating on the decadal timescale, while the composite patterns over the Southern Ocean show a different regime where the westerly winds weakened over the composite period, the mixed layer shoaled, and the Southern Ocean sink weakened. Using a box model, we show that this reduction in mixed layer entrainment drives an accumulation of DIC in the mixed layer, which, when amplified by the high Revelle factor in the Southern Ocean, results in a 14-fold amplification in the surface pCO₂, reducing the ocean's capacity to uptake CO₂.

Thesis (Ph. D.)--Joint Program in Marine Geology and Geophysics (Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences; and the Woods Hole Oceanographic Institution), 2012. / Cataloged from PDF version of thesis. / Includes bibliographical references. / Determining timescales of the formation and preservation of carbonate alteration products in mantle peridotite is important in order to better understand the role of this potentially important sink in the global carbon cycle and also to evaluate the feasibility of using artificially-enhanced, in situ formation of carbonates in peridotite to mitigate the buildup of anthropogenic CO₂ emissions in the atmosphere. Timescales of natural carbonation of peridotite were investigated in the mantle layer of the Samail Ophiolite, Sultanate of Oman. Rates of ongoing, low-temperature CO₂ uptake were estimated through ¹⁴C and ²³⁰Th dating of carbonate alteration products. Approximately 1-3 x 10⁶ kg CO₂/yr is sequestered in Ca-rich surface travertines and approximately 10⁷ kg CO₂/yr is sequestered in Mg-rich carbonate veins. Rates of CO₂ removal were estimated through calculation of maximum erosion rates from cosmogenic 3He measurements in partially-serpentinized peridotite bedrock associated with carbonate alteration products. Maximum erosion rates for serpentinized peridotite bedrock are ~5 to 180 m/Myr (average: ~40 m/Myr), which removes at most 10⁵-10⁶ kg CO₂/yr through erosion of Mg-rich carbonate veins. / by Evelyn Martinique Mervine. / Ph.D.

Thesis (M.E.S.)--The Evergreen State College, 2007. / Title from title screen viewed 1/17/2008. Includes bibliographical references (p. 123-126).

Thesis (M.E.S.)--The Evergreen State College, 2007. / Title from title screen (viewed 1/24/2008). Includes bibliographical references.

The ocean is the only cumulative sink of atmospheric CO2. It has absorbed approximately 40% of the CO2 from fossil fuel burning and cement production, lowering atmospheric CO2 and limiting climate change. Here we will examine the regional and global mechanisms controlling the evolution of ocean uptake of this additional carbon from human activities (anthropogenic carbon, Cant) using ocean models and observations. Cant is rapidly injected into the deep ocean, sequestering it from the atmosphere for centuries. It is currently uncertain whether any of this sequestered Cant was absorbed from the atmosphere in the subpolar North Atlantic. Here we present evidence that the upper limb of the ocean’s overturning circulation supplies the subpolar North Atlantic with capacity to absorb Cant from the atmosphere. Using a coupled ocean model, we find that surface freshening of the subpolar North Atlantic reduces the volume available for Cant storage. We also investigate whether global ocean Cant uptake is reduced due to changing ocean circulation, this time across multiple emission scenarios, including scenarios with aggressive emission mitigation. Though it is clear that emission mitigation will reduce the magnitude of the ocean carbon sink, the mechanisms governing the decline in uptake have not been studied in detail. We find that the ocean sink becomes less efficient due to kinematic effects wherein Cant escapes from the surface ocean as atmospheric CO2 plateaus and then declines. In emission scenarios ranging from high to low emissions, projected changes in global Cant uptake due to ocean circulation are small. This is in contrast with the subpolar North Atlantic, where future circulation change plays a important role in the declining Cant uptake.

