Browsing by Author "Barnes, Elizabeth A., committee member"
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Item Open Access A new post-processing paradigm? Improving high-impact weather forecasts with machine learning(Colorado State University. Libraries, 2018) Herman, Gregory Reid, author; Schumacher, Russ S., advisor; Barnes, Elizabeth A., committee member; van den Heever, Susan C., committee member; Cooley, Daniel S., committee member; Hamill, Thomas M., committee memberHigh-impact weather comes in many different shapes, sizes, environments, and storm types, but all pose threats to human life, property, and the economy. Because of the significant societal hazards inflicted by these events, having skillful forecasts of the risks with sufficient lead time to make appropriate precautions is critical. In order to occur, these extreme events require a special conglomeration of unusual meteorological conditions. Consequently, effective forecasting of such events often requires different perspectives and tools than routine forecasts. A number of other factors make advance forecasts of rare, high-impact weather events particularly challenging, including the lack of sufficient resolution to adequately simulate the phenomena dynamically in a forecast model; model biases in representing storms, and which often become increasingly pronounced in extreme scenarios; and even difficulty in defining and verifying the high-impact event. This dissertation systematically addresses these recurring challenges for several types of high-impact weather: flash flooding and extreme rainfall, tornadoes, severe hail, and strong convective winds. For each listed phenomenon, research to more concretely define the current state of the science in analyzing, verifying, and forecasting the phenomenon. From there, in order to address the aforementioned persistent limitations with forecasting extreme weather events, machine learning-based post-processing models are developed to generate skillful, calibrated probabilistic forecasts for high-impact weather risk across the United States. Flash flooding is a notoriously challenging forecast problem. But the challenge is rooted even more fundamentally with difficulties in assessing and verifying flash flooding from observations due to the complex combination of hydrometeorological factors affecting flash flood occurrence and intensity. The first study in this dissertation investigates the multi-faceted flash flood analysis problem from a simplified framework considering only quantitative precipitation estimates (QPEs) to assess flash flood risk. Many different QPE-to-flash flood potential frameworks and QPE sources are considered over a multi-year evaluation period and QPE exceedances are compared against flash flood observations and warnings. No conclusive "best" flash flood analysis framework is clearly identified, though specific strengths and weaknesses of different approaches and QPE sources are identified in addition to regional differences in optimal correspondence with observations. The next two-part study accompanies the flash flood analysis investigation by approaching forecasting challenges associated with extreme precipitation. In particular, more than a decade of forecasts from a convection-parameterized global ensemble, the National Oceanic and Atmospheric Administration's Second Generation Global Ensemble Forecast System Reforecast (GEFS/R) model, are used to develop machine learning (ML) models for probabilistic prediction of extreme rainfall across the conterminous United States (CONUS) at Days 2 and 3. Both random forests (RFs) and logistic regression models (LR) are developed, with separate models trained for each lead time and for eight different CONUS regions. Models use the spatiotemporal evolution of a host of different atmospheric fields as predictors in addition to select geographic and climatological predictors. The models are evaluated over four years of withheld forecasts. The models, and particularly the RFs, are found to compare very favorably with both raw GEFS/R ensemble forecasts and those from a superior global ensemble produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) both in terms of forecast skill and reliability. The trained models are also inspected to discern what statistical findings are identified through ML. Many of the findings quantify anecdotal knowledge that is already recognized regarding the forecast problem, such as the relative skill of simulated precipitation in areas where extreme precipitation events are associated with large-scale processes well resolved by the GEFS/R compared with areas where extreme precipitation predominantly occurs in association with convection in the warm-season. But more subtle spatiotemporal biases are also diagnosed, including a northern displacement bias in the placement of convective systems and a southern displacement bias in placing landfalling atmospheric rivers. The final extended study shifts weather phenomenon focus from extreme rainfall to severe weather: tornadoes, large hail, and severe convective winds. While both high-impact, the two classes of weather hazards share some commonalities and contrasts. While rainfall is directly forecast by dynamical