Weather – cold, wet, hot and windy – pervades life, material and social. So present and obvious as to provide a challenge for research, material though ephemeral too, weather breaks boundaries and refuses categorisation. While night becomes day, the cold season warms up over weeks and annual patterns are changing on a scale of years, practices in the face of weather transitions are themselves shifting. Based on ten months of fieldwork in the small village of Gau in the Pahari Indian Himalayas this thesis interrogates the saliencies and permeations of weather in people’s lives. It investigates how people intra-act (Barad 2007) with the weather, though practices, infrastructures and relationships with others. My approach argues for the validity of weather as a means by which to learn about socio-material lives. Pahari villagers live and act within the weather that moves around them. They are subject to, but also modify, their thermal environment. Through housing, clothing and tools such as the fire and the fan they affect the impact of the weather as it meets their bodies, but also daily patterns of movement are coloured by weather considerations. This work views weather in relation to health practices (such as refraining from working during the rain so as not to fall ill), for care of others (such as domestic bovines), for house-building and hospitable relationship-building among neighbours, for negotiation of landslide-fraught access roads to elsewhere and for understandings of pollution in the air. This focus on weather is intended to connect dots for people working on climate change, both within and beyond anthropology, and to contribute to discussions in areas including human-animal relations, health and illness and housing.

Walkability indices are currently used for a wide range of research and commercial applications. Few studies have examined the relationship between walkability indices and measured pedestrian volume or walking rates, nor explored moderators of pedestrian volume such as weather. With 14 years of traffic study data from the City of Ottawa, a spatial auto-regressive (SAR) multi-level model (MLM) was used to understand the proportion of variance in walking explained by the commercial Walk Score® index and selected weather variables. Modeling revealed that a significant proportion of pedestrian volume at a given location in Ottawa, including its spatial lag, was explained by the corresponding Walk Score® value and its spatial lag (51.45%). Furthermore, weather expressed as a combination of ‘felt’ temperature, presence or absence of precipitation, and percent cloud cover, accounted for 2.79% of the variance in walking. These findings indicate that walkability indices may provide value as cost-effective engineering and urban planning tools.

This paper studies the effects of peer pressure in relation to football audiences’ possible impact on how the referees judge. The data used is new data from the highest Swedish football league, Allsvenskan, for the seasons 2016-2018. The paper treats the problem with the size of the audience not being random by using weather as an instrument. The OLS-estimations suggest that the referee is affected by the audience in the sense that more spectators increase referee stringency. The IV-estimations are precise and close to zero suggesting that it is important to account for omitted variables when studying the effect of peer pressure on referees.

The historical underinvestment in power infrastructure and the poor performance of power delivery has resulted in extensive and regular power shortages in Nigeria. As Nigeria aims to bridge its power supply gap, the recent deregulation of its electricity market has seen the privatisation of its generation and distribution companies. Ambitious plans have also been put in place to expand the transmission network and the total power generation capacity. However, these plans have been developed with essentially arbitrary estimates for prevailing demand levels as the network and generation limits mean actual demand cannot be measured directly due to a programme of almost constant load shedding; the managed and intermittent distribution of inadequate energy allocation from the system operator. Network expansion planning and system reliability analysis require time series demand data to assess generation adequacy and to evaluate the impact of daily and seasonal influences on the energy supply-demand balance. To facilitate such analysis this thesis describes efforts to develop a credible time series electricity demand model for Nigeria. The focus of the approach has been to develop a fundamental bottom-up model of individual households accounting for a range of dwelling characteristics, socioeconomic factors, appliance use and household activities. A householder survey was conducted to provide essential inputs to allow a portfolio of household demand models which can account for weather-dependence and other factors. A range of national and regional socioeconomic and weather datasets have been employed to create a regionally disaggregated time series demand model. The generated demand estimates are validated against metered data obtained from Nigeria. The value of the approach is highlighted by using the model to investigate the potential for future load growth as well as analyse the impact of renewable energy generation on the Nigerian grid.