With warming temperatures, Arctic ecosystems are changing from a net sink to a net sourceof carbon to the atmosphere, but the Arctic’s carbon balance remains highly uncertain. Landscapes are often assumed to be homogeneous when interpreting eddy covariance carbon fluxes, which can lead to biases when gap-filling and scaling-up observations to determine regional carbon budgets. Tundra ecosystems are heterogeneous at multiple scales. Plant functional types, soil moisture, thaw depth, and microtopography, for example, vary across the landscape and influence carbon dioxide (CO₂) and methane (CH4) fluxes. In Chapter 2, I reported results from growing season CO₂ and CH₄ fluxes from an eddy covariance tower in the Yukon-Kuskokwim (YK) Delta in Alaska. I used flux footprint models and Bayesian Markov Chain Monte Carlo (MCMC) methods to unmix eddy covariance observations into constituent landcover fluxes based on high resolution landcover maps of the region. I compared three types of footprint models and used two landcover maps with varying complexity to determine the effects of these choices on derived ecosystem fluxes. I used artificially created gaps of withheld observations to compare gap-filling performance using our derived landcover-specific fluxes and traditional gap-filling methods that assume homogeneous landscapes. I also compared regional carbon budgets scaled up from observations using heterogeneous and homogeneous approaches. Gap-filling methods that accounted for heterogeneous landscapes were better at predicting artificially withheld gaps in CO₂ fluxes than traditional approaches, and there were only slight differences performance between footprint models and landcover maps. I identified and quantified hot spots of carbon fluxes in the landscape (e.g., late growing season emissions from wetlands and small ponds). I resolved distinct seasonality in tundra growing season CO₂ fluxes. Scaling while assuming a homogeneous landscape overestimated the growing season CO₂ sink by a factor of two and underestimated CH₄ emissions by a factor of two when compared to scaling with any method that accounts for landscape heterogeneity. I showed how Bayesian MCMC, analytical footprint models, and high resolution landcover maps can be leveraged to derive detailed landcover carbon fluxes from eddy covariance timeseries. These results demonstrate the importance of landscape heterogeneity when scaling carbon emissions across the Arctic. Climate change is causing an intensification in tundra fires across the Arctic, including the unprecedented 2015 fires in the YK Delta. The YK Delta contains extensive surface waters (approximately 33% cover) and significant quantities of organic carbon, much of which is stored in vulnerable permafrost. Inland aquatic ecosystems act as hot-spots for landscape CO₂ and CH₄ emissions and likely represent a significant component of the Arctic carbon balance, yet aquatic fluxes of CO₂ and CH₄ are also some of the most uncertain. In Chapter 3, I measured dissolved CO₂ and CH₄ concentrations (n = 364), in surface waters from different types of waterbodies during summers from 2016 to 2019. I used Sentinel-2 multispectral imagery to classify landcover types and area burned in contributing watersheds. I developed a model using machine learning to assess how waterbody properties (size, shape, and landscape properties), environmental conditions (O₂ concentration, temperature), and surface water chemistry (dissolved organic carbon composition, nutrient concentrations) help predict in situ observations of CO₂ and CH₄ concentrations across deltaic waterbodies. CO₂ concentrations were negatively related to waterbody size and positively related to waterbody edge effects. CH₄ concentrations were primarily related to organic matter quantity and composition. Waterbodies in burned watersheds appeared to be less carbon limited and had longer soil water residence times than in unburned watersheds. My results illustrated the importance of small lakes for regional carbon emissions and demonstrate the need for a mechanistic understanding of the drivers of greenhouse gasses in small waterbodies. In the Arctic waterbodies are abundant and rapid thaw of permafrost is destabilizing the carbon cycle and changing hydrology. It is particularly important to quantify and accurately scale aquatic carbon emissions in arctic ecosystems. Recently available high-resolution remote sensing datasets capture the physical characteristics of arctic landscapes at unprecedented spatial resolution. In Chapter 4, I demonstrated how machine learning models can capitalize on these spatial datasets to greatly improve accuracy when scaling waterbody CO₂ and CH₄ fluxes across the YK Delta of south-west AK. I found that waterbody size and contour were strong predictors for aquatic CO₂ emissions, attributing greater than two-thirds of the influence to the scaling model. Small ponds (<0.001 km²) were hotspots of emissions, contributing fluxes several times their relative area, but were less than 5% of the total carbon budget. Small to medium lakes (0.001–0.1 km²) contributed the majority of carbon emissions from waterbodies. Waterbody CH₄ emissions were predicted by a combination of wetland landcover and related drivers, as well as watershed hydrology, and waterbody surface reflectance related to chromophoric dissolved organic matter. When compared to my machine learning approach, traditional scaling methods that did not account for relevant landscape characteristics overestimated waterbody CO₂ and CH₄ emissions by 26%–79% and 8%–53% respectively. This chapter demonstrated the importance of an integrated terrestrial-aquatic approach to improving estimates and uncertainty when scaling carbon emissions in the arctic. In order to understand carbon feedbacks with the atmosphere and predict climate change, we need to develop methods to model and scale up carbon emissions. Gridded datasets of carbon fluxes are used to benchmark Earth system models, attribute changes in rates of atmospheric concentrations of greenhouse gases, and project future climate change. There are two main approaches to deriving gridded datasets of carbon fluxes and global or regional carbon budgets: bottom-up scaling, and top-down atmospheric inversions. There is often divergence between approaches, with carbon budgets calculated from bottom-up and top-down studies rarely overlapping. The resulting uncertainty in carbon budgets calculated from either approach is more pronounced in high-latitudes. One of the challenges with combining bottom-up models and comparing top-down models is the variable spatial resolutions used in each approach. In Chapter 5, I applied flux scaling models from earlier chapters to create bottom-up carbon budgets at very high resolution (10 m) for the entire YK Delta domain. I used ERA5 land reanalysis data to extend the flux models to 2012-2015 and 2017 growing seasons to coincide with airborne observations of atmospheric CO₂ and CH₄ concentrations from NASA CARVE and Arctic-CAP campaigns. I progressively coarsened remote sensing imagery for the region to 30 m, 90 m, 250 m, and 1 km to create coarser landcover maps and corresponding bottom-up carbon budgets. The high resolution bottom-up models, when convolved with concentration footprints, produced simulated atmospheric enhancements that were similar to observed atmospheric enhancements. There was little change coarsening to 30 m and 90 m, but simulated atmospheric enhancements and especially carbon budgets were quite different at 250 m and 1 km spatial resolution. The changes with resolution were largely the result of an increase in area mapped as wetlands and shrub tundra, and less area mapped as small waterbodies and lichen tundra. Coarser resolution bottom-up scaling consistently overestimated CH4 budgets. By evaluating flux models against atmospheric observations, I was able to diagnose missing components such as inland water carbon emissions and times when the scaling models overestimated emissions to missing seasonal dynamics. This dissertation combined novel uses of statistical techniques with a high density of field observations to yield process-level understanding of carbon cycling that could be applied to scaling-up carbon emissions. By merging terrestrial and aquatic perspectives and concurrently mapping ecosystem landcovers and disturbances at high spatial resolution, I avoided common sources of uncertainty in carbon budgets such as double-counting of areas. I investigated how we represent the landscape in terms of both spatial resolution and the level of landscape heterogeneity, and determined the effects of these choices on carbon fluxes and budget estimates. By comparing to the atmosphere, I evaluated the validity of different approaches to modeling carbon fluxes in the Arctic. Together, the chapters in this dissertation provided a holistic study of carbon cycling in the Arctic.

