Here we analyze climate variability using instrumental, paleoclimate (proxy), and the latest climate model data to understand more about the sources and impacts of low-frequency climate variability. Understanding the drivers of climate variability at interannual to century timescales is important for studies of climate change, including analyses of detection and attribution of climate change impacts. Additionally, correctly modeling the sources and impacts of variability is key to the simulation of abrupt change (Alley et al., 2003) and extended drought (Seager et al., 2005; Pelletier and Turcotte, 1997; Ault et al., 2014). In Appendix A, we employ an Earth system model (GFDL-ESM2M) simulation to study the impacts of a weakening of the Atlantic meridional overturning circulation (AMOC) on the climate of the American Tropics. The AMOC drives some degree of local and global internal low-frequency climate variability (Manabe and Stouffer, 1995; Thornalley et al., 2009) and helps control the position of the tropical rainfall belt (Zhang and Delworth, 2005). We find that a major weakening of the AMOC can cause large-scale temperature, precipitation, and carbon storage changes in Central and South America. Our results suggest that possible future changes in AMOC strength alone will not be sufficient to drive a large-scale dieback of the Amazonian forest, but this key natural ecosystem is sensitive to dry-season length and timing of rainfall (Parsons et al., 2014). In Appendix B, we compare a paleoclimate record of precipitation variability in the Peruvian Amazon to climate model precipitation variability. The paleoclimate (Lake Limón) record indicates that precipitation variability in western Amazonia is ‘red’ (i.e., increasing variability with timescale). By contrast, most state-of-the-art climate models indicate precipitation variability in this region is nearly'‘white' (i.e., equally variability across timescales). This paleo-model disagreement in the overall structure of the variance spectrum has important consequences for the probability of multi-year drought. Our lake record suggests there is a significant background threat of multi-year, and even decade-length, drought in western Amazonia, whereas climate model simulations indicate most droughts likely last no longer than one to three years. These findings suggest climate models may underestimate the future risk of extended drought in this important region. In Appendix C, we expand our analysis of climate variability beyond South America. We use observations, well-constrained tropical paleoclimate, and Earth system model data to examine the overall shape of the climate spectrum across interannual to century frequencies. We find a general agreement among observations and models that temperature variability increases with timescale across most of the globe outside the tropics. However, as compared to paleoclimate records, climate models generate too little low-frequency variability in the tropics (e.g., Laepple and Huybers, 2014). When we compare the shape of the simulated climate spectrum to the spectrum of a simple autoregressive process, we find much of the modeled surface temperature variability in the tropics could be explained by ocean smoothing of weather noise. Importantly, modeled precipitation tends to be similar to white noise across much of the globe. By contrast, paleoclimate records of various types from around the globe indicate that both temperature and precipitation variability should experience much more low-frequency variability than a simple autoregressive or white-noise process. In summary, state-of-the-art climate models generate some degree of dynamically driven low-frequency climate variability, especially at high latitudes. However, the latest climate models, observations, and paleoclimate data provide us with drastically different pictures of the background climate system and its associated risks. This research has important consequences for improving how we simulate climate extremes as we enter a warmer (and often drier) world in the coming centuries; if climate models underestimate low-frequency variability, we will underestimate the risk of future abrupt change and extreme events, such as megadroughts.

The current cycle of climate change along with increases in hurricane activity, changing precipitation patterns, glacial melt, and other extremes of weather has led to interest and research into the global correlation or teleconnection between these events. Examination of historical climate records, proxies and observations is leading to formulation of hypotheses of climate dynamics with modeling and simulation being used to test these hypotheses as well as making projections. Ocean currents are believed to be an important factor in climate change with thermohaline circulation (THC) fluctuations being implicated in past cycles of abrupt change. Freshwater water discharge into high-latitude oceans attributed to changing precipitation patterns and glacial melt, particularly the North Atlantic, has also been associated with historical abrupt climate changes and is believed to have inhibited or shut down the THC overturning mechanism by diluting saline surface waters transported from the tropics. Here we analyze outputs of general circulation model (GCM) simulations parameterized by different levels of freshwater flux (no flux (control), 0.1 Sverdrup (Sv) and 1.0 Sv) with respect to tropical cyclone-like vortices (TCLVs) to determine any trend in simulated tropical storm frequency, duration, and location relative to flux level, as well as considering the applicability of using GCMs for tropical weather research. Increasing flux levels produced fewer storms and storm days, increased storm duration, a southerly and westerly shift (more pronounced for the 0.1 Sv level) in geographic distribution and increased activity near the African coast (more pronounced for the 1.0 Sv level). Storm intensities and tracks were not realistic compared to observational (real-life) values and is attributed to the GCM resolution not being fine enough to realistically simulate storm (microscale) dynamics.