weather models, most severe weather occurs on too small of spatial scales to be directly simulated by the same models. Consequently, unlike with extreme precipitation, when developing post-processed severe weather forecasts, there is no obvious benchmark for objectively determining whether and how much improvement the post-processing is yielding. A natural alternative, albeit much more stringent, benchmark is operational forecasts produced by human forecasters. Operational severe weather forecasts are produced by the Storm Prediction Center (SPC), but there is limited published verification of their outlooks quantifying their probabilistic skill. In the first part of this study, an extended record SPC severe weather outlooks were evaluated to quantitatively assess the state of operational severe weather forecasting, including strengths and weaknesses. SPC convective outlooks were found to decrease in skill with increased forecast lead time, and were most skillful for severe winds, with the worst performance for tornado outlooks. Many seasonal and regional variations were also observed, with performance generally best in the North and East and worst in the South and especially West. The second part of the study follows similar methodology to the extreme precipitation models, developing RF-based probabilistic forecast models forced from the GEFS/R for Days 1--3 across CONUS, analogous to the format in which SPC produces its convective outlooks. RF properties are inspected to investigate the statistical relationships identified between GEFS/R fields and severe weather occurrence. Like with the extreme precipitation model, RF severe weather forecasts are generated and evaluated from several years of withheld validation cases. These forecasts are compared alongside SPC outlooks and also blended with them to produce a combined forecast. Overall, by statistically quantifying relationships between the synoptic-scale environment and severe weather in a manner consistent with the community's physical understanding of the forecast problems, the RF models are able to demonstrate skill over SPC outlooks at Days 2 and 3, and can be blended with SPC outlooks to enhance skill at Day 1. Overall, multiple high-impact weather phenomena---extreme precipitation and severe weather---are investigated from verification, analysis, and forecasting standpoints. On verification and analysis, foundations have been laid both to improve existing operational products as well as better frame and contextualize future studies. ML post-processing models developed were highly successful in advancing forecast skill and reliability for these hazardous weather phenomena despite being developed from predictors of a coarse, dated dynamical model in the GEFS/R. The findings also suggest adaptability across a wide array of forecast problems, types of predictor inputs, and lead times, raising the possibility of broader applicability of these methods in operational numerical weather prediction.Item Open 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 memberA 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.Item Open Access An examination of the large-scale drivers of North Atlantic vertical wind shear and seasonal tropical cyclone variability(Colorado State University. Libraries, 2021) Jones, Jhordanne J., author; Bell, Michael M., advisor; Klotzbach, Philip J., advisor; Barnes, Elizabeth A., committee member; Maloney, Eric D., committee member; Florant, Gregory L., committee memberThis dissertation characterizes and examines the large-scale sources of variability driving tropical North Atlantic deep-layer vertical wind shear (VWS). VWS is a key variable for the seasonal prediction of tropical cyclone (TC) activity and can be used to assess sources of predictability within a given season. Part 1 of the dissertation examines tropical versus subtropical impacts on TC activity by considering large-scale influences on boreal summer tropical zonal VWS variability, a key predictor of seasonal TC activity. Through an empirical orthogonal function analysis, I show that subtropical anticyclonic wave breaking (AWB) activity drives the second mode of variability in tropical zonal VWS, while El Niño-Southern Oscillation (ENSO) primarily drives the leading mode of tropical zonal VWS variability. Linear regressions of the four leading principal components against tropical North Atlantic zonal VWS and accumulated cyclone energy show that, while the leading mode holds much of the regression strength, some improvement can be achieved with the addition of the second and third modes. Furthermore, an index of AWB-associated VWS anomalies, a proxy for AWB impacts on the large-scale environment, may be a better indicator of summertime VWS anomalies. The utilization of this index may be used to better understand AWB's contribution to seasonal TC activity. Part 2 shows that predictors representing the environmental impacts of subtropical AWB on seasonal TC activity improve the skill of extended-range seasonal forecasts of TC activity. There is a significant correlation between boreal winter and boreal summer AWB activity via AWB-forced phases of the quasi-stationary North Atlantic Oscillation (NAO). Years with above-normal boreal