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  • ItemUnknown
    Comparing precipitation estimates, model forecasts, and random forest based predictions for excessive rainfall
    (Colorado State University. Libraries, 2023) James, Eric, author; Schumacher, Russ, advisor; Bell, Michael, committee member; Van Leeuwen, Peter Jan, committee member; Morrison, Ryan, committee member
    Flash flooding is an important societal challenge, and improved tools are needed for both real-time analysis and short-range forecasts. We present an evaluation of threshold exceedances of quantitative precipitation estimate (QPE) and forecast (QPF) datasets in terms of their degree of correspondence with observed flash flood events over a seven-year period. We find that major uncertainties persist in QPE for heavy rainfall. In general, comparison with flash flood guidance (FFG) thresholds provides the best correspondence, but fixed thresholds and average recurrence interval thresholds provide the best correspondence in certain regions of the contiguous US (CONUS). QPF threshold exceedances from the High-Resolution Rapid Refresh (HRRR) generally do not correspond as well as QPE exceedances with observed flash floods, except for the 1-h duration in the southwestern CONUS; this suggests that high-resolution model QPF may be a better indicator of flash flooding than QPE in some poorly observed regions. Subsequently, we describe a new random forest (RF) based excessive rainfall forecast system using predictor information from the 3-km operational HRRR. Experiments exploring the use of spatial predictor information reveal the importance of averaging HRRR predictor fields across a spatial radius rather than using only information from sparse input grid points for regimes with small-scale excessive rain events. Tree interpreter results indicate that the forecast benefits of spatial aggregation stem from greater contributions provided by storm attribute predictors. Forecasts are slightly degraded when there is a mismatch between the trained RF model and the daily HRRR forecasts to which the model is applied, both in terms of initialization time and HRRR model version. Use of FFG as an additional predictor leads to forecast improvements, highlighting the potential of hydrologic information to contribute to forecast skill. In addition, averaging predictor information across several HRRR initializations leads to a statistically significant improvement in forecasts relative to using predictor fields from a single HRRR initialization. The HRRR-based RF has been evaluated at the annual Flash Flood and Intense Rainfall Experiment (FFaIR) over the past three years, with year-over-year improvements stemming from the results of sensitivity experiments. The HRRR-based RF represents an important baseline for future machine learning based excessive rainfall forecasts based on convection-allowing models.
  • ItemEmbargo
    Marine ice nucleating particles: sources, composition, emissions, and model parameterizations
    (Colorado State University. Libraries, 2023) Moore, Kathryn A., author; Kreidenweis, Sonia M., advisor; DeMott, Paul J., advisor; Farmer, Delphine K., committee member; Pierce, Jeffrey R., committee member; van den Heever, Susan C., committee member
    Sea spray aerosol has received increasing attention over the last decade as a source of ice nucleating particles (INPs) to the atmosphere. Sparse measurements in remote marine regions indicate both marine INP concentrations and ice nucleating efficiency are several orders of magnitude lower than those of mineral or soil dusts, which dominate the INP budget on a global scale. The Southern Ocean (SO) surrounding Antarctica is thought to be the only region where marine INPs are the predominant INP type due to its remoteness from continental and anthropogenic aerosol sources and persistent strong westerlies, although several recent studies have suggested this may also be true of the high Arctic seasonally or intermittently. INPs are critical for initiating cloud glaciation at temperatures warmer than ~-36 °C and can thus have an outsize effect on cloud phase and related climate feedbacks due to their relative scarcity. This is particularly true over the polar oceans, where low and mid-level mixed phase and supercooled clouds are ubiquitous and especially sensitive to aerosols due to the generally low background particle concentrations. The research presented here aimed to improve our understanding of the factors influencing marine INP emissions and the sources and composition of INPs in remote marine regions, as well as to evaluate and improve current INP model parameterizations. This was accomplished using observations made in the Southern Ocean, one of the few remaining pristine aerosol environments, during the Southern Ocean Cloud Radiation Aerosol Transport Experimental Study (SOCRATES) aircraft campaign on the NSF/NCAR G-V, and the second Clouds, Aerosols, Precipitation, Radiation and atmospherIc Composition Over the southeRN ocean (CAPRICORN-2) ship campaign on the R/V Investigator in 2018. Ambient observations were supplemented by measurements from the CHaracterizing Atmosphere-Ocean parameters in SOARS (CHAOS) mesocosm experiment in the new Scripps Ocean-Atmosphere Research Simulator (SOARS) wind-wave channel. CHAOS measurements allowed for isolation of the role of wind speed in marine INP production, which had not previously been characterized through controlled experiments. SOCRATES and CAPRICORN-2 are notable for collecting the first vertically resolved INP measurements over the Southern Ocean, including the first in situ observations in and above cloud in the region. Both aerosol and INP concentrations showed excellent agreement between G-V and R/V Investigator observations during overflights of the ship, supporting the use of such a multi-platform measurement approach for future campaigns interested in aerosol and INP vertical profiles. New techniques for estimating marine aerosol surface area and the number of particles >0.5 μm, key quantities often used in INP parameterizations, were developed based on lidar and nephelometer measurements. An additional parameterization for marine INPs is proposed, which uses both wind speed and activation temperature, and reduces bias compared to the existing parameterization based solely on temperature. Marine boundary layer (MBL) and above cloud INP concentrations from the same SOCRATES flight support the hypothesis suggested by several modeling studies that marine INPs dominate at low altitudes, and mineral dust becomes increasingly important with height. Unexpectedly, enhanced INP and aerosol iron concentrations, but low iron solubilities, were observed for samples collected south of 60 °S during CAPRICORN-2. Antarctica is suggested as a potential source of both biological and inorganic INPs to the Southern Ocean marine boundary layer through the emission of mineral and soil dusts from ice-free areas. Similar high latitude dust sources in Iceland and Svalbard have been observed to contribute to INPs in the Arctic atmosphere, and are anticipated to increase in importance as the climate warms.
