Mountain Scholar :: Browsing by Author "Barnes, Elizabeth A., advisor"

Browsing by Author "Barnes, Elizabeth A., advisor"

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Open Access
Application of neural networks to subseasonal to seasonal predictability in present and future climates
(Colorado State University. Libraries, 2022) Mayer, Kirsten J., author; Barnes, Elizabeth A., advisor; Hurrell, James W., committee member; Maloney, Eric D., committee member; Anderson, Charles, committee member
The Earth system is known for its lack of predictability on subseasonal to seasonal timescales (S2S; 2 weeks to a season). Yet accurate predictions on these timescales provide crucial, actionable lead times for agriculture, energy, and water management sectors. Fortunately, specific Earth system states – deemed forecasts of opportunity – can be leveraged to improve prediction skill. Our current understanding of these opportunities are rooted in our knowledge of the historical climate. Depending on societal actions, the future climate could vary drastically, and these possible futures could lead to varying changes to S2S predictability. In recent years, neural networks have been successfully applied to weather and climate prediction. With the rapid development of neural network explainability techniques, the application of neural networks now provides an opportunity to further understand our climate system as well. The research presented here demonstrates the utility of explainable neural networks for S2S prediction and predictability changes under future climates. The first study presents a novel approach for identifying forecasts of opportunity in observations using neural network confidence. It further demonstrates that neural networks can be used to gain physical insight into predictability, through neural network explainability techniques. We then employ this methodology to explore S2S predictability differences in two future scenarios: under anthropogenic climate change and stratospheric aerosol injection (SAI). In particular, we explore subseasonal predictability and forecasts of opportunity changes under anthropogenic warming compared to a historical climate in the CESM2-LE. We then investigate how future seasonal predictability may differ under SAI compared to a future without SAI deployment in the ARISE-SAI simulations. We find differences in predictability between the historical and future climates and the two future scenarios, respectively, where the largest differences in skill generally occur during forecasts of opportunity. This demonstrates that the forecast of opportunity approach, presented in the first study, is useful for identifying differences in future S2S predictability that may not have been identified if examining predictability across all predictions. Overall, these results demonstrate that neural networks are useful tools for exploring subseasonal to seasonal predictability, its sources, and future changes.
Open Access
Approaching Arctic-midlatitude dynamics from a two-way feedback perspective
(Colorado State University. Libraries, 2019) McGraw, Marie C., author; Barnes, Elizabeth A., advisor; Randall, David A., committee member; Schumacher, Russ S., committee member; Venayagamoorthy, Karan, committee member
Arctic variability and the variability of the midlatitude circulation are closely intertwined. Although these connections are interrelated and bi-directional, and occur on a variety of timescales, they are not often studied together. Modeling studies generally focus on a single direction of influence--usually, how the midlatitude circulation responds to the Arctic. Studying these relationships in a two-way feedback perspective can offer new insights into these connections, providing information on how they feed back upon each other. This work approaches Arctic-midlatitude dynamics from a two-way feedback perspective, mostly on sub-monthly timescales. Particular emphasis is placed on the influence of midlatitude circulation variability upon the Arctic, as this direction of influence is less-studied than the converse pathway. Reinforcing feedback loops are identified between the North Pacific and North Atlantic jet streams and the Arctic. Variability in both the North Atlantic and North Pacific jet streams drives Arctic variability, which then drives further variability in the jet streams. The circulation variability in many regions, including North America, the east Pacific and Alaska, and Siberia, drives Arctic variability far more than it is driven by Arctic variability. These relationships exhibit substantial regional variability, stressing the important role of an analytical approach that incorporates this spatial heterogeneity. The two-way nature of Arctic-midlatitude connections is also explored in the