This study examines the spatial and temporal characteristics of the surface air temperature in Portland, Oregon. Spatial temperature patterns indicate that the dominant control factors on seasonal temperature distribution are local topography, elevation, and urban-rural differences in surface structure. A heat island exists in the Portland area; the intensity of the heat island rang€s from 4° to 10° F, and varies throughout the year. The strongest heat island is found in the July minimum temperature. Temperature distribution in Portland and the adjacent area is affected by winds and rainy conditions, but less influenced under overcast skies. The long-term temperature over the last century shows that Portland's mean annual temperature trends are 0.057° F/yr and 0.052° F/yr in the two warming periods 1900-1940 and 1961-1984, respectively, and these warming trends are largely due to warming in spring and early summer as well as in winter months except January. Comparisons between Portland and other local non-urban climatic stations show a general warming trend in Portland since the end of the last century, which is 0.028° F/yr in the mean annual temperature, and 0.017° F/yr in maximum temperature after the regional trends are removed. Monthly mean temperature in July and January demonstrate a warming by 0.023° F/yr and 0.015° F/yr at Portland, respectively. All these warming trends are due mainly to the impact of urbanization. It is found that the cooling effect on the northern Willamette Valley due to the presence of the Columbia Gorge is most noticeable in the daytime and in January.

Local climate action plans have become more prevalent in recent years yet information on their success is limited. While unlikely, on their own, to be able to mitigate enough carbon emissions to prevent catastrophic impacts of global temperature increase, local climate planning has the potential to play an important role in a number of key ways. Cities have traditionally exercised control in areas that have GHG abatement potential at low cost (e.g. building codes, land use, energy procurement) and the total population represented by cities committed to GHG reduction efforts is not insignificant and continues to grow. The extent to which local climate plans can serve as a meaningful element in a larger (but currently woefully inadequate) policy picture, will depend on their ability to set aggressive goals, dedicate resources, test innovative strategies, and measure progress systematically. Looking at the plans and progress reports of five U.S. cities, many have set aggressive goals and created innovative programs that could be replicated at other levels of government, but most are somewhat lacking in measuring and reporting progress metrics and financial resources committed to these efforts. For local climate planning to contribute significantly to broader climate policy, it will need to develop more rigorous progress metrics so the highest yield, lowest cost abatement strategies can be identified and advanced in other cities and at higher levels of government. / text

This item was digitized as part of the Million Books Project led by Carnegie Mellon University and supported by grants from the National Science Foundation (NSF). Cornell University coordinated the participation of land-grant and agricultural libraries in providing historical agricultural information for the digitization project; the University of Arizona Libraries, the College of Agriculture and Life Sciences, and the Office of Arid Lands Studies collaborated in the selection and provision of material for the digitization project.

The climate of the eastern section of the Sahelian latitude, especially over the Eritrean subdomain, is often associated with long drought episodes from which the atmospheric mechanisms are poorly understood. In an effort to improve our knowledge of weather and climate systems over this region, the PRECIS Regional Climate Model (RCM) from the United Kingdom (UK) was obtained and implemented. Such a climate model that is based upon the physical laws of nature has the ability to simulate regional-scale atmospheric patterns, and therefore, may significantly contribute to our understanding of local atmospheric processes. In this dissertation the assessment of past regional climate trends from both observations and model simulations, and the simulation of scenarios for possible future climate change were regarded as important. To investigate this, the PRECIS RCM was first nested over the Eritrean domain into the “atmosphere only” HadAM3H global General Circulation Model (GCM) and forced at its lateral boundaries by a 30-year present-day (1961-1990) integration of the same global model. Secondly, the PRECIS RCM was constrained at its lateral boundary by the “fully coupled” HadCM3 GCM (for Sea Surface Temperatures (SSTs) and sea-ice) and its improved atmospheric component (HadAM3H GCM). The latter simulations provided boundary conditions for the A2 and B2 future emission scenarios (Special Report on Emission Scenarios (SRES)) to simulate a 20-year (2070-2090) projection of future climate. These experiments allowed for verification of both spatial and temporal present-day climate simulations, as well as possible future climate trends as simulated by the PRECIS RCM over the Eritrean domain, with specific emphasis on temperature and moisture related variables. The study indicates that PRECIS RCM climate simulations are mostly in harmony with observed spatial patterns. This skill may be attributed to the full representation of the climatic system (land surface, sea, ice, atmosphere and atmospheric chemistry such as sulphur and greenhouse gasses) in the model configuration. However, when comparing PRECIS RCM results with the much coarser resolution (2.5ox2.5o) National Centre for Environmental Prediction (NCEP) reanalysis data, obvious differences do occur. These differences are not necessarily the result of poor model performance, but may be attributed to more detailed simulations over the finer RCM grid (0.44o x 0.44o). Future climate scenario simulation with the PRECIS RCM over Eritrea produce increased surface temperature in both the A2 and B2 SRES scenario integrations, relative to the present climatology. This temperature increase also appears in the driving GCM (HadCM3) as well as in other GCM results from the Inter Governmental Panel for Climate Change (IPCC) initiative. There are, however, mixed signals in rainfall projections. According to PRECIS RCM results, rainfall is expected to increase in most of the Eritrean region. Copyright 2005, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. Please cite as follows: Beraki, A F 2005, Climate change scenario simulations over Eritrea by using a fine resolution limited area climate model : temperature and moisture sensitivity , MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://upetd.up.ac.za/thesis/available/etd-02102006-152327 / > / Dissertation (MSc)--University of Pretoria, 2007. / Geography, Geoinformatics and Meteorology / Unrestricted

Previous research indicates a distinct seasonal pattern in mortality rates. Increases are prominent during the northern-hemispheric cold season. These patterns are seen in overall mortality, diabetes mellitus, circulatory, digestive, and respiratory diseases. A principal component analysis indicates that departure from normal temperature, minimum, maximum, and average daily temperature, and dew-point temperature are the primary atmospheric variables that influence mortality patterns. ANOVA and Kruskal-Wallis tests support findings of principal component analysis. Although a day-to-day relationship between mortality rates and atmospheric variables was noted in several instances, results suggest that the influence of the primary atmospheric variables on mortality rates is greatest when a three to five-day lag time is in place. Furthermore, results indicate that the combination of these variables in conjunction with frontal passage is linked to seasonal increases in mortality. A combination of atmospheric variables that influence mortality rates has been identified, however, their exact influence is still unclear.

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.

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