In addition to causing an increase in mean temperatures, climate change is also altering precipitation regimes across the globe. General circulation models project both latitude-dependent changes in precipitation mean and increases in precipitation variability. These changes in water availability will impact terrestrial primary productivity, the fixation of carbon dioxide into organic matter by plants. In my thesis, I addressed the following three questions: 1.) What will be the relative effect of changes in the mean and standard deviation of annual precipitation on mean annual primary production? 2.) Which ecosystems will be the most sensitive to changes in precipitation? 3.) Will increases in production variability be disproportionately greater than increases in precipitation variability? I gathered 58 time series of annual precipitation and aboveground net primary production (ANPP) from long-term ecological study sites across the globe. I quantified the sensitivity of ANPP at each site to changes in precipitation mean and variance. My results indicated that mean ANPP is about 40 times more sensitive to changes in precipitation mean than to changes in precipitation variance. I showed that semi-arid ecosystems such as shortgrass steppe in Colorado or typical steppe in Inner Mongolia may be the most sensitive to changes in precipitation mean. At these sites and several others, a 1% change in mean precipitation may result in a change in ANPP that is greater than 1%. To address how increases in interannual precipitation variability will impact the variability of ANPP, I perturbed the variability of observed precipitation time series and evaluated the impact of this perturbation on predicted ANPP variability. I found that different assumptions about the precipitation-ANPP relationship had different implications for how increases in precipitation variability will impact ANPP variability. Increases in ANPP variability were always directly proportional to increases in precipitation variability when ANPP was modeled as a simple linear or a lagged function of precipitation. However, when ANPP was modeled as a nonlinear, saturating function of precipitation, increases in ANPP variability were disproportionately low compared to increases in precipitation variability during wet years but disproportionately high during dry years. My thesis addresses an existing research gap regarding the long-term impact of increases in interannual precipitation variability on key ecosystem functioning. I showed that increases in precipitation variability will have negligible impacts on ANPP mean and have disproportionately large impacts on ANPP variability only when ANPP is a concave down, nonlinear function of precipitation. My work also demonstrates the importance of the precipitation-ANPP relationship in determining the magnitude of impacts to ANPP caused by changes in precipitation. Finally, my thesis highlights the potential for considerable changes in ANPP variability due to increases in precipitation variability.

The semiarid region between the North African Savanna and Sahara Desert, known as the Sahel, experienced dramatic multidecadal precipitation variability in the 20th century that was unparalleled in the rest of the world, including devastating droughts and famine in the early 1970s and 80s. Accurate predictions of this region’s hydroclimate future are essential to avoid future disasters of this kind, yet simulations from state of the art general circulation models (GCMs) do a poor job of simulating past Sahel rainfall variability, and don’t even agree on whether future precipitation will increase or decrease under global warming. Furthermore, climate scientists are still not in agreement about whether anthropogenic emissions played an important role relative to natural variability in dictating past Sahel rainfall change. Because the climate system is complex and coupled, it is difficult to determine which processes should be considered causal drivers of circulation changes and which should be considered part of the climate response, and therefore many theories for monsoon rainfall variability coexist in the literature. It is difficult to evaluate these competing theories because observational studies generally cannot be interpreted causally, but simulated experiments may not represent the dynamics of the real world. The Coupled Model Intercomparison Project (CMIP) provides a wealth of data in which GCMs maintained at research institutions worldwide perform similar experiments, allowing the researcher to reach conclusions that are robust to differences in parameterization between GCMs. The scientific community has been using a wide range of statistical techniques to analyze this data, and each has notable limitations. This dissertation explores two statistical techniques for leveraging CMIP to explore the drivers and mechanisms of historical Sahel rainfall variability: analysis of ensemble-mean responses to prescribed variables, and causal inference. In ‎Chapter 1, we give an overview of the climatology and variability of Sahel rainfall and present relevant physical theory. In ‎Chapter 2, we examine the roles of various types of anthropogenic forcing in observations and coupled simulations, using a 3-tiered multi-model mean (MMM) to extract robust climate signals from CMIP phase 5 (CMIP5). We examine “20th century” historical