context of Arctic moisture fluxes. The circulation response to sea ice loss also drives changes in Arctic moisture fluxes, with moisture flux out of the Arctic increasing more than moisture flux into the Arctic. The two-way feedback perspective explored in this research is built around the ideas of causal discovery, particularly Granger causality. Most of these two-way Arctic-midlatitude relationships are considered in the context of added variance explained, or added predictive power. That is, these relationships are characterized by comparing how much an additional predictor improves predictability beyond autocorrelation. Limiting the ability of autocorrelation to color these results emphasizes added variance explained--how much additional variance in the circulation can be explained by Arctic temperature variability, and vice versa? As an example, many recent studies have concluded that warm Arctic temperatures or low sea ice conditions drive a strengthening of high pressures and an increase in cold temperatures over Siberia. However, when memory and autocorrelation are accounted for, it emerges that the circulation variability over Siberia drives a response in the Arctic more than the other way around--results that are in concordance with modeling studies that have also disputed the veracity of the claim of the Arctic driving a strong response in Siberia. Ultimately, this research seeks to offer a different perspective on analyzing climate dynamics, with an emphasis on two-way feedbacks. While targeted climate modeling studies offer great physical insights, and provide substantial opportunities to explore and test physical mechanisms, they are often limited to exploring only one pathway of influence. In reality, these relationships do go in both directions, and a comprehensive understanding of such large-scale interactions between different parts of the atmosphere must ultimately consider the two-way relationships. The causal discovery methods used in much of this research can be used in conjunction with modeling studies to better understand these two-way relationships, and improve interpretation of results. While this research has focused on the relationships between the Arctic and the midlatitude circulation on sub-seasonal timescales, the broad framework and ideas presented within can be more widely applied to many other questions in climate variability studies. Thus, this work has also put a special emphasis on describing and implementing these causality-based methods in a manner that is accessible and interpretable for atmospheric and climate scientists.
Open Access
Assessing outcomes in stratospheric aerosol injection scenarios shortly after deployment
(Colorado State University. Libraries, 2022) Hueholt, Daniel M., author; Hurrell, James W., advisor; Barnes, Elizabeth A., advisor; Conant, Richard T., committee member
Current global actions to reduce greenhouse gas emissions are very likely to be insufficient to meet climate targets outlined under the Paris Agreement. This motivates performing research on possible methods for intervening in the Earth system to minimize climate risk while decarbonization efforts continue. One such hypothetical climate intervention is stratospheric aerosol injection (SAI), where reflective particles would be emitted into the stratosphere to cool the planet by reducing solar insolation. The climate response to SAI is not well understood, particularly on short-term time horizons frequently used by decision makers and planning practitioners to assess climate information. This knowledge gap limits informed discussion of SAI outside the scientific community. We demonstrate two framings to explore the climate response in the decade after SAI deployment in modeling experiments with parallel SAI and no-SAI simulations. The first framing, which we call a snapshot around deployment, displays change over time within the SAI scenarios and corresponds to the question "What happens before and after SAI is deployed in the model?" The second framing, the intervention impact, displays the difference between the SAI and no-SAI simulations, corresponding to the question "What is the impact of a given intervention relative to climate change with no intervention?" We apply these framings to annual mean 2-meter temperature, precipitation, and a precipitation extreme in the first two experiments to use ensembles of Earth system models that comprehensively represent both the SAI injection process and climate response, and connect these results to implications for other climate variables. The parallel SAI and no-SAI simulations in these experiments allow us to explore the climate response in the context of the response to SAI, the underlying greenhouse gas forcing scenario, and the noise from internal climate variability.