summer AWB activity over the North Atlantic region also show above-normal AWB activity in the preceding boreal winter that forces a positive phase of the NAO that persists through the spring. These conditions are sustained by continued AWB throughout the year, particularly when ENSO plays less of a role at forcing the large-scale circulation. While individual AWB events are synoptic and nonlinear with little predictability beyond 8-10 days, the strong dynamical connection between winter and summer wave breaking lends enough persistence to AWB activity to allow for predictability of its potential impacts on TC activity. We find that the winter-summer relationship improves the skill of extended-range seasonal forecasts from as early as an April lead time, particularly for years when wave breaking has played a crucial role in suppressing TC development. Part 3 characterizes VWS variability within the Community Earth System Model version 1 Large Ensemble (CESM1-LE). The 35 historical runs of the CESM1-LE provide substantially larger samples of the environment and various large-scale drivers than the ERA5 reanalysis that spans 1979 to present. Firstly, ENSO is shown to be the leading mode of tropical Atlantic variability and explains most, if not all, of the structured variance. Secondly, while the CESM1-LE shows robust physical representations of known climate phenomena, their relationships with tropical Atlantic VWS remain marginal except for ENSO. Eigenanalysis applied to the CESM1-LE shows that the principal components are ill-defined and gives no distinct pattern for non-ENSO associated large-scale drivers. Thirdly, composite analyses show that despite the narrow range of VWS variability associated with non-ENSO large-scale drivers, their individual contribution to VWS is noticeably stronger during ENSO-neutral conditions as represented by the large ensemble.Item Open Access Climate model error in the evolution of sea surface temperature patterns affects radiation and precipitation projections(Colorado State University. Libraries, 2024) Alessi, Marc J., author; Rugenstein, Maria A.A., advisor; Barnes, Elizabeth A., committee member; Maloney, Eric D., committee member; Willis, Megan D., committee memberAtmosphere-ocean general circulation models (AOGCMs) are the primary tool climate scientists use in predicting the effects of climate change. While they have skill in reproducing global-mean temperature over the historical period, they struggle to replicate recently observed sea surface temperature (SST) trend patterns. In this dissertation, we quantify the impact of potential future model error in SST pattern trends on projections of global-mean temperature and Southwest U.S. (SWUS) precipitation. We primarily use a Green's function (GF) approach to identify which SST regions are most relevant for changes in these variables. Our findings demonstrate significant sensitivity of both global-mean temperature and SWUS precipitation to the pattern of sea surface warming, meaning that a continuation of AOGCM error in SST trend patterns adds uncertainty to climate projections which are currently not accounted for. In Chapter 1, we quantify the relevance of future model error in SST to global-mean temperature projections through convolving a GF with physically plausible SST pattern scenarios that differ from the ones AOGCMs produce by themselves. We find that future model error in the pattern of SST has a significant impact on projections, such as increasing total model uncertainty by 40% in a high-emissions scenario by 2085. A reversal of the current cooling trend in the East Pacific over the next few decades could lead to a period of global-mean warming with a 60% higher rate than currently projected. These SST pattern scenarios work through a destabilization of the shortwave cloud feedback to affect temperature projections. In Chapter 2, we focus on near-term projections of precipitation in the SWUS. The observed decrease in SWUS precipitation since the 1980s and heightened drought conditions since the 2000s have been linked to a cooling sea surface temperature (SST) trend in the Equatorial Pacific. Notably, climate models fail to reproduce this observed SST trend, and they may continue doing so in the future. In this chapter, we assess the sensitivity of SWUS precipitation projections to future SST trends using a GF approach. Our findings reveal that a slight redistribution of SST leads to a wetting or drying of the SWUS. A reversal of the observed cooling trend in the Central and East Pacific over the next few decades would lead to a period of wetting in the SWUS. In Chapter 3, we analyze SWUS precipitation sensitivity to SST patterns on long timescales (7+ years) according to a GF approach and a convolutional neural network (CNN) approach. The GF and CNN identify different SST regions as having greater influence on SWUS precipitation: the GF highlights the Central Pacific known from theory to be relevant, while the CNN highlights the South-Central Pacific. To determine if the South-Central Pacific has a physically meaningful and so far overlooked influence on SWUS precipitation, rather than just a statistical relationship, we force an atmosphere-only climate model with an