  • ItemOpen Access
    Using laboratory and airborne measurements to investigate the role of ice nucleating particles in ice and mixed-phase clouds
    (Colorado State University. Libraries, 2023) Patnaude, Ryan John, author; Kreidenweis, Sonia M., advisor; DeMott, Paul J., advisor; van den Heever, Susan C., committee member; Chui, J. Christine, committee member; Willis, Megan D., committee member
    Ice may be present in the atmosphere either in cirrus or mixed-phase cloud regions, each with their own distinctly different characteristics and formation mechanisms. The former is characterized by the presence of only ice crystals at temperatures < -38 °C, while the latter includes the coexistence of both supercooled liquid cloud droplets and ice crystals between temperatures of 0 °C and -38 °C. Cirrus clouds represent an important cloud type as they are ubiquitous in the atmosphere and their radiative effects depend upon their microphysical properties. Their formation mechanisms may proceed via homogeneous or heterogeneous nucleation, and whether one or the other or both occur determines the size and number of ice crystals. The ocean represents one of the largest sources of aerosols into the atmosphere, and sea spray aerosols (SSA), if they are lofted to the upper troposphere, may act as ice nucleating particles (INPs) to initiate heterogeneous nucleation under cirrus conditions. Although a number of previous studies have investigated the ice nucleating behavior of SSA proxies such as sodium chloride (NaCl), or SSA generated from commercially-available artificial seawater products, ice nucleation under cirrus conditions of SSA generated from natural seawater had not been examined at the inception of this research program. Additionally, whether secondary marine aerosols (SMA), which form via the gas-to-particle conversion of ocean-emitted gas-phase species, may act as an INP in cirrus clouds is currently unknown. The first half of this dissertation highlights two laboratory studies that investigated the role and characteristics of SSA and SMA to act as INPs at cirrus cloud temperatures. The first study compared ice nucleation results for submicron SSA and NaCl particles and examined whether particle size affected the low temperature ice nucleation. Results showed that both SSA and NaCl initiated heterogeneous nucleation strongly at temperatures below 220 K, and that the size of the particles did not affect the ice nucleating ability of SSA. The similarities between the freezing behaviors of SSA and NaCl particles suggested the salt components were controlling heterogeneous ice nucleation. The second study used a more realistic aerosol generation method, utilizing a Marine Aerosol Reference Tank (MART) that was filled with natural seawater, and investigated the effects of atmospheric oxidation on SSA using an oxidation flow reactor (OFR), which was also used to generate SMA from gaseous emissions released in the MART. SMA alone were also examined for their ice nucleation behavior at cirrus temperatures. Results from this study indicated that atmospheric oxidation did not hinder low temperature ice nucleation of SSA, and that SMA are not efficient ice nucleating particles at cirrus temperatures, but could participate in homogeneous nucleation. Finally, the similarities between the findings from the two studies indicated that the generation method of SSA, and any impacts on SSA organic aerosol content, did not affect the ice nucleating behavior of SSA at cirrus temperatures. Ice in mixed-phase clouds (MPCs), on the other hand, forms initially via heterogeneous nucleation at a wide range of temperatures and relative humidity conditions, depending on the abundance and characteristics of available INPs. Secondary ice production (SIP) may follow heterogeneous nucleation in MPCs, where new ice crystals form either during the heterogeneous freezing event, or through subsequent interactions between the pre-existing liquid cloud droplets and ice crystals. SIP may lead to enhanced ice crystal number concentrations via a number of proposed mechanisms, especially in convective environments. Despite decades of study toward developing better understanding of ice formation in MPCs, the freezing pathways of ice crystals over the course of cloud lifetimes, and the conditions that favor the various proposed SIP pathways, are not fully resolved. The third study in this dissertation reports and interprets observations of INPs during an airborne campaign over the U.S. Central Great Plains during the Secondary Production of Ice in Cumulus Experiment (SPICULE) campaign that primarily sampled cumulus congestus clouds. Coincident measurements of INP and ice crystal number concentrations in cumulus congestus clouds were used to infer the ice formation pathway, either through heterogeneous nucleation or SIP. Warmer cloud base temperatures and faster updrafts were found to facilitate environmental conditions favorable for SIP. Further, the fragmentation of freezing droplets (FFD) SIP mechanism was found to be critical in the enhancement of observed ice crystal number concentrations during the earliest stages of the cloud lifetime. Numerical model simulations of an idealized, single congestus cloud, designed to mimic the clouds sampled during SPICULE, were conducted with newly-implemented SIP mechanisms, added to the existing Hallet-Mossop (HM) rime-splintering mechanism. The model results indicated that HM dominated the production of ice crystals, but without the FFD and ice-ice collisional breakup (BR) SIP mechanisms, the model could not accurately resolve ice crystal number concentrations compared to observations. Competing results in the dominant SIP mechanisms underscore the need for improved mechanistic understanding of these SIP processes, either through laboratory or observational studies, in order to close this gap between model prediction and observations. The final portion of this dissertation describes airborne observations of INPs during a field campaign along the U.S. Gulf Coast, also aimed at investigating the impacts of various aerosol-cloud interaction mechanisms on development of convective clouds. During this campaign, a widespread and prolonged Saharan Air Layer (SAL) event took place and INP characteristics during this event are reported and contrasted with INP characteristics prior to the arrival of the SAL. The INP concentrations at temperatures below -20 °C were enhanced by 1–2 orders of magnitude compared to the flights prior to the dust intrusion, and showed good agreement with one previous study of Saharan dust near Barbados, but lower INP concentrations than another study off the coast of western Africa. The INP concentrations in the SAL also generally overlapped with or exceeded INP concentrations during SPICULE, but only for INPs active temperatures < -25 °C. These observations were the first airborne measurements in nearly two decades tagging INP concentrations to North African dust that had been transported all the way to the United States. Further, they provide the most comprehensive description of these INPs yet recorded, and suggest a common natural INP perturbation in the southeastern U.S. and Gulf regions in early summer, with implications for cloud processes that warrant further study.
  • ItemOpen Access
    When is the unpredictable (slightly more) predictable? An assessment of opportunities for skillful decadal climate prediction using explainable neural networks
    (Colorado State University. Libraries, 2023) Gordon, Emily M., author; Barnes, Elizabeth A., advisor; Hurrell, James W., committee member; Rugenstein, Maria, committee member; Anderson, Charles, committee member
    Predicting climate variability on decadal (2-10 year) timescales can have huge implications for society because it can provide better estimates of both global trends as well as regional climate variability for crucial, actionable lead times. The key to skillful decadal prediction is understanding and predicting oceanic variability. However, predictable signals in the ocean can be masked by the inherent noise in the system, and therefore, skillful prediction on decadal timescales is challenging. Machine learning, with its ability to extract nonlinear signals from large sets of noisy data, has been shown a powerful tool for predicting and understanding processes across weather and climate applications. In this dissertation, I explore applications of machine learning to decadal prediction. First, I present a machine learning approach to predicting the Pacific decadal oscillation (PDO) with artificial neural networks (ANNs) within the Community Earth System Model version 2 (CESM2) pre-industrial control simulation. Predicting PDO transitions and understanding the associated mechanisms has proven a critical but challenging task in climate science. As a form of decadal variability, the PDO is associated with both large- scale climate shifts and regional climate predictability. I show that ANNs predict PDO persistence and transitions at lead times of 12 months onward. Using layer-wise relevance propagation to investigate the ANN predictions, I demonstrate that the ANNs utilize oceanic patterns that have been previously linked to predictable PDO behavior. ANNs recognize a build-up of ocean heat content in the off-equatorial western Pacific 12–27 months before a transition occurs. The ANNs also distinguish transition mechanisms between positive-to-negative sign transitions, and negative-to-positive transitions. Secondly, I demonstrate a technique for incorporating an uncertainty estimate into the prediction of a regression neural network, allowing the identification of predictable sea surface temperature (SST) anomalies on decadal timescales in the CESM2 pre-industrial control simulation. Predictability in SSTs can be masked by unpredictable variability, and one approach to extracting predictable signals is to investigate state-dependent predictability – how differences in prediction skill depend on the initial state of the system. I leverage the network's prediction of uncertainty to examine state-dependent predictability in SSTs by focusing on predictions with the lowest uncertainty. In particular, I study two regions of the global ocean–the North Atlantic and North Pacific–and find that skillful initial states identified by the neural network correspond to particular phases of low frequency variability in the North Pacific and North Atlantic oceans. Finally, I examine the potential role of predictable internal variability in a future, warmer climate by designing an interpretable neural network that can be decomposed to examine the relative contributions of external forcing and internal variability to future regional decadal SST trend predictions. I show that there is additional prediction skill to be garnered from internal variability in the CESM2 Large Ensemble in the near-term climate (2020-2050), even in a relatively high forcing future scenario. This predictability is especially apparent in the North Atlantic, North Pacific and Tropical Pacific Oceans as well as in the Southern Ocean. I further investigate how prediction skill covaries across the ocean and find three regions with distinct coherent prediction skill driven by internal variability. SST trend predictability is found to be associated with consistent patterns of interannual and decadal variability for the grid points within each region.