Open Access
Assessment of numerical weather prediction model re-forecasts of atmospheric rivers along the west coast of North America
(Colorado State University. Libraries, 2018) Nardi, Kyle M., author; Barnes, Elizabeth A., advisor; Schumacher, Russ S., committee member; Ham, Jay M., committee member
Atmospheric rivers (ARs) - narrow corridors of high atmospheric water vapor transport - occur globally and are associated with flooding and maintenance of the regional water supply. Therefore, it is important to improve forecasts of AR occurrence and characteristics. Although prior work has examined the skill of numerical weather prediction (NWP) models in forecasting ARs, these studies only cover several years of re-forecasts from a handful of models. Here, we expand this previous work and assess the performance of 10-30 years of wintertime (November-February) AR landfall re-forecasts from nine operational weather models, obtained from the International Subseasonal to Seasonal (S2S) Project Database. Model errors along the West Coast of North America at leads of 1-14 days are examined in terms of AR occurrence, intensity, and landfall location. We demonstrate that re-forecast performance varies across models, lead times, and geographical regions. Occurrence-based skill approaches that of climatology at 14 days, while models are, on average, more skillful at shorter leads in California, Oregon, and Washington compared to British Columbia and Alaska. We also find that the average magnitude of landfall Integrated Water Vapor Transport (IVT) error stays fairly constant across lead times, although over-prediction of IVT is more common at later lead times. We then show that northward landfall location errors are favored in California, Oregon, and Washington, although southward errors occur more often than expected from climatology. We next explore the link between the predictability of ARs at 1-14 days and synoptic-scale weather conditions by examining re-forecasts of 500-hPa geopotential height anomaly patterns conducive to landfalling ARs. Finally, the potential for skillful forecasts of IVT and precipitation at subseasonal to seasonal (S2S) leads is explored using an empirical forecast model based on the Madden-Julian oscillation (MJO) and the quasi-biennial oscillation (QBO). Overall, these results highlight the need for model improvements at 1-14 days, while helping to identify factors that cause model errors as well as sources of additional predictability.
Open Access
Climatology and variability of atmospheric rivers over the north Pacific
(Colorado State University. Libraries, 2017) Mundhenk, Bryan D., author; Barnes, Elizabeth A., advisor; Maloney, Eric D., advisor; Randall, David A., committee member; Ham, Jay M., committee member
Atmospheric rivers (ARs) are plumes of intense water vapor transport that dominate the flux of water vapor into and within the extratropics. Upon landfall, ARs are a major source of precipitation and often trigger weather and/or hydrologic extremes. Over time, landfalling AR activity, or a lack thereof, can influence periods of regional water abundance or drought. An objective detection algorithm is developed to identify and characterize these features using gridded fields of anomalous vertically integrated water vapor transport. Output from this algorithm enables the investigation into the relationships between tropical variability and ARs over the North Pacific undertaken in this dissertation. In the first segment of this study, an all-season analysis of AR incidence within the North Pacific basin is performed for the period spanning 1979 to 2014. The variability of AR activity due to the seasonal cycle, the El Nino-Southern Oscillation (ENSO) cycle, and the Madden-Julian oscillation (MJO) is presented. The results highlight that ARs exist throughout the year over the North Pacific. In general, the seasonal cycle manifests itself as northward and westward displacement of AR activity during boreal summer, rather than a seasonal change in the total number of ARs within the domain. It is also shown that changes to the North Pacific mean-state due to the ENSO cycle and the MJO may enhance or completely offset the seasonal cycle of AR activity, but that such influences vary greatly based on location within the basin. The second segment of this study investigates ARs at high northern latitudes. Comparatively little is known about the dynamics supporting these ARs in contrast to their mid-latitude counterparts. ARs are found to occur near the Gulf of Alaska and the U.S. West Coast with similar frequency, but with different seasonality. Composited atmospheric conditions reveal that a broad height anomaly over the northeast Pacific is influential to AR activity near both of these regions. When a positive height anomaly exists over the northeast Pacific, AR activity is often deflected poleward toward Alaska, while the U.S. West Coast experiences a decrease in AR activity, and vice versa. This tradeoff in AR activity between these two regions applies across a range of time scales, not just with respect to individual transient waves. Both ARs and height anomalies are found to be associated with Rossby wave breaking, thereby dynamically linking the modulation of AR activity with broader North Pacific dynamics. The third segment of this study explores the predictability of anomalous landfalling AR activity within the subseasonal time scale (approximately 2-5 weeks). An empirical prediction scheme based solely on the initial state of the MJO and the stratospheric quasi-biennial oscillation (QBO) is constructed and evaluated over 36 boreal winter seasons. This scheme is based on the premise that the MJO modulates landfalling AR activity along the west coast of North America within the subseasonal time scale by exciting large-scale circulation anomalies over the North Pacific. The QBO is found to further modulate the MJO--AR relationship. The prediction scheme reveals skillful subseasonal "forecasts of opportunity" when knowledge of the MJO and the QBO can be leveraged to predict periods of increased or decreased AR activity. Moreover, certain MJO and QBO phase combinations provide predictive skill competitive with, or even exceeding, a state-of-the-art numerical weather prediction model.