Accurately modeling climate and the impacts of climate change relies heavily on extensive observations. Soil moisture is a critical variable in this regard, as it influences energy partitioning, regulates the water cycle, directly affects vegetation dynamics, modulates terrestrial carbon sinks and sources, and overall plays a vital role in the land-atmosphere interactions and feedback. This work aims to improve the quality of available surface soil moisture data and its complementary dataset -- vegetation optical depth (since both are derived from the same satellite measurements). The datasets developed in the scope of this study fill the gap in the available observational data pool as unique, long-term, consistent datasets developed based on remote sensing data. These datasets were created with the help of machine learning tools, in particular, deep dense neural networks. The distinctive characteristics of the utilized approach include (1) decomposition of the signal into seasonal and residual parts and training a neural network to match the residuals; (2) applying a special transfer learning training scheme that allows adjusting the features of a trained neural network to a slightly different input that ultimately permits merging the non-compatible directly and disjoint satellite sources into a consistent dataset; (3) using an ensemble of neural networks to assess the data uncertainty. Upon development, the datasets were profoundly validated vs. in-situ soil moisture measurements for soil moisture and biomass and photosynthesis-related datasets for vegetation optical depth. The consistent and long-term nature of the created datasets allowed for the study of decadal trends in soil moisture and the potential drivers for its dynamics. Finally, this study presents two showcases of the datasets used for constraining models -- as data assimilated in a simple carbon cycle model and as an emergent constraint in an ensemble of global climate models. The vegetation optical depth dataset was used in a simple carbon cycle model and demonstrated how it can constrain unobserved respiration flux and carbon pools. In this project's scope, the role of information content, data quality, and local conditions is assessed. The soil moisture dataset is used to constrain global climate models' projections of future soil moisture change by constraining the past soil moisture change range. Altogether, this study proposes a robust methodology for merging data from different sources into a consistent long-term dataset (provided that at least a short overlap in data exists for transfer learning). The analysis of the soil moisture dataset reveals that the regions of drying and wetting dynamics exist globally and can be identified with statistically significant trends in soil moisture. The dynamics are studied seasonally, revealing the contradicting trends in soil moisture in some regions (for example, in Europe, wetting in spring and drying in summer) and persistent trends throughout the year for others (for example, drying in the Mediterranean). Similarly, the local drivers of the soil moisture change are established. The soil moisture change is mainly driven by variations in precipitation for dry regions and in temperature in wet regions with the rising role of vegetation dynamics, especially in high latitudes. Finally, the vegetation optical depth data has proven its high potential in constraining respiration flux and carbon pools, significantly improving the carbon cycle model predictions in the regions subjected to interannual variability in meteorological forcing conditions and vegetation response.

