Large explosive volcanic eruptions inject SO2 into the stratosphere where it is oxidised to sulphate aerosols which reflect sunlight. This causes a reduction in global temperature and precipitation lasting a few years. Here the robust features of this precipitation response are investigated, using superposed epoch analysis that combines results from multiple eruptions. The precipitation response is first analysed using the climate model HadCM3 compared to two gauge based land precipitation datasets. The analysis is then extended to a large suite of state-of-the art climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). This is the first multi-model study focusing on the precipitation response to volcanoes. The large ensemble allows analysis of a short satellite based dataset which includes ocean coverage. Finally the response of major world rivers to eruptions is examined using historical records. Whilst previous studies focus on the response of just a few rivers or global discharge to single eruptions, here the response of 50 major world rivers is averaged across multiple eruptions. Results are applicable in predicting the precipitation response to future eruptions and to geoengineering schemes that seek to counteract global warming through reducing incoming solar radiation. The main model-simulated features of the precipitation response include a significant global drying over both land and ocean, which is dominated by the wet tropical regions, whilst the dry tropical ocean regions get significantly wetter following eruptions. Monsoon rainfall decreases, whilst in response to individual eruptions the Intertropical Convergence Zone shifts away from the hemisphere with the greater concentration of volcanic aerosols. The ocean precipitation response is longer lived than that over land and correlates with near surface air temperature, whilst the land response correlates with aerosol optical depth and a reduction in land-ocean temperature gradient Many of these modelled features are also seen in observational data, including the decrease in global mean and wet tropical regions precipitation over land and the increase of precipitation over dry tropical ocean regions, all of which are significant in the boreal cold season. The land precipitation response features were robust to choice of dataset. Removing the influence of the El Nino Southern Oscillation (ENSO) reduces the magnitude of the volcanic response, as several recent eruptions coincided with El Nino events. However, results generally remain significant after subtraction of ENSO, at least in the cold season. Over ocean, observed results only match model expectations in the cold season, whilst data are noisy in the warm season. Results are too noisy in both seasons to confirm whether a long ocean precipitation response occurs. Spatial patterns of precipitation response agree well between observational datasets, including a decrease in precipitation over most monsoon regions. A positive North Atlantic Oscillation-like precipitation response can be seen in all datasets in boreal winter, but this is not captured by the models. A detection analysis is performed that builds on previous detection studies by focusing specifically on the influence of volcanoes. The influence of volcanism on precipitation is detectable using all three observational datasets in boreal winter, including for the first time in a dataset with ocean coverage, and marginally detectable in summer. However, the models underestimate the size of the winter response, with the discrepancy originating in the wet tropics. Finally, the number of major rivers that undergo a significant change in discharge following eruptions is slightly higher than expected by chance, including decreased flow in the Amazon, Congo, Nile, Orange, Ob and Yenisey. This proportion increases when only large or less humanly influenced basins are considered. Results are clearer when neighbouring basins are combined that undergo the same sign of CMIP5 simulated precipitation response. In this way a significant reduction in flow is detected for northern South American, central African and less robustly for high-latitude Asian rivers, along with a significant increase for southern South American and SW North American rivers, as expected from the model simulated precipitation response.