Resilience and solar energy are separately growing in popularity for urban planners and similar professionals. This project links the two discrete terms together and examines the extent to which solar energy can improve local government resilience efforts. It includes a detailed literature review of both topics, as well as the methodology and findings related to a survey and interviews of local government officials and key stakeholders across the country related to hazard mitigation and energy assurance planning. This research finds that integrating the use of solar energy can improve local government resilience efforts related to mitigation, preparedness, response and recovery activities in the following ways: by being incorporated into hazard mitigation strategies as a means to maintain critical operations, thereby reducing loss of life and property; by being utilized in comprehensive planning efforts to increase capacity and decrease reliance and stress upon the grid, thereby reducing the likelihood of blackout events; by being used in tandem with backup storage systems as an integral part of energy assurance planning, which can help ensure critical functions continue in times of grid outage; by being used to provide power for response activities such as water purification, medicine storage and device charging; and by being used as an integral part of rebuilding communities in a more environmentally-conscious manner. The result of the research is a document entitled Solar Energy & Resilience Planning: a practical guide for local governments, a guidebook for local government officials wishing to have more information about incorporating solar energy into current resilience initiatives; it is included at the end of the report as Appendix C.

Upper-tropospheric (200 hPa) velocity potential is useful in identifying areas of rising or sinking atmospheric motions on varying temporal scales (e.g., weekly, seasonal, interannual) especially in the global tropics. These areas are associated with enhancement (rising motion) or suppression (sinking motion) of tropical convection and subsequent weather phenomena dependent on these processes (e.g., tropical cyclones). This study employed three commonly used global weather reanalysis datasets (NCEP/NCAR Reanalysis 1, JMA JRA-55, ECMWF ERA5) to calculate and compare upper-tropospheric velocity potential fields on varying temporal scales and quantify any differences that existed between them from 1958 to 2020 over four key regions of variability (Equatorial Africa, Amazon Basin, Equatorial Central Pacific, and Equatorial Indonesia). To supplement this analysis, the highly correlated variables to velocity potential of outgoing longwave radiation (OLR) and daily precipitation rate were used and directly compared with independent OLR and precipitation datasets to determine the reanalysis' level of agreement with the independent data. The ECMWF ERA5 held the highest agreement to these data over all regions examined and was reasoned to have the highest confidence in capturing the variability of upper-tropospheric velocity potential fields for the study period. Confidence was decreased in the usefulness of the NCEP/NCAR Reanalysis 1 as it consistently performed poorly over much of the study domain. The results of this study also emphasized the usefulness in ensemble-based approaches to assessing climate variability and understanding potential biases and uncertainties that are inherent in the data sources. / Master of Science / Historical weather data across the globe is analyzed using global weather reanalysis datasets which provide the most complete picture of how the atmosphere has evolved over the course of the last several decades. This data is a vital component in today's research investigating climate change and variability over time. This study examined how the history of upper-tropospheric velocity potential was captured in three commonly used global weather reanalysis datasets (NCEP/NCAR Reanalysis 1, JMA JRA-55, ECMWF ERA5) from 1958 to 2020 over four key regions of variability (Equatorial Africa, Amazon Basin, Equatorial Central Pacific, and Equatorial Indonesia). The variable of velocity potential is useful in identifying areas of rising or sinking atmospheric motions on varying time scales (e.g., weekly, seasonal, interannual) especially in the global tropics. These areas are associated with enhancement (rising motion) or suppression (sinking motion) of tropical convection (i.e., thunderstorms) and subsequent weather phenomena dependent on these processes (e.g., tropical cyclones). The analysis conducted found that the newest of the reanalysis datasets, the ECMWF ERA5, held the highest agreement to independent weather observations over all regions examined was reasoned to have the highest confidence in capturing the variability of upper-tropospheric velocity potential fields for the study period. Confidence was decreased in the usefulness of the NCEP/NCAR Reanalysis 1, the oldest of the reanalysis datasets, as it consistently performed poorly over much of the study domain. The results of this study also emphasized the usefulness in ensemble-based approaches to assessing climate variability and understanding potential biases and uncertainties that can be found in the data sources.