  • ItemOpen Access
    GREMLIN: GOES radar estimation via machine learning to inform NWP
    (Colorado State University. Libraries, 2023) Hilburn, Kyle Aaron, author; Miller, Steven D., advisor; Kummerow, Christian D., committee member; Barnes, Elizabeth A., committee member; Ebert-Uphoff, Imme, committee member; Alexander, Curtis R., committee member
    Imagery from the Geostationary Operational Environmental Satellite (GOES) has been a key element of U.S. operational weather forecasting since 1975. The latest generation, the GOES-R Series, offers new capabilities to support the need for high-resolution rapidly refreshing imagery for situational awareness. Despite the well demonstrated value to human forecasters, usage of GOES imagery in data assimilation (DA) for initializing numerical weather prediction (NWP) has been limited, particularly in cloudy and precipitating scenes. By providing a rich and powerful library of nonlinear statistical tools, artificial intelligence (AI) / machine learning (ML) enables new approaches for connecting models and observations. The objective of this research is to develop techniques for assimilating GOES-R Series observations in precipitating scenes for the purpose of improving short-term convective-scale forecasts of high-impact weather hazards. The hypothesis of this dissertation is that by harnessing the power of ML, the new GOES-R capabilities can be used to create equivalent radar reflectivity suitable for initializing convection in high-resolution NWP models. Chapter 1 will present a proof-of-concept that ML can be used as an observation operator for GOES-R to simulate Multi-Radar Multi-Sensor (MRMS) composite reflectivity data and thereby initialize convection in NOAA's Rapid Refresh and High-Resolution Rapid Refresh (RAP/HRRR). Development of the GREMLIN (GOES Radar Estimation via Machine Learning to Inform NWP) convolutional neural network (CNN) will be described. This includes the creation of a hierarchy of open source datasets, and will emphasize the importance of the neural network loss function in focusing the attention of the network on the most important meteorological features. Explainable AI (XAI) tools are applied to GREMLIN to discover three primary strategies employed by the network in making predictions, highlighting the unique ability of CNNs to utilize spatial context in satellite imagery. The results of retrospective Rapid Refresh Forecast System (RRFS) forecasts will be described, which show that GREMLIN can produce more accurate short-term forecasts than using real radar data over areas of the U.S. with poor radar coverage. In Chapter 2, the Interpretable GREMLIN model is developed to elucidate the nature of the spatial context utilized by CNNs to make accurate predictions. This clarity is accomplished by moving the inner workings of the CNN out into a feature engineering step and replacing the neural network with a linear regression model. This exposes the effective input space of the CNN and establishes well defined relationships between inputs and outputs, which provides guarantees on how the model will respond to novel inputs. Despite a 24x reduction in the number of trainable parameters, the interpretable model has similar accuracy as the original CNN. Using the interpretable model, five additional physical strategies missed by XAI are discovered. The pros and cons of interpretable model development and implications for generalizability, consistency, and trustworthy AI will be discussed. Finally, Chapter 3 will extend this research for the development of Global GREMLIN, discussing the challenges and opportunities. GREMLIN is validated for regimes outside of the training dataset, and regime dependence is quantified in terms of temperature and moisture. The impacts of additional predictors and advanced ML architectures, and the derivation of uncertainty estimates that will be needed for new DA approaches in RRFS, will be discussed. Current efforts to implement GREMLIN on NOAA's GeoCloud, which will make GREMLIN available to a broader base of users, will be described.
  • ItemOpen Access
    High-dimensional nonlinear data assimilation with non-Gaussian observation errors for the geosciences
    (Colorado State University. Libraries, 2023) Hu, Chih-Chi, author; van Leeuwen, Peter Jan, advisor; Kummerow, Christian, committee member; Anderson, Jeffrey, committee member; Bell, Michael, committee member; Kirby, Michael, committee member
    Data assimilation (DA) plays an indispensable role in modern weather forecasting. DA aims to provide better initial conditions for the model by combining the model forecast and the observations. However, modern DA methods for weather forecasting rely on linear and Gaussian assumptions to seek efficient solutions. These assumptions can be invalid, e.g., for problems associated with clouds, or for the assimilation of remotely-sensed observations. Some of these observations are either discarded, or not used properly due to these inappropriate assumptions in DA. Therefore, the goal of this dissertation is to seek solutions to tackle the issues arising from the linear and Gaussian assumptions in DA. This dissertation can be divided into two parts. In the first part, we explore the potential of the particle flow filter (PFF) in high dimensional systems. First, we tested the PFF in the 1000- dimensional Lorenz 96 model. The key innovation is we find that using a matrix kernel in the PFF can prevent the collapse of particles along the observed directions, for a sparsely observed and high-dimensional system with only a small number of particles. We also demonstrate that the PFF is able to represent a multi-modal posterior distribution in a high-dimensional space. Next, in order to apply the PFF for the atmospheric problem, we devise a parallel algorithm for PFF in the Data Assimilation Research Testbed (DART), called PFF-DART. A two-step PFF was developed that closely resembles the original PFF algorithm. A year-long cycling data assimilation experiment with a simplified atmospheric general circulation model shows PFF-DART is able to produce stable and comparable results to the Ensemble Adjustment Kalman Filter (EAKF) for linear and Gaussian observations. Moreover, PFF-DART can better assimilate the non-linear observations and reduce the errors of the ensemble, compared to the EAKF. In the second part, we shift our focus to the observation error in data assimilation. Traditionally, observation errors have been assumed to follow a Gaussian distribution mainly for two reasons: it is difficult to estimate observation error statistics beyond its second moment, and most of the DA methods assume a Gaussian observation error by construction. We developed the so-called Deconvolution-based Observation Error Estimation (DOEE), that can estimate the full distribution of the observation error. We apply DOEE to the all-sky microwave radiances and show that they indeed have non-Gaussian observation errors, especially in a cloudy and humid environment. Next, in order to incorporate the non-Gaussian observation errors into variational methods, we explore an evolving-Gaussian approach, that essentially uses a state dependent Gaussian observation error in each outer loop of the minimization. We demonstrate the merits of this method in an idealized experiment, and implemented it in the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts. Preliminary results show improvement for the short-term forecast of lower-tropospheric humidity, cloud, and precipitation when the observation error models of a small set of microwave channels are replaced by the non-Gaussian error models. In all, this dissertation provides possible solutions for outstanding non-linear and non-Gaussian data assimilation problems in high-dimension systems. While there are still important remaining issues, we hope this dissertation lays a foundation for the future non-linear and non-Gaussian data assimilation research and practice.