Our ability to predict precipitation on climate time-scales (months–decades) is limited by our ability to separate signals in the climate system (cyclical and secular) from noise — that is, variability due to processes that are inherently unpredictable on climate time-scales. This dissertation describes methods for characterizing “weather” noise — variability that arises from daily-scale processes — as well as the potential predictability of precipitation on climate time-scales. In each method, we make use of a climate-stationary null model for precipitation and determine which characteristics of the true, non-stationary system cannot be captured by a stationary assumption. This un-captured climate variability is potentially predictable, meaning that it is due to climate time-scale processes, although those processes themselves may not be entirely predictable, either practically or theoretically. The three primary methods proposed in this dissertation are 1. A stochastic framework for modeling precipitation occurrence with proper daily-scale memory representation, using variable order Markov chains and information criteria for order selection. 2. A corresponding method for representing precipitation intensity, allowing for memory in intensity processes. 3. A semi-parametric stochastic framework for precipitation which represents intensity and occurrence without separating the processes, designed to handle the issues that arise from estimating likelihoods for zero-inflated processes. Using each of these methods, potential predictability is determined across the contiguous 48 United States. Additionally, the methods of Chapter 4 are used to determine the magnitude of weather and climate variability for the “historical runs” of five global climate models for comparison against observational data. It is found that while some areas of the contiguous 48 United States are potentially very predictable (up to ∼ 70% of interannual variability), many regions are so dominated by weather noise that climate signals are effectively masked. Broadly, perhaps 20–30% of interannual variability may be potentially predictable, but this ranges considerably with geography and the annual seasonal cycle, yielding “hot spots” and “cold spots” of potential predictability. The analyzed global climate models demonstrate a fairly robust representation of weather-scale processes, and properly represent the ratio of weather-to- climate induced variability, despite some regional errors in mean precipitation totals and corresponding variability.

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Atmospheric particles represent a component of air pollution that has been identified as a major contributor to adverse health effects and mortality. Aerosols also interact with solar radiation and clouds perturbing the atmosphere and generating responses in a wide range of scales, such as changes to severe weather and climate. Thus, being able to accurately predict aerosols and its effects on atmospheric properties is of upmost importance. This thesis presents a collection of studies with the global objective to advance in science and operations the use of WRF-Chem, a regional model able to provide weather and atmospheric chemistry predictions and simultaneously representing aerosol effects on climate. Different strategies are used to obtain accurate predictions, including finding an adequate model configuration for each application (e.g., grid resolution, parameterizations choices, processes modeled), using accurate forcing elements (e.g., weather and chemical boundary conditions, emissions), and developing and applying data assimilation techniques for different observational sources. Several environments and scales are simulated, including complex terrain at a city scale, meso-scale over the southeast US for severe weather applications, and regional simulations over the three subtropical persistent stratocumulus decks (off shore California and southeast Pacific and Atlantic) and over North America. Model performance is evaluated against a large spectrum of observations, including field experiments and ground based and satellite measurements. Overall, very positive results were obtained with the WRF-Chem system once it had been configured properly and the inputs chosen. Also, data assimilation of aerosol and cloud satellite observations contributed to improve model performance even further. The model is proven to be an excellent tool for forecasting applications, both for local and long range transported pollution. Also, advances are made to better understand aerosol effects on climate and its uncertainties. Aerosols are found to generate important perturbations, ranging from changes in cloud properties over extensive regions, up to playing a role in increasing the likelihood of tornado occurrence and intensity. Future directions are outline to keep advancing in better predictions of aerosols and its feedbacks.