SST anomaly inspired by an Explainable Artificial Intelligence (XAI) method. We find that SSTs in the South-Central Pacific influence SWUS precipitation through an atmospheric bridge dynamical pathway, justifying the CNN's sensitivity physically. The fact that we cannot fully trust the evolution of SST patterns in AOGCMs has many implications for the field of climate science and for how the world's governments and organizations respond to global warming. It is critical for climate change adaptation and mitigation assessments to consider this previously unaccounted for uncertainty in climate projections. Climate scientists can do this by developing SST pattern storylines based on theory, observations, and our understanding of the ocean-atmosphere system. If we fail to communicate known uncertainties for both global-mean and regional projections, the world could lose faith in the climate science community, resulting in less of a global response to climate change.Item Open Access From surface to tropopause: on the vertical structure of the tropical cyclone vortex(Colorado State University. Libraries, 2024) DesRosiers, Alexander J., author; Bell, Michael M., advisor; Barnes, Elizabeth A., committee member; Rasmussen, Kristen L., committee member; Davenport, Frances V., committee memberThe internal vortex structure of a tropical cyclone (TC) influences intensity change. Beneficial structural characteristics that allow TCs to capitalize on favorable environmental conditions are an important determinant as to whether a TC will undergo rapid intensification (RI) or not. Accurately forecasting RI is a significant challenge and past work identified characteristics of radial and azimuthal structure of the tangential winds which favor RI, but vertical structure has received less attention. This dissertation aims to define vertical structure in a consistent manner to improve our understanding of how it influences intensity change in observed and modeled TCs, as well as discern when strong winds are more likely to reach the surface with potential for greater impacts. Part 1 investigates the height of the vortex (HOV) in observed TCs and its potential relationships with intensity and intensification rate. As a TC intensifies, the tangential wind field expands vertically and increases in magnitude. Past work supports the notion that vortex height is important throughout the TC lifecycle. The Tropical Cyclone Radar Archive of Doppler Analyses with Recentering (TC-RADAR) dataset provides kinematic analyses for calculation of HOV in observed TCs. Analyses are azimuthally-averaged with tangential wind values taken along the radius of maximum winds (RMW). A threshold-based technique is used to determine the HOV. A fixed-threshold HOV strongly correlates with current TC intensity. A dynamic HOV (DHOV) metric quantifies vertical decay of the tangential wind normalized to its maximum at lower levels with reduced intensity dependence. DHOV exhibits a statistically significant relationship with TC intensity change with taller vortices favoring intensification. A tall vortex is always present in observed cases meeting a pressure-based RI definition in the following 24-hr period, suggesting DHOV may be useful to intensity prediction. In Part 2, numerical modeling simulations are utilized to discern mechanisms responsible for the observed relationships in Part 1. Vertical wind shear (VWS) can tilt the TC vortex by misaligning the low- and mid-level circulation centers which prevents intensification until realignment occurs. Both observed and simulated TCs with small vortex tilt magnitudes possess DHOV values consistent with those observed prior to RI. In aligned TC intensification, DHOV and intensity have a mutually increasing relationship, indicating the metric provides useful information about vertical structure in both tilted and aligned TCs. Vertical vortex growth during RI is sensitive to internal processes which strengthen the TC warm core in the upper-levels of the troposphere. Comparison of a TC simulated in the presence of a concentrated upper-level jet of VWS to a control simulation in quiescent flow indicates that disruption of intensification in the upper levels limits vortex height and intensity without appreciable low- to mid-level tilt. Part 3 focuses on decay of the TC wind field as it encounters friction near the surface in the planetary boundary layer (PBL). Surface winds are important to operational TC intensity estimation, but direct observations within the PBL are rare. Forecasters use reduction factors formulated with wind ratios (WRs) from winds observed by aircraft in the free troposphere and surface winds. WRs help reduce stronger winds aloft to their expected weaker values at the surface. Asymmetries in the TC wind field such as those induced by storm motion can limit the accuracy of static existing WR values employed in operations. A large training dataset of horizontally co-located wind measurements at flight level and the surface is constructed to train a neural network (NN) to predict WRs. A custom loss function ensures the model prioritizes accurate prediction of the strongest wind observations which are uncommon. The NN can leverage relevant physical relationships from