  • ItemOpen Access
    Inorganic gas-aerosol partitioning in and around animal feeding operation plumes in northeastern Colorado in late summer 2021
    (Colorado State University. Libraries, 2023) Li, En, author; Pierce, Jeffrey, advisor; Fischer, Emily, advisor; Jathar, Shantanu, committee member; Sullivan, Amy, committee member
    Ammonia (NH3) from animal feeding operations (AFOs) is an important source of reactive nitrogen in the US, but despite its ramifications for air quality and ecosystem health, its near-source evolution remains understudied. To this end, Phase I of the Transport and Transformation of Ammonia (TRANS2Am) field campaign was conducted in the northeastern Colorado Front Range in summer 2021 and characterized atmospheric composition downwind of AFOs during 10 research flights. Airborne measurements of NH3, nitric acid (HNO3), and a suite of water-soluble aerosol species collected onboard the University of Wyoming King Air (UWKA) research aircraft present a unique opportunity to investigate the sensitivity of particulate matter (PM) formation to AFO emissions. We couple the observations with thermodynamic modeling to predict the seasonality of ammonium nitrate (NH4NO3) formation. We find that during TRANS2Am northeastern Colorado is consistently in the NH3-rich and HNO3-limited NH4NO3 formation regime. Further investigation using the Extended Aerosol Inorganics Model (E-AIM) reveals that summertime temperatures (mean: 23 ˚C) of northeastern Colorado, especially near the surface, inhibit NH4NO3 formation despite high NH3 concentrations (max: ≤ 114 ppbv). Lastly, we model and winter conditions to explore the seasonality of NH4NO3 formation and find that cooler temperatures could support substantially more NH4NO3 formation. Whereas summertime NH4NO3 only exceeds 1 µg m-3 ~10% of the time in summer, modeled NH4NO3 would exceed 1 µg m-3 61% (88%) of the time in spring/autumn (winter), with a 10°C (20°C) temperature decrease relative to the campaign.
  • ItemOpen Access
    Summertime ozone production at Carlsbad Caverns National Park, New Mexico: influence of oil and natural gas development
    (Colorado State University. Libraries, 2023) Marsavin, Andrey, author; Collett, Jeffrey L., Jr., advisor; Fischer, Emily V., committee member; Willis, Megan D., committee member
    Southeastern New Mexico's Carlsbad Caverns National Park (CAVE) has increasingly experienced summertime ground-level ozone (O3) levels surpassing the US Environmental Protection Agency's National Ambient Air Quality Standard (NAAQS) of 70 parts per billion by volume (ppbv). The park is located in the western part of the Permian oil and natural gas (O&G) basin, where production rates have more than tripled in the last decade. We investigate O3–precursor relationships by constraining a zero-dimensional (0-D) model to an hourly nitrogen oxides (NOx = NO + NO2) and speciated volatile organic compound (VOC) data set collected at CAVE during the summer of 2019. O&G-related VOCs dominated the calculated VOC reactivity with hydroxyl radicals (OH) on days when O3 concentrations were primarily controlled by local photochemistry. Radical budget analysis showed that NOx levels were high enough to impose VOC sensitivity on O3 formation in the morning hours, while subsequent NOx loss through photochemical consumption led to NOx-sensitive conditions in the afternoon. Daily maximum O3 was sensitive to both NOx and O&G-related VOC emission reductions, with NOx reductions generally being more effective. The model could not reproduce a 5-day high O3 episode when constrained to observed NOx and primary VOCs, likely due to influence from O3 produced during air mass transport from regional O&G basins as indicated by back-trajectory analysis, low i/n-pentane ratios consistent with O&G emissions, increased concentrations of secondary VOCs, and extensive oxidation of emitted NOx. Constraining the model with observed total oxidized reactive nitrogen (NOy), which approximates NOx at the time of emission, greatly improves model-observation agreement during this episode, reaffirming NOx-sensitive conditions in photochemically aged air masses.
  • ItemOpen Access
    Changes in the snowpack of the Upper Colorado River basin in a warmer future climate
    (Colorado State University. Libraries, 2023) Sherman, Erin Alexys, author; Rasmussen, Kristen, advisor; Schumacher, Russ, committee member; Fassnacht, Steven, committee member
    Water is a crucial factor to sustaining life on Earth. Snow acts as a reservoir for water, providing storage during the cold seasons and freshwater resources throughout the warmer months. Streamflow in the upper Colorado River Basin is primarily contributed by seasonal mountain snowmelt that provides critical freshwater resources to humans and wildlife, effectively connecting ecological, hydrological, and atmospheric systems. Global Climate Models (GCMs) and regional climate models do not represent the complex processes that can impact snowpack growth, evolution, and melting, thus they often rely on parameterizations to represent such processes. SnowModel is a high-resolution snowpack-evolution modeling system that can simulate processes such as blowing snow redistribution and sublimation, forest canopy interception, and snow-density evolution. To investigate how snowpack in the Upper Colorado Basin may change in a future warmer climate, high-resolution convection-permitting regional climate atmospheric model simulations at 4-km horizontal grid spacing are used to provide input conditions to drive SnowModel at 100-m in the current and future climate for 13 years. Results show that the average snow season will be shorter in the future, reducing the days that the snowpack can accumulate. In addition, analysis of the characteristics of precipitation in the simulations shows a ~150% increase in convective precipitation frequencies in the winter months, indicating shifts in the character of precipitation in a future climate. Liquid precipitation in winter increases ~200% in a future climate as a result of warmer air temperatures. In contrast, solid precipitation stays roughly the same in the winter, but decreases about 25 percent in the fall and spring. A case study analysis of the high-impact snowstorm on 17-19 March 2003 that delivered between 30-70 inches of snow along the Colorado Front Range in a current and future climate shows a shift from a snow-dominant to a rain-dominant event, as well as increases in moisture and convective precipitation frequencies. The simulated changes in the snowpack of the Upper Colorado River Basin will likely have detrimental impacts on freshwater resources and food production in a future climate that will undoubtedly impact a multitude of humans and ecosystems in the western United States.