Open 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.
Open Access
Impacts of Arctic warming and sea ice loss on the Northern Hemisphere mid-latitude large-scale circulation
(Colorado State University. Libraries, 2020) Ronalds, Bryn, author; Barnes, Elizabeth A., advisor; Thompson, David, committee member; Randall, David A., committee member; Eykholt, Richard, committee member
The consequences of the rapid warming of the Arctic and associated sea ice loss on the Northern Hemisphere atmospheric circulation is still largely debated. The uncertainty in the circulation response stems from a poor understanding of the underlying physical mechanisms of the remote response, regional and seasonal differences, differences between models and experimental set-ups, the large internal variability of the system, and the short observational record. This research seeks to address some of this uncertainty, specifically the uncertainty related to the physical mechanisms, regionality, and modeling differences. The wintertime Northern Hemisphere eddy-driven jet streams over the North Pacific and North Atlantic basins exhibit differing responses to Arctic warming and sea ice loss in a fully coupled climate model. In the North Atlantic the jet weakens, narrows along the poleward flank, and shifts slightly equatorward. This response is similar to previous studies examining the Northern Hemisphere zonal mean jet response. In contrast, the North Pacific jet strengthens and extends eastward in response to Arctic sea ice loss, with no change in latitude, and narrows slightly along the poleward flank. In both cases, there are high latitude anomalous easterlies in the region of sea ice loss, where the local surface temperature gradients are weakening. This can lead to changes in locations and frequency of wave-breaking, thus leading to changes in the mean zonal winds further south, in the vicinity of the jet. This work relates the differing changes in the North Pacific and North Atlantic to these changes in wave-breaking in a simplified atmospheric model, and posits that the location of the jet relative to the region of Arctic sea ice loss is a dominant factor in determining the mean jet response to the sea ice loss and local warming. Changes in the mean wintertime Northern Hemisphere midlatitude zonal winds are found to be indicative of changes to the sub-seasonal variability of the wintertime zonal winds. The sub-seasonal circulation patterns over the ocean basins are closely linked with continental weather regimes, including changes in temperature and precipitation. While establishing a causal link between Arctic sea ice loss and changes to remote weather regimes in the observational record remains difficult, the Polar Amplification Model Intercomparison Project (PAMIP) provides insight into possible relationships and consequences. The design of the project eliminates differences in experimental set-ups across models and aids in addressing the uncertainty in regional responses. Across four climate models, Arctic sea ice loss leads to a strengthened and extended North Pacific jet in the January-February mean. This mean change is also associated with changes to the sub-seasonal, wintertime North Pacific zonal wind variability. All four models show an increase in strengthened and extended North Pacific eddy-driven jet stream events and a decrease in weakened, retracted and equatorward-shifted North Pacific jet events in January-February. Previous work has also established the relationships between North Pacific jet stream variability and downstream, North American weather regimes, and changes to the former are expected to impact the latter. Again, there is model agreement in an increase of a warm west/cold east temperature dipole over North America, associated with the strengthened and extended jet events. There is also a decrease in cold air temperature anomalies over North America, associated with weakened and equatorward-shifted jet events.
Open Access
Midlatitude prediction skill following QBO-MJO activity on subseasonal to seasonal timescales
(Colorado State University. Libraries, 2019) Mayer, Kirsten J., author; Barnes, Elizabeth A., advisor; Maloney, Eric, committee member; Anderson, Chuck, committee member
The Madden-Julian Oscillation (MJO) is known to force extratropical weather days-to-weeks following an MJO event through excitation of Rossby waves, also known as tropical-extratropical teleconnections. Prior research has demonstrated that this tropically forced midlatitude response can lead to increased prediction skill on subseasonal to seasonal (S2S) timescales. Furthermore, the Quasi-Biennial Oscillation (QBO) has been shown to possibly alter these teleconnections through modulation of the MJO itself and the atmospheric basic state upon which the Rossby waves propagate. This implies that the MJO-QBO relationship may affect midlatitude circulation prediction skill on S2S timescales. In this study, we quantify midlatitude circulation sensitivity and prediction skill following active MJOs and QBOs across the Northern Hemisphere on S2S timescales through an examination of the 500 hPa geopotential height field. First, a comparison of the spatial distribution of Northern Hemisphere sensitivity to the MJO during different QBO phases is performed for ERA-Interim reanalysis as well as ECMWF and NCEP hindcasts. Secondly, differences in prediction skill in ECMWF and NCEP hindcasts are quantified following MJO-QBO activity. We find that regions across the Pacific, North America and the Atlantic exhibit increased prediction skill following MJO-QBO activity, but these regions are not always collocated with the locations most sensitive to the MJO under a particular QBO state. Both hindcast systems demonstrate enhanced prediction skill 7-14 days following active MJO events during strong QBO periods compared to MJO events during neutral QBO periods.