the observational data and predict a surface wind field in real-time for forecasters with greater accuracy than the current operational method, especially in high winds.Item Open 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 memberImagery 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.Item Open Access Interactions between the Madden-Julian oscillation and mesoscale to global scale phenomena(Colorado State University. Libraries, 2019) Toms, Benjamin A., author; van den Heever, Susan C., advisor; Barnes, Elizabeth A., committee member; Maloney, Eric D., committee member; Cooley, Daniel, committee memberThe Madden-Julian Oscillation (MJO) influences and interacts with atmospheric phenomena across the globe, from the tropics to the poles. In this two-part study, the interactions of the MJO with other phenomena across a broad range of scales are considered, including mesoscale convective structures within the tropics and global teleconnection patterns. While the two studies are distinct in the scales of the interactions they discuss, each highlights an aspect of the importance of interactions between the MJO and variability across a broad range of scales within the climate system. The study of such cross-scale interactions is important for understanding our climate system, as these interactions can transfer energy between phenomena of starkly different spatial and temporal scales. Part one of the study uses a cloud-resolving model, the Regional Atmospheric Modeling System, to consider the relationship between mesoscale convective structures within the Indo-Pacific region and the regional, intraseasonal anomalies associated with the MJO. The simulation captures the entirety of a canonical boreal summertime MJO event, spanning 45 days in July and August of 2016, during which the convective anomaly associated with the MJO propagated over the Maritime Continent. The convective cloud structures, or cells, within the simulation were tracked and logged according to their location relative to the regional convective anomaly of the MJO. Using both spectral analysis and phase compositing, it was found that a progressive relationship exists between the boreal summertime MJO and mesoscale deep convective structures within the Indo-Pacific region, specifically within the convectively enhanced region of the MJO, as follows: increased cell longevity in the initial phases of the MJO, followed by increased cell number in the intermediate phases, progressing into increased cell expanse in the terminal phases. This progressive relationship is connected back to the low-frequency atmospheric response to the MJO. It is suggested that the bulk thermodynamic and kinematic anomalies of the MJO are closely related to the convective cell expanse and longevity, although the number of convective cells appears to be tied to another source of variability not identified within this study. These findings emphasize that while the MJO is commonly defined as an intraseasonal-scale convective anomaly, it is also intrinsically tied to the mesoscale variability of the convective systems that constitute its existence. The second part of the study quantifies the prevalence of the MJO within the overall climate system, along with the dependence of its teleconnections on variability in another tropical phenomena on a larger scale than itself. It is well known that the MJO exhibits pronounced seasonality in its tropical and global signature, and recent research has suggested that its tropical structure also depends on the state of the Quasi-Biennial Oscillation (QBO). We therefore first quantify the relationship between 300-mb geopotential anomalies and the MJO across the globe, then test the dependence of the relationship on both the meteorological season and the QBO phase using a derivative of cross-spectral analysis, magnitude-squared coherence Coh2. It is found that the global upper-tropospheric signature of the MJO exhibits pronounced seasonality, but also that the QBO significantly modulates the upper-tropospheric tropical and extratropical anomalies associated with the MJO. Globally, variability in upper tropospheric geopotential linked to the MJO is maximized during the boreal summertime and wintertime of easterly QBO phases, which is consistent with previous research that has shown easterly QBO phases to enhance the persistence of tropical convection associated with the MJO. Additional features are identified, such as the global maximum in upper-tropospheric variability associated with the MJO occurring during boreal summertime, rather than boreal wintertime. Overall, the MJO explains seven to thirteen percent of intraseasonal atmospheric variability in 300-mb geopotential, depending on season and QBO phase. These results highlight the importance of considering the phase of the QBO in analyses related to either global or local impacts of the MJO, along with the importance of cross-scale relationships, such as those between the MJO and QBO, in governing the coupling between the MJO and teleconnections across the globe. This thesis considers the relationship between the MJO and processes that operate on both longer and shorter timescales than itself, including tropical convection and the