No description available.

According to the recently released National Climate Assessment (NCA), climate change will disproportionally impact the health of the most vulnerable communities in Central Texas (Melillo, 2014). Exactly how climate change will impact these populations is unclear (Measham, 2011; Martens, 2014). Nationwide, there are few examples of cities looking at the impacts of climate change on existing public health issues and vulnerable communities. The NCA, Austin/Travis County Community Health Assessment (CHA) and Community Health Improvement Plan (CHIP), broadly identifies vulnerable communities as children, the elderly, the sick, the poor, and some communities of color (Melillo, 2014: Luber, 2009). The 2014 release of the NCA, in addition to the 2013 completion of the CHA and CHIP, provides an opportunity to compare current public health issues with projected changes in climate. The deductive process starts with a review of the CHA and CHIP to identify issues that are directly impacted by hotter and longer heat waves including a lack of physical activity, a decrease in mobility, and greater social isolation. These issues are then compared to likely climate scenarios for Austin in the coming century. For Austin, climate scientists project longer and hotter heat waves and higher overnight average temperatures. The results of the process are a hypothetical framework and specific actions to incorporate increasing temperatures into short-term and long-term health improvement planning. Comparing the NCA and CHA/CHIP reveals that an increase in intensity and duration of heat waves will make it especially dangerous for vulnerable communities who already struggle with health issues sensitive to heat such as obesity, respiratory ailments, and social isolation (Martens, 2014). Further analysis finds that the health implications of climate change come down to three broad topics: outdoor physical activities, lack of access to healthcare facilities, and isolation. Austin’s increasing temperatures and growing population means that more resources and efforts are needed to ensure the safety of all Austin residents. In this thesis, I put forth a hypothetical decision-making framework that prioritizes the allocation of resources to advance Austin’s pathway to climate resiliency. In addition, tools and actions are proposed to increase the climate resilience of the most vulnerable community members in Austin. / text

The purpose of this report was to investigate how municipals in the county of Västra Götaland work with climate adaptation of storm water management and to identify problems that occur in the work. As with the rest of the world, Sweden will be affected by expected climate changes and it is therefore important that Swedish municipalities work with climate adaptation and to help them make the work manageable. The method used was semi-structural qualitative interviews where 13 municipalities were interviewed in April 2016. The result of the interviews shows that there is an ambition to work with climate adaptation of storm water management but that there are different problems associated with the work that have made it difficult to start. 69% of the municipalities include climate adaptation to some extent when they work with master plans and 5 out of 8 municipalities are or will be including climate adaptation strategies in their storm water management document. A majority of the municipalities don’t prioritize climate adaptation and could be doing more to include climate adaptation in their work. The most common problems reported by the municipalities were too little resources/lack of finance, undecided responsibility and not clear enough laws regarding the subject.