Among natural disasters affecting the United States, North Atlantic tropical cyclones (TCs) and hurricanes are responsible for the highest economic losses and are one of the main causes of fatalities. Although we cannot prevent these storms from occurring, skillful seasonal predictions of the North Atlantic TC activity and associated impacts can provide basic information critical to our improved preparedness. Unfortunately, it is not yet possible to predict heavy rainfall and flooding associated with these storms several months in advance, and the lead time is limited to few days at the most. On the other hand, overall North Atlantic TC activity can be potentially predicted with a six- to nine-month lead time. This thesis focuses on the evaluation of the skill in predicting basin-wide North Atlantic TC activity with a long lead time and rainfall with a short lead time. For the seasonal forecast of TC activity, we develop statistical-dynamical forecasting systems for different quantities related to the frequency and intensity of North Atlantic TCs using only tropical Atlantic and tropical mean sea surface temperatures (SSTs) as covariates. Our results show that skillful predictions of North Atlantic TC activity are possible starting from November for a TC season that peaks in the August-October months. The short term forecasting of rainfall associated with TC activity is based on five numerical weather prediction (NWP) models. Our analyses focused on 15 North Atlantic TCs that made landfall along the U.S. coast over the period of 2007-2012. The skill of the NWP models is quantified by visual examination of the distribution of the errors for the different lead-times, and numerical examination of the first three moments of the error distribution. Based on our results, we conclude that the NWP models can provide skillful forecasts of TC rainfall with lead times up to 48 hours, without a consistently best or worst NWP model.

Agricultural adaptation to climate change is notoriously context specific. Recently updated projections for the Northeastern US forecast increasingly severe and erratic precipitation events which pose significant risks to every sector of agricultural production in the region. Vegetable and berry farmers are among the most vulnerable to the risks of severe precipitation and drought due to the intensive soil and crop management strategies which characterize of this kind of production. To successfully adapt to a changing climate, these farmers need information which is tailored for the unique challenges of vegetable and berry production, framed at the level of climate impacts, and delivered through the familiar lexicon used by farmers in the region. My approach is grounded by partnerships with farmer networks to inform both the relevance of this information and my outreach strategy for sharing results. This research presents complimentary quantitative and qualitative data sets on adaptive management, and highlights the insight of farmers voices on innovative and promising solutions for managing climate related risks. The goal of the project was to create usable information for producers through a Farmer First approach which privileges the voices and experiences of farmers in determining the information and resources they need. As part of a broader project, this thesis analyzed the results of a regional survey of vegetable and berry growers conducted over the winter months of 2017-2018. The first chapter reviews theoretical foundations for academic study of agricultural management and climate change, with a focus on information usability. The second chapter applies theories of adaptation and resilience to identify agroecological principles for adapting farm management to water extremes and innovative practices emerging in the region. The third chapter uses a regression modelling approach to explore how adaptive management practices vary across site specific characteristics. Our analysis identifies trends and principles for adapting to water excess and water deficits on diversified vegetable and berry farms in the Northeast. The research findings highlight how site characteristics influence the selection of adaptive management practices on farms in the Northeast.

Global Climate Change has empirical evidence to support the idea that CO2 levels may be affecting weather and health, including rates of infectious diseases. The Midwest region of the United States of America has had the highest increase in giardiasis rates in recent years, and Missouri was chosen for this study as a representative state in the Midwest. There is no definitive answer as to why the rates of giardiasis have changed from 2003 - 2013. The Theory of Climate Change was used as the theoretical framework for this study. The purpose of this research was to determine whether temperature, precipitation and CO2 levels are associated with giardiasis. A cross-sectional design was used for this study with a non-probability sample of reported cases of giardiasis for 2003 - 2013, and data were analyzed using a bivariate analysis and multivariate analysis. There was a negative association between precipitation and number of cases of giardiasis in Missouri residents (p < .05), a positive association between temperature and number of cases of giardiasis in Missouri residents (p < .05), and a positive association between CO2 levels and number of cases of giardiasis in Missouri residents (p < .05). Levels of CO2 modified the association between precipitation and number of cases of giardiasis in Missouri residents (p < .05). Levels of CO2 modified the association between temperature and number of cases of giardiasis in Missouri residents (p < .05). These results demonstrate that climatic factors impact public health significantly. The implications for social change are to have the waterways, wells, and public water tested more often, to reinforce the waterway closures with increased measures to prevent morbidity and mortality with giardiasis when possible, and to raise awareness of the climatic impact on health.