Quasi-Biennial Oscillation. In doing so, this work highlights the importance of considering relationships between the MJO and atmospheric phenomena on different spatial and temporal scales and with origins distinct from the MJO itself. While theories exist describing the MJO as its own distinct entity, this research corroborates the idea that it is at its core fundamentally linked to the rest of the climate system, both modulating and being modulated by a broad range of atmospheric processes.Item Open Access Madden-Julian oscillation teleconnections and their influence on Northern Hemisphere winter blocking(Colorado State University. Libraries, 2017) Henderson, Stephanie A., author; Maloney, Eric D., advisor; Barnes, Elizabeth A., committee member; Thompson, David W. J., committee member; Chong, Edwin K. P., committee memberWinter blocking events are characterized by persistent and quasi-stationary patterns that re-direct precipitation and air masses, leading to long-lasting extreme winter weather. Studies have shown that the teleconnection patterns forced by the primary mode of tropical intraseasonal variability, the Madden-Julian Oscillation (MJO), influence extratropical factors associated with blocking, such as the North Atlantic Oscillation. However, the influence of the MJO on winter blocking is not well understood. Understanding this relationship may improve the mid-range forecasting of winter blocking and the associated weather extremes. The impact of the MJO on Northern Hemisphere winter blocking is examined using a two-dimensional blocking index. Results suggest that all MJO phases demonstrate significant changes in west and central Pacific high-latitude blocking. East Pacific and Atlantic blocking are significantly suppressed following phase 3 of the MJO, characterized by anomalous convection in the tropical East Indian Ocean and suppressed convection in the west Pacific. A significant increase in east Pacific and Atlantic blocking follows the opposite-signed MJO heating during MJO phase 7. Over Europe, blocking is suppressed following MJO phase 4 and significantly increased after MJO phase 6. Results suggest that the European blocking increase may be due to two precursors: 1) a pre-existing anomalous Atlantic anticyclone, and 2) a negative Pacific North American (PNA) pattern triggered by the MJO. The influence of the MJO on winter blocking may be different if a change occurs to the basic state and/or MJO heating, such as during El Niño – Southern Oscillation (ENSO) events. MJO teleconnections during ENSO events are examined using composite analysis and a nonlinear baroclinic model and their influence of winter high-latitude blocking is discussed. Results demonstrate that the ENSO-altered MJO teleconnection patterns significantly influence Pacific and Atlantic blocking and the impacts depend on ENSO phase. During El Niño, Pacific and Atlantic blocking is significantly increased following MJO phase 7, with maximum Atlantic blocking frequency anomalies reaching triple the climatological winter mean blocking frequency. Results suggest that the MJO forces the initial anomalous Atlantic dipole associated with the blocking increase, and transient eddy activity aids in its persistence. During La Niña, significant changes to high-latitude blocking are mostly observed during the first half of an MJO event, with significant suppression of Pacific and Atlantic blocking following MJO phase 3. MJO teleconnection patterns may also be altered by basic state and MJO heating biases in General Circulation Models (GCMs), important for mid-range forecasting and future climate studies of weather and climate patterns significantly altered by the MJO, such as winter blocking. Data from phase 5 of the Coupled Model Intercomparison Project (CMIP5) is used to investigate MJO teleconnection biases due to basic state and MJO biases, and a linear baroclinic model is used to interpret the results. Results indicate that poor basic state GCMs (but with a good MJO) can have equally poor skill in simulating the MJO teleconnection patterns as GCMs with a poor MJO. Large biases in MJO teleconnection patterns occur in GCMs with a zonally extended Pacific subtropical jet relative to reanalysis. In good MJO GCMs, bias in the location and horizontal structure of Indo-Pacific MJO heating is found to have modest impacts on MJO teleconnection patterns. However, East Pacific heating during MJO events can influence MJO teleconnection amplitude and the pathways over North America. Results suggest that both the MJO and the basic state must be well represented in order to properly capture the MJO teleconnection patterns.Item Open Access On quasi-periodic Baroclinic variability in the extratropical circulation(Colorado State University. Libraries, 2016) Crow, Brian, author; Thompson, David W. J., advisor; Barnes, Elizabeth A., committee member; Aster, Richard C., committee memberA number of recent studies have demonstrated that large-scale extratropical wave activity is characterized by quasi-periodic behavior on timescales of 20-30 days, particularly in the Southern Hemisphere. This phenomenon has been termed the Baroclinic Annular Mode (BAM), and is responsible for the modulation of eddy heat fluxes, eddy kinetic