and single-forcing simulations—which separate the influence of anthropogenic aerosols, greenhouse gases (GHG), and natural radiative forcing on global coupled ocean-atmosphere system, and were specifically designed for attribution studies—as well as pre-Industrial control simulations, which only contain unforced internal climate variability, to investigate the drivers of simulated Sahel precipitation variability. The comparison of single-forcing and historical simulations highlights the importance of anthropogenic and volcanic aerosols over GHG in generating forced Sahel rainfall variability that reinforces the observed pattern, with anthropogenic aerosols alone responsible for the low-frequency component of simulated variability. However, the forced MMM only accounts for a small fraction of observed variance. A residual consistency test shows that simulated internal variability cannot explain the residual observed multidecadal variability, and points to model deficiency in simulating multidecadal variability in the forced response, internal variability, or both. In ‎Chapter 3, we investigate the causes for discrepancies in low-frequency Sahel precipitation variability between these ensembles and for model deficiency in reproducing observations. In the most recent version of CMIP – phase 6 of the Coupled Model Intercomparison Project (CMIP6) – the differences between observed and simulated variability are amplified rather than reduced: CMIP6 still grossly underestimates the magnitude of low-frequency variability in Sahel rainfall, but unlike CMIP5, historical mean precipitation in CMIP6 does not even correlate with observed multi-decadal variability. We continue to use a MMM to extract robust climate signals from simulations, but now additionally include sea surface temperature (SST) as a mediating variable in order to test the proposed physical processes. This partitions all influences on Sahel precipitation variability into five components: (1) teleconnections to SST; (2) atmospheric and (3) oceanic variability internal to the climate system; (4) the SST response to external radiative forcing; and (5) the “fast” (not mediated by SST) precipitation response to forcing. Though the coupled simulations perform quite poorly, in a vast improvement from previous ensembles, the CMIP6 atmosphere-only ensemble is able to reproduce the full magnitude of observed low-frequency Sahel precipitation variance when observed SST is prescribed. The high performance is due entirely to the atmospheric response to observed global SST – the fast response to forcing has a relatively small impact on Sahel rainfall, and only lowers the performance of the ensemble when it is included. Using the previously-established North Atlantic Relative Index (NARI) to approximate the role of global SST, we estimate that the strength of simulated teleconnections is consistent with observations. Applying the lessons of the atmosphere-only ensemble to coupled settings, we infer that both coupled CMIP ensembles fail to explain low-frequency historical Sahel rainfall variability mostly because they cannot explain the observed combination of forced and internal variability in SST. Though the fast response is small relative to the simulated response to observed SST variability, it is influential relative to simulated SST variability, and differences between CMIP5 and CMIP6 in the simulation of Sahel precipitation and its correlation with observations can be traced to differences in the simulated fast response to forcing or the role of other unexamined SST patterns. In this chapter, we use NARI to approximate the role of global SST because it is considered by some to be the best single index for estimating teleconnections to the Sahel. However, we show that NARI is only able to explain half of the high-performing simulated low-frequency Sahel precipitation variability in the atmospheric simulations with prescribed global SST. Statistical techniques commonly applied in the literature cannot distinguish between correlation and causality, so we cannot analyze the response of Sahel rainfall to global SST in more depth without atmospheric CMIP simulations targeted at every ocean basin of interest or a new method. In ‎Chapter 4, we turn to a novel technique called causal inference to qualify the notion that NARI can adequately represent the role of global SST in determining Sahel rainfall. We apply a causal discovery algorithm to CMIP6 pre-Industrial control simulations to determine which ocean basins influence Sahel rainfall in individual GCMs. Though we find that state of the art causal discovery algorithms for time series still struggle with data that isn’t generated specifically for algorithm evaluation, we robustly find that NARI does not mediate the full effect of global SST variability on Sahel rainfall in any of the climate simulations. This chapter lays the foundation for future work to fully-characterize the dependence of Sahel precipitation on individual ocean basins using the non-targeted simulations already available in CMIP – an approach which can be validated by comparing the composite results to the interventional historical simulations that are available. Furthermore, we hope this chapter will guide algorithm improvement efforts that are needed to increase the performance and usefulness of time series causal discovery algorithms on climate data.