  • ItemUnknown
    Cold pool train dynamics and transport
    (Colorado State University. Libraries, 2023) Neumaier, Christine Allison, author; van den Heever, Susan C., advisor; Grant, Leah D., advisor; Kreidenweis, Sonia M., committee member; Venayagamoorthy, Subhas K., committee member
    Convectively generated cold air outflows, referred to as cold pools, can initiate new convection and loft aerosols, such as dust or pollen. In the BioAerosols and Convective Storms Phase I (BACS-I) field campaign, we observed multiple cold pools passing over the same location on the same day, without colliding, which we refer to as a "cold pool train". The goals of this study are to examine how the dynamics of cold pools in a cold pool train differ, how cold pools in a cold pool train affect the vertical distribution of aerosols, and how the results may change if the properties of the second cold pool change. We utilize idealized simulations of a cold pool train composed of two cold pools to investigate the dynamics of the cold pools in the train and how cold pool trains loft and transport aerosols. We test the sensitivity of the second cold pool's evolution and aerosol lofting to its initial temperature deficit and timing relative to the first cold pool, based on the cold pool trains observed during BACS-I. Passive tracers are initialized at different times to represent the background aerosols present before cold pools, aerosols newly emitted after the passage of the first cold pool in the train, and aerosols within and ahead of each cold pool, to distinguish between how cold pools loft their own air compared to distinct environmental air. We find that the first cold pool (CP1) in the cold pool train stably stratifies the environment ahead of the downshear side of the second cold pool (CP2) in the train. All else equal, this stabilization acts to decrease the height of CP2's head and increase its propagation speed. However, the stratification also increases the horizontal wind shear ahead of CP2 by decreasing the lower level wind speeds, which opposes the stability effects and acts to deepen the head of CP2. In the CONTROL case, where CP2 is initialized two hours after CP1 and with the same temperature deficit as CP1, we find that the wind profile plays a more dominant role for the dynamics of CP2 because overall, CP2's head is deeper and propagates slower compared to CP1. In the temperature deficit sensitivity experiments, we find that CP2's head depth and propagation speed decreases with decreasing temperature deficit. Finally, in the timing sensitivity tests of CP2, we find CP2 initiated 90 minutes after CP1 had the deepest head, while CP2 in the CONTROL (120 minutes) experiment propagated the slowest. Our analysis of the tracer lofting mechanisms in the simulations shows that the downshear leading edge of CP1 lofts the highest concentration of background aerosol, while the downshear leading edge of the CONTROL CP2 lofts less than half of the amount of background aerosol as CP1. However, the downshear leading edge of CP2 lofts more than double the concentration of newly emitted aerosol compared to the background aerosol lofted by CP1. The atmospheric stratification left behind by CP1 acts to trap the newly emitted aerosol near the surface, leading to greater concentrations lofted compared to the background aerosol which is well mixed in the boundary layer. Analysis of the tracers initialized within and ahead of the cold pools demonstrates that the lofted aerosol primarily originates from the air ahead of the cold pools, while the aerosol originating in the cold pools remains trapped within the cold pools. The CONTROL CP2 lofts the most aerosol of the temperature deficit sensitivity tests, and the CONTROL CP2, released the farthest apart temporally from CP1, lofts the most aerosol out of the timing sensitivity tests. Therefore, while the wind profile change ahead of CP2 plays a dominant role in its dynamics, atmospheric static stabilization plays a dominant role for the aerosol concentration lofted by CP2.
  • ItemUnknown
    The effect of projected sea surface temperature change on MJO activity in a warmer climate
    (Colorado State University. Libraries, 2023) Bowden, Amanda Francine Marie, author; Maloney, Eric D., advisor; Hurrell, Jim, committee member; Ross, Matthew, committee member
    The Madden Julian Oscillation (MJO) consists of a convective region that propagates eastward in the tropics on repeat every 30-90 days with peak amplitude during the Boreal Winter (November - March). Since the MJO modulates extreme weather such as tropical cyclones, atmospheric rivers, and monsoon variability, future MJO changes in a warmer climate have implications for prediction of extreme events. Understanding precipitation pattern changes in a changing climate is critical for fresh-water resources and societal planning for oceanic regions. Decadal variability in the climate system causes patterns of sea surface temperature (SST) change in the tropical Pacific and associated precipitation, humidity, and wind pattern changes to vary from one decade to the next. MJO changes are strongly dependent on the pattern of SST change, and so understanding uncertainty in MJO change in future decades in the context of this decadal variability is the primary motivation for this investigation. Since climate models contain climate variability on decadal timescales, different initial conditions across ensemble members can result in diverse projection outcomes in any given decade. This investigation examines the impact of projected SST and moisture pattern changes over the 21st Century on MJO precipitation and zonal wind (850 mb) amplitude changes using 80 members with the SSP370 radiative forcing scenario from the Community Earth System Model 2 (CESM2) Large Ensemble. The projected SST and moisture pattern changes can be weighted more toward the central or eastern equatorial Pacific in earlier parts of the 21st Century across ensemble members, although becomes strongly El Niño-like later in the century. Ensemble members with stronger MJO precipitation amplitude in a given period are characterized by stronger El Niño-like east Pacific warming, associated with a strengthened meridional moisture gradient. As interpreted through moisture mode theory, greater east Pacific warming supports a stronger MJO by enhancing propagation through a stronger meridional moisture gradient, and enhancing MJO amplitude through a stronger vertical moisture gradient. The investigation supports the hypothesis that projected SST and moisture pattern changes influence MJO activity, and also highlights the importance of understanding decadal climate variability for interpreting changes in water resources of oceanic regions.
  • ItemOpen Access
    Diagnosing the angular momentum fluxes that drive the quasi-biennial oscillation
    (Colorado State University. Libraries, 2023) Hughes, Ann-Casey, author; Randall, David A., advisor; Hurrell, James, committee member; Oprea, Iuliana, committee member
    The quasi-biennial oscillation (QBO) is a descending pattern of alternating easterly and westerly equatorial stratospheric winds that is produced by the upward transport of momentum in multiple types of atmospheric waves. The discovery of the QBO and its role in the global circulation are discussed. The angular momentum budget of the QBO is analyzed using ERA-Interim isentropic analyses. We explain the benefits of isentropic coordinates and angular momentum as tools for analyzing atmospheric motion. We diagnose vertical motion utilizing continuity, allowing direct computation of the angular momentum fluxes due to vertical motion. The angular momentum fluxes due to unresolved convectively generated gravity waves are computed as a residual. These results are discussed with the goal of improving the representation of sub-grid scale motions in numerical models. We also discuss these results within the context of the reliability of reanalysis datasets and the downsides to treating reanalysis data as observations. We also revisit and discuss the seasonal dependence of the QBO transition.