energy, and precipitation on large scales. However, the extent to which this periodic modulation is discernable or significant on smaller spatial scales had not yet been established. Using data from the ECMWF Interim Reanalysis for the period 1979-2014, this study extensively examines the spatial structure of the BAM. Spectral analyses reveal the spatial limitations of the periodic behavior, while lag-correlation analyses reveal the patterns of propagation and development of anomalies that give rise to the observed periodicity. Periodic behavior is more robust in the Southern Hemisphere than in the Northern Hemisphere, but filtering out low wavenumbers from NH data helps clarify the BAM signal. Additionally, it is demonstrated that the BAM appears very differently in two relatively similar global climate models, suggesting further study is needed to determine how modern GCMs capture the BAM. Supplementing our analyses of observed and modeled data is a simple two-way linear feedback model, which is utilized to demonstrate the principal mechanism underlying periodic behavior in the BAM. The model makes it apparent that the BAM can be modeled as a simple linear feedback between baroclinicity and eddy heat fluxes. The periodicity seen on larger scales is a product of differential advection rates affecting the development of spatially overlapping, out-of-phase anomalies. The large-scale nature of the periodic behavior, however, makes it difficult to draw conclusions about the potential utility of the BAM for weather analysts and forecasters, and the limitations of this study limit our ability to describe its role in the climate system. It is hoped that the research presented here will pave the way to future studies which may more thoroughly answer such questions.Item Open Access Quantifying internal climate variability and its changes using large-ensembles of climate change simulations(Colorado State University. Libraries, 2020) Li, Jingyuan, author; Thompson, David W. J., advisor; Barnes, Elizabeth A., committee member; Ravishankara, A. R., committee member; Cooley, Daniel, committee memberIncreasing temperatures over the last 50 years have led to a multitude of studies on observed and future impacts on surface climate. However, any changes on the mean need to be placed in the context of its variability to be understood and quantified. This allows us to: 1) understand the relative impact of the mean change on the subsequent environment, and 2) detect and attribute the external change from the underlying "noise" of internal variability. One way to quantify internal variability is through the use of large ensemble models. Each ensemble member is run on the same model and with the same external forcings, but with slight differences in the initial conditions. Differences between ensemble members are due solely to internal variability. This research exploits one such large ensemble of climate change simulations (CESM-LE) to better understand and evaluate surface temperature variability and its effects under external forcing. One large contribution to monthly and annual surface temperature variability is the atmospheric circulation, especially in the extratropics. Dynamical adjustment seeks to determine and remove the effects of circulation on temperature variability in order to narrow the range of uncertainty in the temperature response. The first part of this work compares several commonly used dynamical adjustment methods in both a pre-industrial control run and the CESM-LE. Because there are no external forcings in the control run, it is used to provide a quantitative metric by which the methods are evaluated. We compare and assess these dynamical adjustment methods on the basis of 2 attributes: 1) the method should remove a maximum amount of internal variability while 2) preserving the true forced signal. While the control run is excellent for assessing the methods in an "ideal" environment, results from the CESM-LE show biases in the dynamically-adjusted trends due to a forced response in the circulation fields themselves. This work provides a template from which to assess the various dynamical adjustment methods available to the community. A less studied question is how internal variability itself will respond to climate change. Past studies have found regional changes in surface temperature variance and skewness. This research also investigates the impacts of climate change on day-to-day persistence of surface temperature. Results from the CESM-LE suggest that external warming generally increases surface temperature persistence, with the largest changes over the Arctic and ocean regions. The results are robust and distinct from internal variability. We suggest that persistence changes are mostly due to an increase in the optical thickness of the atmosphere due to increases in both carbon dioxide and water vapor. This increased optical thickness reduces the thermal damping of surface temperatures, increasing their persistence. Model results from idealized aquaplanet simulations with different radiation schemes support this hypothesis. The results thus reflect a robust thermodynamic and radiative constraint on surface temperature variability.Item Open Access The dynamics