Located in the subtropical central-eastern Bahamas, San Salvador Island is impacted by both synoptic-scale weather systems as well as local features and the North Atlantic Subtropical High. This study explores rainfall variability via one year of daily rain gauge observations in relation to daily weather patterns identified from 18 UTC surface analyses. Satellite-derived rainfall estimates are then compared to gauge observations to look at days when gauge data was missing. Though non-synoptic classifications comprised 61.1% of the days and synoptic classifications comprised 38.9% of the days, more rainfall was produced by synoptic days. Unlike other studies done on San Salvador, this study uses multiple observations—in situ, surface analyses, and satellite—to further our understanding of San Salvador’s rainfall. This study also establishes methods to explore synoptic and non-synoptic impacts on the island’s rainfall using additional years as more rain gauge data become available.

Orientador: Luci Hidalgo Nunes / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Geociencias / Made available in DSpace on 2018-08-12T03:18:05Z (GMT). No. of bitstreams: 1 Barbosa_JoaoPauloMacieira_M.pdf: 12381709 bytes, checksum: 9c7fe94773610efd6ec92aab3d1f49d8 (MD5) Previous issue date: 2008 / Resumo: Este estudo avaliou precipitações extremas no litoral do estado de São Paulo e entorno nas décadas de 1970 a 1990 nas escalas decadal, anual, sazonal e mensal. A área é caracterizada por chuvas constantes e copiosas devido a fatores geográficos e atmosféricos. O setor foi dividido em três repartições: norte, central e sul, e a partir de técnicas estatísticas, foram avaliadas as variabilidades espácio-temporais da precipitação. As técnicas empregadas para identificar extremos (distribuição normal padrão, quantis e tempo de retorno) apontaram tendências semelhantes, porém alguns aspectos foram destacados: no nível anual os quantis se mostraram mais apropriados para apontar ocorrências extremas: no nível mensal, a padronização levantou mais eventos extremos e mostrou maior similaridade com o período de retorno. Sugere-se que no estudo de eventos extremos mais de uma técnica deveria ser empregada, tendo em vista que cada uma apresentou vantagens e desvantagens. Não houve nenhum padrão espacial que apontasse alteração significativa de tendência entre as décadas, mas destaca-se que a repartição central e Ubatuba apresentaram, no geral, os totais mais expressivos de chuvas. As estações e meses mais chuvosos são mais homogêneos quanto à distribuição ano a ano das precipitações. / Abstract: The study evaluated extreme precipitation events in the coast and surrounded areas of the state of São Paulo within the decades of 1970 to 1990 at decadal, annual, seasonal and monthly scales. The area is characterized by constant and heavy rainfall due to both geographical and atmospheric factors. The sector was divided into three compartments: north, central and south, and by means of statistical techniques the rainfall spatio-temporal variability was evaluated. The techniques chosen to identify extremes (normal distribution, quantiles and return period) showed similar trends, but some aspects were enhanced: at annual basis, quantiles proved more appropriate to point out extreme occurrences; for monthly data, standardized technique rise more events as extreme ones and showed more similarities to return period. One suggests that more than one statistical technique might be used for the evaluation of extreme events, since each one presents advantages and disadvantages. No spatial pattern consistent to significant alteration among the decades was found, but one enhances that the central compartment and Ubatuba presented in general the highest amounts of rainfall. The seasons and months that registered higher totals are more homogeneous concerning the year to year precipitation distribution. / Mestrado / Análise Ambiental e Dinâmica Territorial / Mestre em Geografia

