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Rising anthropogenic emissions of radiatively active greenhouse gases and particulate matter (PM) are altering Earth's climate, increasing human and ecosystem health risks, and inducing feedbacks from terrestrial and marine ecosystems on future atmospheric carbon dioxide (CO2) levels and PM concentrations. Process-based Earth system models (ESMs) and regional climate and chemistry transport models offer the best approach for quantifying these feedbacks and their uncertainties, projecting future atmospheric CO2 levels and resulting temperature increases and wildfire risks, predicting hazardous PM concentrations and human health risks, and understanding the impacts of potential mitigation efforts. In this dissertation, I address these globally significant environmental issues through three studies designed to highlight biases in global ESM vegetation distributions, investigate terrestrial carbon cycle feedbacks from solar radiation management (SRM) climate change mitigation, and explore impacts of future regional wildfire emissions on ozone (O3) and fine (≤2.5 micrometers) particulate matter (PM2.5) due to unmitigated climate change.In the first study, I analyzed CO2 mole fraction-driven simulations of ESMs from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) and found that ESMs exhibited large biases in forest distribution, fraction, and biomass in leaves, wood, and roots. These biases induced an uncertainty of −20 Pg C to 135 Pg C in forest total biomass estimates over northern extratropical regions in ESMs, influencing estimates of carbon cycle feedbacks, fuel loads and distributions, and, thus, wildfire risk. In the second study, I found terrestrial ecosystems became a stronger carbon sink, adding 79 Pg C stored on land, under a SRM strategy designed to maintain global surface temperature at 2020 levels for the remainder of the twenty-first century. While fuel loads were increased, wildfire risks were reduced by mitigating increases in global temperature. In the third study, I found, by employing global climate and chemistry-transport model output to force the Community Multiscale Air Quality model, increased fire intensity between contemporary (2003-2010) and future years (2050-2059) had little effect on atmospheric O3 concentrations in the Western United States, but projected PM2.5 concentrations induced by fire could be as much as 21 times higher in future years in this region.
This weather guide includes detailed specifications for locating and instrumenting fire weather stations, taking weather observations, and overwintering the Drought Code component of the FWI System. The sensitivity of the FWI System components to weather elements is represented quantitatively. The importance of weather that is not directly observable is discussed in the context of fuel moisture and fire behavior. Current developments in the observation and measurement of fire weather and the forecasting of fire danger are discussed, along with the implications for the reporting of fire weather of increasingly automated fire management information systems.
Resource management information systems. The Glacer National Park study. Subsequent applications of gradient modeling systems.
Understanding and quantifying wildfire behavior is of interest to the scientific community, as well as public health and fire management professionals. To achieve this end, there is a demand for statistical descriptions of wildfire behavior and its relationship to the environment. However, wildfire behavior can be complex, described by multiple characteristics such as final size, duration and growth rates, and influenced by processes that can be regionally dependent. Further challenges arise due to the poor quality and availability of cumulative burn area time series data, which often contain missing and erroneous measurements. To address these issues, a variety of methods are presented. Multiple wildfire behaviors are represented using a simple decomposition of cumulative burn area time series that measures four meaningful quantities from the growth curve. The relationship between wildfire activity and the environment are approximated using regionally specific generalized linear models. Weather and landscape data are used to predict various measures of wildfire behavior. Validation results suggested that most of the models generalized well to independent data, and have potentially useful applications in climatological research. Data quality issues common to cumulative burn area time series are addressed using Bayesian state-space models, which reconstruct growth curves from multiple corrupted burn area time series. Two state space models are presented, a stationary version that assumes idealized fire growth, and a non-stationary version that produces reconstructions with time-varying growth rates. The relative computational costs and goodness-of-fit is illustrated by reconstructing the growth curves of 13 wildfires from 2014 wildfire season using growth data coming from two sources, fire perimeters from the Geospatial Multi-Agency Coordination (GeoMAC) and cumulative hotspot detects from the Hazard Mapping System (HMS). The stationary model had minimal computational costs, but rarely produced adequate descriptions of the burn area observations. The non-stationary model had much higher computational costs, but produced realistic estimates of the time series. An informal sensitivity analysis suggested that the reconstructed curves would be robust to changes in the priors. The main application of the state-space models is to reconstruct burn area time series, which can in turn be used for statistical analysis or validation of currently existing growth models. The framework can be modified for other purposes as well including forecasting burn area, and predicting the extinguishment date of a fire.
Abstract : The Great Lakes of North America are the largest surface freshwater system in the world and many ecosystems, industries, and coastal processes are sensitive to the changes in their water levels. The recent changes in the Great Lakes climate and water levels have particularly highlighted the importance of water level prediction. The water levels of the Great Lakes are primarily governed by the net basin supplies (NBS) of each lake which are the sum of over-lake precipitation and basin runoff minus lake evaporation. Recent studies have utilized Regional Climate Models (RCMs) with a fully coupled one-dimensional (1D) lake model to predict the future NBS, and the Coordinated Great Lakes Regulating and Routing Model (CGLRRM) has been used to predict the future water levels. However, multiple studies have emphasized the need for a three-dimensional (3D) lake model to accurately simulate the Great Lakes water budget. Therefore, in this study, we used the Great Lakes-Atmosphere Regional Model (GLARM) along with the Large Basin Runoff Model (LBRM) and CGLRRM to predict the changes in NBS and water levels by the mid- and late twenty-first century. GLARM is a 3D regional climate modeling system for the Great Lakes region that is fully coupled to a 3D hydrodynamic lake and ice model. This is the first study to use such an advanced model for water level prediction in the Great Lakes. We found that both annual over-lake precipitation and basin runoff are most likely to increase into the future. We also found that annual lake evaporation is most likely to decrease in Lake Superior but increase in all the other lakes. We posit that the decreases in evaporation are due to decreased wind speed over the lakes and decreased difference between saturated and actual specific humidity over the lakes. Our predicted changes in the three components of NBS would lead to mostly increased NBS and water levels in the future. The ensemble average of our predicted water level changes for Lake Superior, Michigan-Huron, and Erie are +0.14 m, +0.37 m, and +0.23 m by the mid-twenty-first century, respectively, and +0.47 m, +1.29 m, and +0.80 m by the late twenty-first century, respectively. However, due to the multiple sources of uncertainties associated with climate modeling and predictions, the water level predictions from this study should not be viewed as exact predictions. These predictions are unique to our model configuration and methodology. Other studies can easily predict different water level changes through the use of different models and methodologies. Therefore, more predictions from advanced modeling systems like GLARM are needed to generate a consensus on future water level changes in the Great Lakes.