Open Access
Quantifying and understanding current and future links between tropical convection and the large-scale circulation
(Colorado State University. Libraries, 2020) Jenney, Andrea M., author; Randall, David A., advisor; Barnes, Elizabeth A., advisor; Maloney, Eric, committee member; Rasmussen, Kristen, committee member; Anderson, Georgiana Brooke, committee member
Tropical deep convection plays an important role in the variability of the global circulation. The Madden Julian Oscillation (MJO) is a large tropical organized convective system that propagates eastward along the equator. It is a key contributor to weather predictability at extended time scales (10-40 days). For example, variability in the MJO is linked with variability in meteorological phenomena such as landfalling atmospheric rivers, tornado and hail activity over parts of North America, and extreme temperature and rainfall patterns across the Northern Hemisphere. Links between the MJO and atmospheric variability in remote locations are heavily studied. This is in part because the current skill of weather forecasts at extended time scales is mediocre, and because of evidence suggesting that the potential predictability offered by the MJO may not be fully captured in numerical prediction models. In the first part of this dissertation, I develop a tool for these types of studies. The "Sensitivity to the Remote Influence of Periodic Events" (STRIPES) index is a novel index that condenses the information obtained through composite analysis of variables after a periodic event (such as the MJO) into a single number, which includes information about the life cycle of the event, and for a range of lags with respect to each stage of the event. I apply the STRIPES index to surface observations and show that the MJO signal is detectable and significant at the level of individual weather stations over many parts of North America, and that the maximum strength of this signal exhibits regionality and seasonality. Tropical convection affects the extratropics primarily through the excitation of Rossby waves at the places where the upper-tropospheric divergent outflow associated with deep convection interacts with the background wind. In a future warmer climate, the strength of the mean circulation and convective mass flux is expected to weaken. A potential consequence is a weakening of Rossby wave excitation by tropical convective systems such as the MJO. In the second part of this study, I analyze a set of idealized simulations with specified surface warming and superparameterized convection and develop a framework to better understand why the mean circulation weakens with warming. I show that the decrease in the strength of the mean circulation can be explained by the slow rate at which atmospheric radiative cooling intensifies relative to the comparatively fast rate that the tropical dry static stability increases. I also show that despite a decrease in the mean convective mass flux, the warming tendency of the convective mass flux over the most deeply- convecting regions is not constrained to follow that of the global mean. In the final part of this dissertation, I consider how changes in the MJO and of the mean atmospheric state due to warming from increases in greenhouse gas concentrations may lead to changes in the MJO's impact over the North Pacific and North America. Specifically, I show that changes to the atmosphere's mean state dry static energy and winds have a larger impact on the MJO teleconnection than changes to MJO intensity and propagation characteristics.