Climate variability and the carbon cycle (C-cycle) are tied together in complex feedback loops and due to these complexities there are still knowledge-gaps of this coupling. However, to make accurate predictions of future climate, profound understanding of the C-cycle and climate variability is essential. To gain more knowledge of climate variability, the study aims to identify recurring spatial patterns of the variability of precipitation anomalies over Scandinavia during spring and summer respectively between 1981 to 2014. These patterns will be related to the C-cycle through changes in summer vegetation greenness, measured as normalized difference vegetation index (NDVI). Finally, the correlation between the patterns of precipitation variability in summer and the teleconnection patterns over the North Atlantic will be investigated. The precipitation data was obtained from ERA5 from the European Centre for Medium-Range Weather Forecasts and the patterns of variability were found through empirical orthogonal function (EOF) analysis. The first three EOFs of the spring and the summer precipitation anomalies together explained 73.5 % and 65.5 % of the variance respectively. The patterns of precipitation variability bore apparent similarities when comparing the spring and summer patterns and the Scandes were identified to be important for the precipitation variability in Scandinavia during both seasons. Anomalous events of the spring EOFs indicated that spring precipitation variability had little impact on anomalies of summer NDVI. Contradictory, summer precipitation variability seemed to impact anomalies of summer NDVI in central- and northeastern Scandinavia, thus indicating that summer precipitation variability modulates some of the terrestrial C-cycle in these regions. Correlations were found between a large part of the summer precipitation variability and the Summer North Atlantic Oscillation and the East Atlantic pattern. Hence, there is a possibility these teleconnections have some impact, through the summer precipitation variability, on the terrestrial C-cycle. / Förändringar och variation i klimatet är sammankopplade med kolcykeln genom komplexa återkopplingsmekanismer. På grund av denna komplexitet är kunskapen om kopplingen mellan klimatvariation och kolcykeln fortfarande bristande, men för att möjliggöra precisa prognoser om framtida klimat är det viktigt att ha kunskap om denna koppling. För att få mer kunskap om klimatvariation syftar därför denna studie till att identifiera återkommande strukturer av nederbördsvariation över Skandinavien under vår respektive sommar från 1981 till 2014. Dessa relateras till förändringar i sommarväxtlighetens grönhet, uppmätt som skillnaden i normaliserat vegetationsindex (NDVI). Även korrelationen mellan sommarstrukturerna av nederbördsvariationen och storskaliga atmosfäriska svängningar, s.k. "teleconnections", över Nordatlanten undersöks. Nederbördsdatan erhölls från ERA5 analysdata från Europacentret för Medellånga Väderprognoser och strukturer av nederbördsvariationen identifierades genom empirisk ortogonal funktionsanalys (EOF) av nederbördsavvikelser. De tre första EOF av vår- respektive sommarnederbördsavvikelser förklarade tillsammans 73,5 % respektive 65,5 % av nederbördsvariationen. Strukturerna av nederbördsvariation under vår respektive sommar uppvisade tydliga likheter sinsemellan. Dessutom identifierades Skanderna vara av stor vikt för nederbördsvariationen i Skandinavien under båda årstider. Avvikande år av nederbördsvariation under våren indikerade att sagda nederbördsvariation haft liten påverkan på NDVI-avvikelser under sommaren. Emellertid verkade nederbördsvariationen under sommaren påverkat NDVI-avvikelser under sommaren i centrala och nordöstra Skandinavien. Detta indikerar att nederbördsvariationen under sommaren till viss del styr den terrestra kolcykeln i dessa regioner. För nederbördsvariationen under sommaren fanns korrelation mellan både Nordatlantiska sommaroscillationen och Östatlantiska svängningen. Det finns således en möjlighet att dessa "teleconnections" har en viss påverkan på den terrestra kolcykeln genom nederbördsvariationen under sommaren.