  • ItemOpen Access
    The influence of prescribed burning on springtime PM2.5 concentrations in eastern Kansas
    (Colorado State University. Libraries, 2023) Sablan, Olivia, author; Fischer, Emily V., advisor; Pierce, Jeffrey R., advisor; Magzamen, Sheryl, committee member; Ford, Bonne, committee member
    Annual springtime (March - May) prescribed burning is practiced in the Flint Hills of eastern Kansas to mitigate wildfire risk, improve nutritional value of vegetation for cattle grazing, limit woody encroachment, and maintain the health of the tall grass prairie ecosystem. Smoke from these prescribed fires produces fine particulate matter (PM2.5), degrading air quality. Smoke from prescribed fires is understudied due to their short duration and a lack of monitoring in the rural regions where prescribed burning occurs. To quantify the contribution of springtime prescribed burning to PM2.5 concentrations in the Flint Hills and downwind regions, we deployed 38 PurpleAir PM2.5 sensors for the 2022 burning season. We used observations from this ground-based network alongside a suite of satellite products to determine the PM2.5 attributable to smoke. In 2022, the Flint Hills were also impacted by dust and transported smoke from high winds, drought, and wildfires in New Mexico. We separated the local and transported smoke effects for our exposure estimates. Across the low-cost sensor network, 24-hour median PM2.5 increased by 5.2 µg m-3 on days impacted by smoke from fires in the eastern Kansas region versus smoke-free days. We compared our findings to two existing PM2.5 estimates derived from satellites and ground-based measurements. Satellite-based products show a similar daily smoke-driven median increase in PM2.5 concentration and a consistent increase in seasonal average PM2.5 concentrations in the Flint Hills region as our estimates based on in situ monitors.
  • ItemOpen Access
    Errors of opportunity: using neural networks to predict errors in the unified forecast system (UFS) on S2S timescales
    (Colorado State University. Libraries, 2023) Cahill, Jack, author; Barnes, Elizabeth A., advisor; Maloney, Eric D., advisor; Ross, Matthew, committee member
    Making predictions of impactful weather on timescales of weeks to months (subseasonal to seasonal; S2S) in advance is incredibly challenging. Dynamical models often struggle to simulate tropical systems that evolve over multiple weeks such as the Madden Julian Oscillation (MJO) and the Boreal Summer Intraseasonal Oscillation (BSISO), and these errors can impact geopotential heights, precipitation, and other variables in the continental United States through teleconnections. While many data-driven S2S studies attempt to predict future midlatitude variables using current conditions, here we instead focus on post-processing of the National Oceanic and Atmospheric Association's (NOAA) Unified Forecast System (UFS) to predict UFS errors. Specifically, by looking at when/where there are errors in the UFS, neural networks can be used to understand what atmospheric conditions helped produce these errors via explainability methods. Our 'Errors of Opportunity' approach identifies phase 4 of the MJO and phases 1 and 2 of the BSISO as significant factors in aiding UFS error prediction across different regions and seasons. Specifically, we see high accuracy for underestimates of geopotential heights in the Pacific Northwest during Spring and as well as high accuracy for overestimates of geopotential heights in Northwest Mexico during Summer. Furthermore, we demonstrate enhanced error prediction skill for overestimates of Summer precipitation in the Midwest following BSISO phases 1 and 2. Most notably, our findings highlight that the identified errors stem from the UFS's failure to accurately forecast teleconnection patterns.
  • ItemUnknown
    The role of Earth system interactions in large-scale atmospheric circulation and climate
    (Colorado State University. Libraries, 2023) Yook, Simchan, author; Thompson, David W. J., advisor; Ravishankara, A. R., committee member; Hurrell, James, committee member; Ebert-Uphoff, Imme, committee member
    The complex interactions among different components of the Earth system play a key role in governing the climate variability through various physical processes. For example, an interaction between the fluctuations in one component of the Earth system and associated variations in another component of the Earth system can either amplify or dampen the climate variability depending on the nature of their two-way feedback mechanisms. Thus, understanding the role of various physical interactions among components of the Earth system is critical to understand the changes in climate as well as to reduce the uncertainty in future climate projections. This dissertation focuses on discovering the key processes and interactions among different components of the Earth system on the climate variability using observations and model hierarchies. In Part 1, the interactions between the atmospheric circulation and western North Pacific SST anomalies are explored in two sets of simulations: 1) a simulation run on a coupled atmosphere-ocean general circulation model (GCM), and 2) a simulation forced with prescribed, time-evolving SST anomalies over the western North Pacific. The results support the interpretation of the observed lead/lag relationships between western North Pacific Sea Surface Temperature (SST) anomalies and the atmospheric circulation, and provide numerical evidence that SST variability over the western North Pacific has a demonstrable effect on the large-scale atmospheric circulation throughout the North Pacific sector. In Part 2, the role of moist lapse rate in altering the temperature variability under climate change is explored. To reduce the complexity of the problem, the changes in the temperature variance under global warming are first analyzed in the simplest version of model hierarchy: a single column Rapid Radiative Transfer Model with a simplified convective adjustment. Similar analyses were repeated with varying model hierarchies with additional complexities: a global general circulation model in global Radiative Convective Equilibrium (RCE) setting with fixed SST, and fully coupled Earth system models. The results highlight the role of moist lapse rate as a potential constraint for climate variability in the tropical atmosphere simulated by different model hierarchies. In Part 3, the effects of coupled chemistry-climate interactions on the amplitude and structure of stratospheric temperature variability are quantified in two numerical simulations: A "free running" simulation that includes fully coupled chemistry-climate interactions; and a "specified chemistry" version of the model forced with prescribed chemical composition. The results indicate that the inclusion of coupled chemistry-climate interactions increases the internal variability of temperature by a factor of ~two in the lower tropical stratosphere through dynamically driven ozone-temperature feedbacks. The results highlight the fundamental role of two-way feedbacks between the atmospheric circulation and chemistry in driving climate variability in the lower stratosphere. In Part 4, the effects of coupled chemistry-climate interactions on the large-scale atmospheric circulation are further explored based on two observational case studies of the Antarctic ozone holes of 2020 and 2021. The 2020 and 2021 were marked by two of the largest Antarctic ozone holes on record. It has been demonstrated that the ozone holes of 2020 and 2021 were associated with large changes in the atmospheric circulation consistent with the climate impacts of Antarctic ozone depletion. The ozone holes were also unusual for their associations with aerosol burdens due to two extraordinary events: the Australian wildfires of early 2020 and the eruption of La Soufriere in 2021. The results provide suggestive evidence that injections of both wildfire smoke and volcanic emissions into the stratosphere can lead to hemispheric-scale changes in surface climate. This dissertation provides a detailed look at the complex aspects of the coupled interactions among different components of the Earth system and their roles on climate variability and large-scale dynamics. To clarify the role of the different physical processes contributing to the climate responses, this study performed a comprehensive analysis based on observations as well as a series of numerical experiments run on different configurations of climate model hierarchies. The findings herein improve our understanding of different Earth system interactions and their influences on global climate and large-scale atmospheric dynamics.