Open Access
Role of Rossby wave breaking in the variability of large-scale atmospheric transport and mixing
(Colorado State University. Libraries, 2017) Liu, Chengji, author; Barnes, Elizabeth A., advisor; Birner, Thomas, committee member; Kiladis, George N., committee member; Schubert, Wayne H., committee member; Venayagamoorthy, Karan, committee member
We demonstrate that Rossby wave breaking (RWB) plays an important role in both horizontal and vertical large-scale transport/mixing in both observations and idealized general circulation models. In the horizontal direction, RWB contributes to a substantial fraction of transient moisture flux into the Arctic. In the vertical direction, RWB modifies thermal stratification near the tropopause which leads to enhanced mass exchange across the tropopause. In understanding the variability of RWB related transport and mixing, we show that it is essential to separate the two types of RWB – anticyclonic wave breaking (AWB) and cyclonic wave breaking (CWB) – for two fundamental differences between them. The first difference is the opposite relationship between jet positions and their frequencies of occurrence. For both horizontal transport of moisture into the Arctic and vertical mixing of ozone across the tropopause, the robust relationship between jet position and AWB/CWB frequency is of first order importance in explaining the large-scale transport/mixing anomaly patterns influenced by climate variabilities involving jet shifting, such as the El-Nino Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). The second robust difference is the mixing strength exhibited by individual AWB and CWB events. In idealized lifecycle and climate simulations, as well as reanalysis data, CWB consistently exhibits stronger mixing strength than AWB. Combined with the robust relationship between jet variability and AWB/CWB frequency, such a difference is demonstrated to translate into a decrease in total upper troposphere diffusivity as the jet shifts poleward in an idealized climate simulation.
Open Access
The Madden Julian oscillation and tropical-extratropical teleconnections
(Colorado State University. Libraries, 2019) Tseng, Kai-Chih, author; Barnes, Elizabeth A., advisor; Maloney, Eric D., advisor; Randall, David A., committee member; Ebert-Uphoff, Imme, committee member
The Madden Julian Oscillation (MJO) excites strong variations in extratropical circulations that have important implications for subseasonal-to-seasonal (S2S) prediction. In particular, certain MJO phases are characterized by a consistent modulation of geopotential height patterns in the North Pacific and North America. Although the MJO's influence in the downstream weather has been widely explored in previous studies, the relationship between robust MJO teleconnection patterns and model prediction skills has received little attention. In this study, the reanalysis data and ensemble hindcasts from numerical weather forecast system are used to quantify the influence of robust MJO teleconnection on model prediction skills. By calculating the pattern consistency of MJO teleconnection, the ability of MJO convection to modulate extratropical weather is quantified over different time lags and phases. The diagnostic result demonstrates that the robust MJO teleconnection in specific MJO phases/lags are also characterized by excellent agreement in the prediction of geopotential height anomalies across model ensemble members at forecast lead of up to 3 weeks. The mechanisms that lead some MJO phases to have more consistent teleconnections than others are examined by using a linear baroclinic model (LBM). The simulation results show that MJO phases 2, 3, 6 and 7 consistently generate Pacific-North America like (PNA-like) pattern on S2S timescales while other phases do not. By employing a Rossby wave source analysis, the result shows that a dipole-like Rossby wave source patterns on each side of the jet in MJO phase 2, 3, 6 and 7 can increase the pattern consistency of teleconnection due to the constructive interference of similar teleconnection signals. On the other hand, the symmetric patterns of Rossby wave source in other phases can dramatically reduce the pattern consistency due to destructive interference. The consistency of MJO teleconnections is also characterized by an interannual variability. During the El Niño years, the pattern consistency is dramatically decreased compared to the La Niña years. Employing the numerical experiments in LBM and applying a Rossby wave ray tracing algorithm, we demonstrate two factors largely determine the interannual variability of MJO teleconnection consistency. During El Niño years, the eastward extension of subtropical jet and a less-dipole like Rossby wave source pattern on each side of the jet dramatically decrease poleward propagating wave signals. By contrast, the competing effect between these two factors results in modest changes in pattern consistency during La Niña years. Thus, the observed consistency of MJO teleconnections is much smaller during El Niño years than La Niña years. The dynamics associated with the pattern consistency of MJO teleconnection are addressed in the first half of this work. What is still unclear, however, is the importance of the accumulated influence of past MJO activity on these results. To examine the importance of past MJO phases in determining future states of extratropical circulations, a LBM and one of the simplest machine learning algorithm: logistic regression are used. By increasing the complexity of logistic regressions with additional informational about past MJO phases, we show that 15 additional lags before lag 0 play a dominant role in determining the future state of MJO teleconnections. This result is supported by the numerical LBM simulations. We further demonstrate that this 15-day span is characterized by a phase/lead time dependent feature, which is relevant to the dynamics of MJO teleconnections and explained in this work. Ultimately, a particular emphasis is placed on the role of model MJO in influencing the winter climatol- ogy of extratropical circulations. The MJO is known for consistently modulating the extratropical weather. In addition, simulating the MJO continues to be a challenge for many state-of-art climate models, and it is unclear the extent to which these biases in the MJO may cause biases in midlatitude variability. By analyzing 22 climate model simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and the reanalysis data, we demonstrate that one of leading variability of daily geopotential height is associated with MJO activity, and can be identified without prior knowledge of MJO in both observations and CMIP5 data. This shows the dominant role of MJO in modulating extratropical circulations. However, due to this strong relationship between MJO and extratropical circulations, the model biases in the MJO convection is also reflected in the wintertime climatology of extratropical circulations.