Understanding of climate change and policy responses thereto rely on accurate measurements as well as models of both socio-economic and physical processes. However, data to assess impacts and establish historical climate records are non-stationary: distributions shift over time due to shocks, measurement changes, and stochastic trends - all of which invalidate standard statistical inference. This thesis establishes econometric methods to model non-stationary climate data consistent with known physical laws, enabling joint estimation and testing, develops techniques for the automatic detection of structural breaks, and evaluates socio-economic scenarios used in long-run climate projections. Econometric cointegration analysis can be used to overcome inferential difficulties stemming from stochastic trends in time series, however, cointegration has been criticised in climate research for lacking a physical justification for its use. I show that physical two-component energy balance models of global mean climate can be mapped to a cointegrated system, making them directly testable, and thereby provide a physical justification for econometric methods in climate research. Automatic model selection with more variables than observations is introduced in modelling concentrations of atmospheric CO₂, while controlling for outliers and breaks at any point in the sample using impulse indicator saturation. Without imposing the inclusion of variables a-priori, model selection results find that vegetation, temperature and other natural factors alone cannot explain the trend or the variation in CO₂ growth. Industrial production components, driven by business cycles and economic shocks, are highly significant contributors. Generalizing the principle of indicator saturation, I present a methodology to detect structural breaks at any point in a time series using designed functions. Selecting over these break functions at every point in time using a general-to-specific algorithm, yields unbiased estimates of the break date and magnitude. Analytical derivations for the split-sample approach are provided under the null of no breaks and the alternative of one or more breaks. The methodology is demonstrated by detecting volcanic eruptions in a time series of Northern Hemisphere mean temperature derived from a coupled climate simulation spanning close to 1200 years. All climate models require socio-economic projections to make statements about future climate change. The large span of projected temperature changes then originates predominantly from the wide range of scenarios, rather than uncertainty in climate models themselves. For the first time, observations over two decades are available against which the first sets of socio-economic scenarios used in the Intergovernmental Panel on Climate Change reports can be assessed. The results show that the growth rate in fossil fuel CO₂ emission intensity (fossil fuel CO2 emissions per GDP) over the 2000s exceeds all main scenario values, with the discrepancy being driven by underprediction of high growth rates in Asia. This underestimation of emission intensity raises concerns about achieving a world of economic prosperity in an environmentally sustainable fashion.

Thesis advisor: Jeremy Shakun / As recently measured amounts of global atmospheric carbon dioxide concentrations have risen 40% from pre-Industrial levels and will likely reach double by mid-century, climate scientists have expressed concern over the future state of the climate system, and have attempted to gauge the consequences of such a large forcing. The principal parameter for climate scientists is equilibrium climate sensitivity, which is the change in temperature following a doubling of atmospheric CO2 concentrations. Current estimates of climate sensitivity span too expansive of a range to provide a clear understanding of the magnitude of temperature changes one can expect. Therefore, I conduct many individual multivariate analyses as a means of narrowing these ranges of sensitivity and to investigate geographical distributions of sensitivity, at the very least. To do so, I analyze four major climate forcings: greenhouse gas, atmospheric dust, ice volume, and insolation. Using several multiple linear regressions, I calculate the relative weighting of each forcing in driving the temperature signal in 47 local temperature proxy records. The paleoclimate proxy records chosen span glacial cycles over the past 800 kyr. These results provide insight into the geographical distributions of the relative influences of each of the forcings, while working to constrain the range of sensitivity estimates through the weighting of the greenhouse gas forcing. Separating out the individual climate inputs allows me to conclude what percentage of climate change was caused by CO2 in the past, and by implication how much warming might be expected due to GHG forcing in the future. / Thesis (BS) — Boston College, 2015. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Earth and Environmental Sciences.

This document is a summary of research conducted to develop and apply climate analysis tools toward a better understanding of the past and future of hydroclimate variability in the state of Utah. Two pilot studies developed data management and climate analysis tools subsequently applied to our region of interest. The first investigated the role of natural atmospheric forcing in the inter-annual variability of precipitation of the Sahel region in Africa, and found a previously undocumented link with the East Atlantic mode, which explains 29% of variance in regional precipitation. An analysis of output from an operational seasonal climate forecast model revealed a failure in the model to reproduce this linkage, thus highlighting a shortcoming in model performance. The second pilot study studied long-term trends in the strength of the Great Plains low-level jet, an driver of storm development in the region’s wet spring season. Our analysis showed that since 1979 the low-level jet has strengthened as shifted the timing of peak activity, resulting in shifts both in time and location for peak precipitation, possibly the result of anthropogenic forcing. Our third study used a unique tree-ring dataset to create a reconstruction of April 1 snow water equivalent, an important measure of water supply in the Intermountain West, for the state of Utah to 1850. Analysis of the reconstruction shows the majority of snowpack variability occurs monotonically over the whole state at decadal to multidecadal frequencies. The final study evaluated decadal prediction performance of climate models participating in the Coupled Model Intercomparison Project 5. We found that the analyzed models exhibit modest skill in prediction of the Pacific Decadal Oscillation and better skill in prediction of global temperature trends post 1960.