  • ItemUnknown
    Influence of terrain on the characteristics and life cycle of convection observed in subtropical South America
    (Colorado State University. Libraries, 2023) Rocque, Marquette N., author; Rasmussen, Kristen L., advisor; Schumacher, Russ S., committee member; Miller, Steven D., committee member; Chandrasekar, V., committee member
    Subtropical South America (SSA) is a hotspot for deep, intense convection that often grows upscale into large mesoscale convective systems (MCSs) overnight. The local terrain, including the Andes and a secondary feature known as the Sierras de Córdoba (SDC) are hypothesized to play a major role in the initiation, development, and evolution of convection in the region. Some satellite studies have investigated this role, but storm-scale and life cycle characteristics of these MCSs have not been studied in depth due to the lack of high-resolution, ground-based instruments in the region. However, in 2018-2019, several research-quality platforms were deployed to Córdoba, Argentina as part of the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) and the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaigns. The data collected during these campaigns is used in the studies presented in this dissertation to investigate how the Andes and SDC contribute to convection initiation and rapid upscale growth under varying synoptic conditions. Determining why convection is so unique in SSA may provide insight into characteristics of other storms around the world. The first two studies in the dissertation evaluate how the Andes and SDC modulate the large-scale environment and storm-scale characteristics under strong vs. weak synoptic forcing. High resolution, convection-permitting simulations in which the terrain is modified are designed to investigate synoptic (Chapter 2) and mesoscale (Chapter 3) processes related to the development of two severe mesoscale convective systems (MCSs) observed during RELAMPAGO-CACTI. Results from the simulations are also compared with radar observations to determine how well the model performs. Under strong synoptic forcing, when the Andes are reduced by 50%, the lee cyclone that develops is weaker, the South American Low-level Jet (SALLJ) is weaker and shallower, and the MCS that develops is weaker and moves quickly off the terrain. When the SDC are removed, there are no substantial changes to the large-scale environment. However, there is no back-building signature of deep convection, likely because cold pools are no longer blocked by the SDC. Under weak synoptic forcing, there are no significant changes to the large-scale environment, even when the Andes are halved. Similar to the strongly forced case though, when the SDC are removed, there are fewer deep convective cores toward the west. In both cases, the model tends to overestimate convection compared to observations. These studies show that the terrain plays varying roles in the evolution of convection in SSA. The third and fourth studies use ground-based lightning observations from RELAMPAGO-CACTI to better understand the electrical and microphysical characteristics of these intense storms. Three-dimensional storm structures are identified in the radar data and lightning flashes are matched with these storm modes to evaluate how lightning varies throughout the convective life cycle (Chapter 4). Results show that lightning flashes associated with deep convective cores are most common along the higher terrain of the SDC and occur in the afternoon hours. They also tend to be the smallest in size. Flashes associated with wide convective cores occur more frequently along the eastern edge of the SDC and are observed around midnight local time. Stratiform flashes are found most frequently in the early morning hours about 50-100 km east of the SDC, and they tend to be the largest in area and occur lower within the cloud. These distributions highlight the life cycle of systems, which initiate along the SDC and grow upscale as they move towards the plains overnight. Flash rates are then related to microphysical properties such as graupel mass and ice water path (Chapter 5). The first lightning flash rate parameterizations are developed for storms in SSA. We find these storms have considerably more graupel associated with them compared to storms in the U.S. These new parameterizations are tested on the simulated strongly forced MCS, and results agree well with observed flash rates. If parameterizations based on U.S. storms had been used instead, the flash rates would have been overestimated by up to a factor of 8. This work, in conjunction with other studies in this dissertation, highlights just how different storms in SSA are compared to the U.S.
  • ItemOpen Access
    Links between climate feedbacks and the large-scale circulation across idealized and complex climate models
    (Colorado State University. Libraries, 2023) Davis, Luke L. B., author; Thompson, David W. J., advisor; Maloney, Eric, committee member; Randall, David, committee member; Pinaud, Olivier, committee member; Gerber, Edwin, committee member
    The circulation response to anthropogenic forcing is typically considered in one of two distinct frameworks: One that uses radiative forcings and feedbacks to investigate the thermodynamics of the response, and another that uses circulation feedbacks and thermodynamic constraints to investigate the dynamics of the response. In this thesis, I aim to help bridge the gap between these two frameworks by exploring direct links between climate feedbacks and the atmospheric circulation across ensembles of experiments from idealized and complex general circulation models (GCMs). I first demonstrate that an existing, widely-used type of idealized GCM — the dynamical core model — has climate feedbacks that are explicitly prescribed and determined by a single parameter: The thermal relaxation timescale. The dynamical core model may thus help to fill gaps in the model hierarchies commonly used to study climate forcings and climate feedbacks. I then perform two experiments: One that explores the influence of prescribed feedbacks on the unperturbed, climatological circulation; and a second that explores their influence on the circulation response to a horizontally uniform, global warming-like forcing perturbation. The results indicate that more stabilizing climate feedbacks are associated with 1) a more vigorous climatological circulation with increased thermal diffusivity, and 2) a weaker poleward displacement of the circulation in response to the global warming-like forcing. Importantly, since the most commonly-used relaxation timescale field resembles the real-world clear-sky feedback field, the uniform forcing perturbations produce realistic warming patterns, with amplified warming in the tropical upper troposphere and polar lower troposphere. The warming pattern and circulation response disappear when the relaxation timescale field is instead spatially uniform, demonstrating the critical role of spatially-varying feedback processes on shaping the response to anthropogenic forcing. I next explore circulation-feedback relationships in more complex GCMs using results from the most recent Coupled Model Intercomparison Projects (CMIP5 and CMIP6). Here, I estimate climate feedbacks by regressing top-of-atmosphere radiation against surface temperature for both 1) an unperturbed pre-industrial control experiment and 2) a perturbed global warming experiment forced by an abrupt quadrupling of CO2 concentrations. I find that across both ensembles, the cloud component of the perturbed climate feedback is closely related to the cloud component of the unperturbed climate feedback. Critically, the relationship is much stronger in CMIP6 than CMIP5, contrasting with many previously proposed constraints on the perturbation response. The relationship also explains the slow part of the CO2 response better than the fast, transient response. In general, the strength of the relationship depends on the degree to which the spatial pattern of the response resembles ENSO-dominated internal variability, with "El Niño-like" East Pacific warming and related tropical cloud changes. This is consistent with fluctuation-dissipation theory: Regions with stronger deep ocean heat exchange and weaker net feedbacks must always dominate both 1) internal fluctuations in the global energy budget, and 2) the slow part of the response to forcing perturbations. The stronger CMIP6 inter-model relationships are due to both an amplification of this mechanism and higher inter-model correlations between tropical cloud changes and extratropical cloud changes. Finally, I present emergent constraints on the slow response using a recent observational estimate of the unperturbed cloud feedback. I conclude by discussing some implications of these results. I consider how the relaxation feedback framework might be further developed and reconciled with traditional climate feedbacks to provide future research opportunities with climate model hierarchies.