of Hadley circulation variability and change(Colorado State University. Libraries, 2017) Davis, Nicholas Alexander, author; Birner, Thomas, advisor; Randall, David A., committee member; Barnes, Elizabeth A., committee member; Venayagamoorthy, Subhas K., committee member; Randel, William J., committee memberThe Hadley circulation exerts a dominant control on the surface climate of earth's tropical belt. Its converging surface winds fuel the tropical rains, while subsidence in the subtropics dries and stabilizes the atmosphere, creating deserts on land and stratocumulus decks over the oceans. Because of the strong meridional gradients in temperature and precipitation in the subtropics, any shift in the Hadley circulation edge could project as major changes in surface climate. While climate model simulations predict an expansion of the Hadley cells in response to greenhouse gas forcings, the mechanisms remain elusive. An analysis of the climatology, variability, and response of the Hadley circulation to radiative forcings in climate models and reanalyses illuminates the broader landscape in which Hadley cell expansion is realized. The expansion is a fundamental response of the atmosphere to increasing greenhouse gas concentrations as it scales with other key climate system changes, including polar amplification, increasing static stability, stratospheric cooling, and increasing global-mean surface temperatures. Multiple measures of the Hadley circulation edge latitudes co-vary with the latitudes of the eddy-driven jets on all timescales, and both exhibit a robust poleward shift in response to forcings. Further, across models there is a robust coupling between the eddy-driving on the Hadley cells and their width. On the other hand, the subtropical jet and tropopause break latitudes, two common observational proxies for the tropical belt edges, lack a strong statistical relationship with the Hadley cell edges and have no coherent response to forcings. This undermines theories for the Hadley cell width predicated on angular momentum conservation and calls for a new framework for understanding Hadley cell expansion. A numerical framework is developed within an idealized general circulation model to isolate the mean flow and eddy responses of the global atmosphere to radiative forcings. It is found that it is primarily the eddy response to greenhouse-gas-like forcings that causes Hadley cell expansion. However, the mean flow changes in the Hadley circulation itself crucially mediate this eddy response such that the full response comes about due to eddy-mean flow interactions. A theoretical scaling for the Hadley cell width based on moist static energy is developed to provide an improved framework to understand climate change responses of the general circulation. The scaling predicts that expansion is driven by increases in the surface latent heat flux and the width of the rising branch of the circulation and opposed by increases in tropospheric radiative cooling. A reduction in subtropical moist static energy flux divergence by the eddies is key, as it tilts the energetic balance in favor of expansion.Item Open Access Towards understanding the role of natural variability in climate change(Colorado State University. Libraries, 2017) Li, Jingyuan, author; Thompson, David W. J., advisor; Barnes, Elizabeth A., committee member; Cooley, Daniel, committee memberNatural variability plays a large role in determining surface climate on local and regional scales. Understanding the role of natural variability is crucial for accurately assessing and attributing climate trends, both past and future. One successful way to examine the role of natural variability in climate change has been through large ensembles of climate models. This thesis uses one such large ensemble (the NCAR CESM-LE) to test various methods used to quantify natural variability in the context of climate change. We first introduce a simple analytic expression for calculating the lead time required for a linear trend to emerge in a Gaussian first order autoregressive process. The expression is derived from the standard error of the regression and is tested using the CESM-LE. It is shown to provide a robust estimate of the point in time when the forced signal of climate change has emerged from the natural variability of the climate system with a predetermined level of statistical confidence. The expression provides a novel analytic tool for estimating the time of emergence of anthropogenic climate change and its associated regional climate impacts from either observed or modeled estimates of natural variability and trends. We next compare and analyze various methods for calculating the effects of internal circulation dynamics on surface temperature. Dynamical adjustment seeks to separate out dynamical contribution to temperature trends, thus reducing the amplitude of natural variability that obscures the signal of anthropogenic forcing. Three specific methods used in the climate literature are examined: principal component analysis (PCR), maximum covariance analysis (MCA), and constructed circulation analogs. An assessment of these methods are given with their respective results from the CESM control run and large ensemble.