Open Access
Towards using neural networks for geoscientific discovery
(Colorado State University. Libraries, 2020) Toms, Benjamin A., author; Barnes, Elizabeth A., advisor; Ebert-Uphoff, Imme, committee member; Hurrell, James W., committee member; Thompson, David W. J., committee member
How can we use computational methods to extract physically meaningful patterns from geoscientific data? This question has been asked in some form for decades within the geoscientific community, with many landmark discoveries resulting from the novel application of computational methods to a geoscientific dataset. For example, the Madden-Julian Oscillation was discovered through Fourier transforms of tropical time-series, while the defining structures of the Northern Hemispheric annular modes were first captured using principal component analysis. These discoveries rooted in computational methods have since driven decades of geoscientific research and innovation, and are only two of among many similar examples. It is therefore clear that computational science and geoscience are inextricably intertwined, and so the continued advancement of both fields in tandem is beneficial to future geoscientific discovery. Many methods exist to discover patterns within geoscientific data, although each is limited by its own set of assumptions. The most common assumption is that of linearity, which oftentimes conflicts with our understanding that the earth system can be both dynamically and statistically nonlinear. However, a recently popularized subset of methods within the computer science community known as neural networks can identify nonlinear patterns and are therefore potentially powerful tools for geoscientific discovery. Neural networks learn how to map one dataset to another using a combination of nonlinear relationships, and are generalizable to a broad range of tasks including forecasting and identifying patterns within images. Regardless of the application, a common limitation of neural networks has been the difficulty to understand how and why they make their decisions. Therefore, while they have been used in geoscience for more than two decades, they have mostly been applied when accuracy is valued more than understanding, such as for making forecasts. Within this dissertation, we first propose a framework for how neural networks can be used for geoscientific discovery by applying recently invented methods from the computer science community. We focus on methods that explain which aspects of the input dataset are useful for the neural network when making connections to the output dataset. This framework enables physical interpretations of how and why neural networks make decisions, since the geoscientist that designs the neural network is likely familiar with the physical meaning of each input. In the first study of the dissertation, we outline the framework and apply it to two simple tasks to ensure the neural network interpretations abide by our current understanding of the earth system. The interpretable neural networks successfully identify the pattern of the El Niño Southern Oscillation and oceanic patterns that lend seasonal predictability, which lends confidence that the framework is reliable. In the second study, we then further test the methods by applying them to a more spatially and temporally complex oscillation called the Madden-Julian Oscillation (MJO). The interpretable neural networks correctly identify the known spatial structures and seasonality of the MJO, and also suggest that the MJO is nonlinear and expresses its nonlinearity through the uniqueness of each event. The final study assesses whether the proposed framework can be used to identify predictable patterns of earth-system variability within climate models through its application to decadal predictability. We find that the interpretable neural networks identify known modes of oceanic decadal variability that contribute to predictability of continental surface temperatures. The interpretations can also be used to identify distinct regimes of predictability, wherein spatially and temporally unique oceanic modes contribute predictability for the same location at different times. From a broader perspective, these studies suggest that neural networks are a viable tool for geoscientific discovery and are particularly useful given their ability to capture nonlinear, time-evolving patterns. It is likely that new neural network algorithms and methods for their interpretation will continue to be developed by the computer science community, and so this research provides a guideline for how such methods can be gainfully applied within the geosciences.
Open 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.

Browsing by Author "Barnes, Elizabeth A., advisor"

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