  • ItemOpen Access
    A potential vorticity diagnosis of tropical cyclone track forecast errors
    (Colorado State University. Libraries, 2023) Barbero, Tyler Warren, author; Bell, Michael M., advisor; Barnes, Elizabeth A., committee member; Chen, Jan-Huey, committee member; Klotzbach, Philip J., committee member; Zhou, Yongcheng, committee member
    A tropical cyclone (TC) can cause significant impacts on coastal and near-coastal communities from storm surge, flooding, intense winds, and heavy rainfall. Accurately predicting TC track is crucial to providing affected populations with time to prepare and evacuate. Over the years, advancements in observational quality and quantity, numerical models, and data assimilation techniques have led to a reduction in average track errors. However, large forecast errors still occur, highlighting the need for ongoing research into the causes of track errors in models. We use the piecewise potential vorticity (PV) inversion diagnosis technique to investigate the sources of errors in track forecasts of four high-resolution numerical weather models during the hyperactive 2017 Atlantic hurricane season. The piecewise PV inversion technique is able to quantify the amount of steering, as well as steering errors, on TC track from individual large-scale pressure systems. Through the systematic use of the diagnostic tool, errors that occur consistently (model biases) could also be identified. TC movement generally follows the atmospheric flow generated by large-scale environmental pressure systems, such that errors in the simulated flow cause errors in the TC track forecast. To understand how the environment steers TCs, we use the Shapiro decomposition to remove the TC PV field from the total PV field, and the environmental (i.e., perturbation) PV field is isolated. The perturbation PV field was partitioned into six systems: the Bermuda High and the Continental High, which compose the negative environmental PV, and quadrants to the northwest, northeast, southeast, and southwest of the TC, which compose the positive environmental PV. Each piecewise PV perturbation system was inverted to retrieve the balanced mass and wind fields. To quantify the steering contribution in individual systems to TC movement, a metric called the deep layer mean steering flow (DLMSF) is defined, and errors in the forecast DLMSF were calculated by comparing the forecast to the analysis steering flow. Lag correlation analyses of DLMSF errors and track errors showed moderate-high correlation at -24 to 0 hrs in time, which indicates that track errors are caused in part by DLMSF errors. Three hurricanes (Harvey, Irma, and Maria) were analyzed in-depth and errors in their track forecasts are attributed to errors in the DLMSF. A basin-scale analysis was also performed on all hurricanes in the 2017 Atlantic hurricane season. The DLMSF mean absolute error (MAE) showed the Bermuda High was the highest contributor to error, the Continental High showed moderate error, while the four quadrants showed lower errors. High error cases were composited to examine potential model biases. On average, the composite showed lower balanced geopotential heights around the climatological position of the Bermuda High associated with the recurving of storms in the North Atlantic basin. The analysis techniques developed in this thesis aids in the identification of model biases which will lead to improved track forecasts in the future.
  • ItemOpen Access
    Assessing the impact of stratospheric aerosol injection on convective weather environments in the United States
    (Colorado State University. Libraries, 2023) Glade, Ivy, author; Hurrell, James W., advisor; Rasmussen, Kristen L , committee member; Anderson, Brooke, committee member
    Continued climate warming, together with the overall development and implementation of climate mitigation and adaptation approaches, has prompted increasing research into the potential of proposed solar climate intervention (SCI) methods, such as stratospheric aerosol injection (SAI). SAI would reflect a small amount of incoming solar radiation away from the Earth to reduce warming due to increasing greenhouse gas concentrations. Research into the possible risks and benefits of SAI relative to the risks from climate change is emerging. There is not yet, however, an adequate understanding of how SAI might impact human and natural systems. To date, little or no research has been done to examine how SAI might impact environmental conditions critical to the formation of severe convective weather over the United States (U.S.), for instance. We use parallel ensembles of Earth system model simulations of future climate change, with and without hypothetical SAI deployment, to examine possible future changes in thermodynamic and kinematic parameters critical to the formation of severe weather during convectively active seasons over the U.S. Southeast and Midwest. We find that simulated forced changes in thermodynamic parameters are significantly reduced under SAI relative to a no-SAI world, while simulated changes in kinematic parameters are more difficult to distinguish. We also find that unforced internal climate variability may significantly modulate the projected forced climate changes over large regions of the U.S.
  • ItemOpen Access
    Investigating the impact of forced and internal climate variability on future convective storm environments in subtropical South America: a large ensemble approach
    (Colorado State University. Libraries, 2023) Chakraborty, Anindita, author; Rasmussen, Kristen, advisor; Hurrell, James, advisor; Anderson, Brooke, committee member
    Subtropical South America (SSA) has some of the most intense deep convection in the world. Large hail and frequent lightning are just two of the hazards that profoundly affect people, agriculture, and infrastructure in this region. Therefore, it is important to understand the future convective storm environments over SSA associated with climate change and how these large-scale environmental changes are likely to change high-impact weather events in the future. Previous studies have used convection-permitting regional models and radar data to examine convective storm environments in the current climate across different regions of South America. Here, we use a large ensemble of Earth system model simulations to quantify anthropogenically-driven future changes in large-scale convective environments, as well as how those forced changes might be modified by unforced, internal climate variability. Specifically, we examine changes in different thermodynamic parameters of relevance to severe weather events over SSA in austral spring and summer (September-February). We use daily data from a 50-member ensemble from 1870-2100 performed with version two of the Community Earth System Model (CESM2). Results indicate that no forced changes in convective environments are evident until very late in the 20th century. However, increases in convective available potential energy and atmospheric stability, as well as an increase in lower tropospheric vertical wind shear, became apparent around 1990, and these trends are projected to continue throughout the rest of this century. The implication is that future large-scale environments may be favorable for less frequent, but perhaps more intense and severe convective modes and their associated hazards. Results also demonstrate that anthropogenic changes are likely to be significantly modified, regionally, by internal climate variability.