Atmospheric carbon dioxide levels have been steadily increasing from anthropogenic energy production, development and use. Carbon cycling in the terrestrial biosphere, particularly forest ecosystems, has an important role in regulating atmospheric concentrations of carbon dioxide. US West coast forest management policies are being developed to implement forest bioenergy production while reducing risk of catastrophic wildfire. Modeling and understanding the response of terrestrial ecosystems to changing environmental conditions associated with energy production and use are primary goals of global change science. Coupled carbon-nitrogen ecosystem process models identify and predict important factors that govern long term changes in terrestrial carbon stores or net ecosystem production (NEP). By quantifying and reducing uncertainty in model estimates using existing datasets, this research provides a solid scientific foundation for evaluating carbon dynamics under conditions of future climate change and land management practices at local and regional scales. Through the combined use of field observations, remote sensing data products, and the NCAR CESM/CLM4-CN coupled carbon-climate model, the objectives of this project were to 1) determine the interactive effects of changing environmental factors (i.e. increased CO���, nitrogen deposition, warming) on net carbon uptake in temperate forest ecosystems and 2) predict the net carbon emissions of West Coast forests under future climate scenarios and implementation of bioenergy programs. West Coast forests were found to be a current strong carbon sink after accounting for removals from harvest and fire. Net biome production (NBP) was 26 �� 3 Tg C yr�����, an amount equal to 18% of Washington, Oregon, and California fossil fuel emissions combined. Modeling of future conditions showed increased net primary production (NPP) because of climate and CO��� fertilization, but was eventually limited by nitrogen availability, while heterotrophic respiration (R[subscript h]) continued to increase, leading to little change in net ecosystem production (NEP). After accounting for harvest removals, management strategies which increased harvest compared to business-as-usual (BAU) resulted in decreased NBP. Increased harvest activity for bioenergy did not reduce short- or long-term emissions to the atmosphere regardless of the treatment intensity or product use. By the end of the 21st century, the carbon accumulated in forest regrowth and wood product sinks combined with avoided emissions from fossil fuels and fire were insufficient to offset the carbon lost from harvest removals, decomposition of wood products, associated harvest/transport/manufacturing emissions, and bioenergy combustion emissions. The only scenario that reduced carbon emissions compared to BAU over the 90 year period was a 'No Harvest' scenario where NBP was significantly higher than BAU for most of the simulation period. Current and future changes to baseline conditions that weaken the forest carbon sink may result in no change to emissions in some forest types. / Graduation date: 2013