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第19168号 / 理博第4108号 / 新制||理||1591(附属図書館) / 32160 / 京都大学大学院理学研究科地球惑星科学専攻 / (主査)准教授 重 尚一, 准教授 林 泰一, 教授 余田 成男 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / A large-scale phenomenon that greatly affects the weather and the climate of different locations of the Globe and that has been widely studied during the last three decades is the El Niño Southern Oscilation (ENSO). ENSO leads to changes in the atmospheric circulation and affects climate variables, mainly rainfall, in all regions of Brazil. During El Niño years (the positive phase of ENSO), rainfall is often above normal in Southern Brazil whereas during La Niña years (the negative phase of ENSO), rainfall is often below normal. Some of the rainfall properties are affected by ENSO like frequency, intensity and amount. These properties are important to define if rainfall events are erosive. There are some methods of determining rainfall erosivity, among them the best and most used in Rio Grande do Sul State is the EI30 erosivity index. The objective of this dissertation was to determine and associate the EI30 erosivity index of rainfalls with the ENSO phenomenon in Santa Maria, RS, Brazil. Daily precipitation data recorded from 01 July 1978 to 30 July 2008 collected in a standard Climatological Station in Santa Maria, RS, were used. Individual erosive rainfall events were identified in the records, grouped into El Niño, La Niña and Neutral years, and had their EI30 calculated. Rainfalls were also grouped into three patterns: Advanced, Intermediate and Delayed. Pearson correlation and regression analyses between the EI30 erosivity index and the Niño Oceanic Index (NOI) were performed and the regression was tested with the t test in order to quantify the relationship between the two variables, aiming a possible predictability of the erosion potential of rainfall events from Sea Surface Temperature (SST) in the Pacific Ocean. Rainfall erosion potential in Santa Maria is affected by ENSO, so that a greater number of rainfall events have higher erosion potential during El Niño and neutral years. The variability of the rainfall erosion potential is greater during Neutral years than during years with SST anomalies. The frequency distribution of the EI30 erosivity index is skewed to the right in El Niño, La Niña and Neutral years. Rainfalls pattern is changed in years with SST anomalies, so that there is a slight increase in Advanced rainfalls during El Niño years and a slight decrease in Delayed rainfalls during La Niña years, whereas there is a decrease in rainfall events with Intermediate pattern during El Niño and La Niña years compared to Neutral years. The predictive capability of the erosion potential of rainfall events in Santa Maria from NOI index is weak or not possible. / Um dos fenômenos de grande escala que mais afetam o tempo e o clima de diferentes locais na superfície terrestre e que tem sido bastante estudado nas últimas três décadas é o fenômeno El Niño Oscilação Sul (ENOS). O ENOS provoca alterações na circulação atmosférica que afetam os elementos meteorológicos, principalmente a precipitação pluviométrica, em todas as regiões do Brasil. Em anos de El Niño, fase positiva do fenômeno, a precipitação pluviométrica é freqüentemente acima da normal na Região Sul do Brasil e em anos de La Niña, fase negativa do fenômeno, a precipitação pluviométrica é freqüentemente abaixo da normal. Algumas características das precipitações pluviométricas são afetadas pelo ENOS como a freqüência, a intensidade e a quantidade. Essas características são importantes para se definir se as chuvas são erosivas. Existem alguns métodos de determinação da erosividade das chuvas, através de índices de erosividade, entre os quais, o melhor e mais usado no Rio Grande do Sul (RS) é o Índice de Erosividade EI30. O objetivo desta dissertação foi determinar e associar o Índice de Erosividade EI30 das chuvas com o fenômeno ENOS para Santa Maria, RS. Usaram-se os dados de precipitação diária retirados de pluviogramas a partir de 01 de julho de 1978 a 30 de junho de 2008 coletados na Estação Climatológica Principal de Santa Maria, RS. As chuvas individuais e erosivas foram identificadas nos pluviogramas, classificadas em anos de El Nino, La Niña e Neutros e calculado o seu índice EI30. Também foram classificadas as chuvas em padrões de chuvas Avançado, Intermediário e Atrasado. Foi realizada a análise de correlação de Pearson e análise de regressão entre o Índice de Erosividade EI30 e o Índice Oceânico do Niño (ION) e a significância da regressão foi testada com o teste t com o objetivo de quantificar a associação entre as duas variáveis, com vistas à possível previsibilidade do potencial erosivo das chuvas a partir de anomalias de Temperatura da Superfície do Mar (TSM) no Oceano Pacífico. O potencial erosivo das chuvas em Santa Maria é afetado pelo fenômeno ENOS, de modo que um maior número de chuvas tem maior potencial erosivo em anos de El Niño e em anos Neutros. A variabilidade do potencial erosivo das chuvas em Santa Maria é maior nos anos Neutros do que nos anos de anomalia da TSM. A distribuição de freqüência do índice de erosividade EI30 de chuvas individuais é assimétrica positiva em anos de El Niño, La Niña e Neutros. O padrão das chuvas é alterado em anos de anomalia da TSM no sentido de que nos anos El Niño há um leve acréscimo nas chuvas de padrão avançado e em anos de La Niña há um leve acréscimo nas chuvas de padrão atrasado, e no padrão intermediário, decrescem o número de chuvas em anos de El Niño e La Niña comparado com anos Neutros. A capacidade preditiva do potencial erosivo das chuvas em Santa Maria pelo Índice ION é fraca ou inexistente.