Southeastern South America (SESA), a region encompassing Paraguay, Southern Brazil, Uruguay, and northern Argentina, experienced a 23% increase in austral summer precipitation from 1902-2022, one of the largest precipitation trends observed globally. There is little consensus on the drivers of the precipitation trend, but Atlantic multidecadal variability, stratospheric ozone depletion, and greenhouse gas emissions stand out as key contributing factors. The work presented in this dissertation addresses two main questions. First, what are the historical drivers of the SESA precipitation increase? To address this, I investigate simulations from the Coupled Model Intercomparison Project (CMIP) Phases 3, 5, and 6 and find that not only do fully-coupled climate models simulate positive SESA precipitation trends that are much weaker over the historical interval, but some models persistently simulate negative precipitation trends. The same is true of two atmospheric models forced with observed historical sea surface temperatures. While future 21st-century projections yield positive ensemble mean precipitation trends that grow with increasing greenhouse-gas emissions, the mean forced response never exceeds the observed historical trend. Finally, some pre-industrial control runs occasionally simulate centennial-scale trends that fall within the observational range, but most do not. The second question I address is why climate models struggle to simulate the observed SESA precipitation trend. In an attempt to understand the model bias, I investigate one driver of SESA precipitation variability: the South American low-level jet. By developing a jet index from low-level moisture fluxes into SESA, I find that increased moisture flux through the jet accounts for 20-45% of the observed SESA precipitation trend from 1951-2020 in two reanalysis datasets. While results vary among reanalyses, both point to increased humidity as a fundamental driver of increased moisture flux and precipitation. Increased humidity within the jet is consistent with warming sea surface temperatures driven by anthropogenic forcing, although additional natural climate variations also may have played a role. The jet’s velocity also increased, further enhancing precipitation, but without a clear connection to anthropogenic forcing. These findings indicate that the SESA precipitation trend is partly attributable to jet intensification arising from both natural variability and anthropogenic forcing. In my final research chapter, I explore whether CMIP6 models simulate a realistic relationship between SESA precipitation and the jet, as well as whether inaccuracies in the characterization of the jet could explain muted trends in simulated SESA precipitation. I find that the interannual variability in the simulated jet-precipitation relationship aligns well with results from observations from 1951-2014. Interannual precipitation variability across the models is primarily dominated by the jet’s velocity. The models simulate a forced increase in humidity within the jet, consistent with observations and theory, that contributes a positive trend to SESA precipitation. Given that the models generally simulate realistic jet-precipitation relationships, I conclude that model misrepresentation of the jet is not a likely explanation for the discrepancy between simulated and observed SESA precipitation trends. Despite remaining uncertainties, my work sheds new light on our understanding of SESA precipitation variability and trends. Future work is needed to better understand the large-scale drivers of SESA precipitation outside of the jet and why climate models largely underestimate or fail to reproduce the observed precipitation trend. While Atlantic multidecadal variability is often cited as an important contributor to the SESA precipitation trend, I find austral summer forcing from the Atlantic to be ambiguous with regard to SESA precipitation and requires further analysis. Additionally, I highlight the Pacific South American mode as another contributing factor that warrants further exploration.

Precipitation changes and urban growth are two factors altering the state of water quality. Changes in precipitation will alter the amount and timing of flows, and the corresponding sediment and nutrient dynamics. Meanwhile, densification associated with urban growth will create more impervious surfaces which will alter sediment and nutrient loadings. Land and water managers often rely on models to develop possible future scenarios and devise management responses to these projected changes. We use the Soil and Water Assessment Tool (SWAT) to assess the sensitivities of stream flow, sediment, and nutrient loads in two urbanizing watersheds in Northwest Oregon, USA to various climate and urbanization scenarios. We evaluate the spatial patterns climate change and urban growth will have on water, sediment and nutrient yields. We also identify critical source areas (CSAs) and investigate how implementation of vegetative filter strips (VFS) could ameliorate the effects of these changes. Our findings suggest that: 1) Water yield is tightly coupled to precipitation. 2) Large increases in winter and spring precipitation provide enough sub-surface storage to increase summertime water yields despite a moderate decrease in summer precipitation. 3) Expansion of urban areas increases surface runoff and has mixed effects on sediment and nutrients. 4) Implementation of VFS reduces pollutant loads helping overall watershed health. This research demonstrates the usefulness of SWAT in facilitating informed land and water management decisions.

Conselho Nacional de Desenvolvimento Científico e Tecnológico / This study presents the analysis of precipitation trends for the Southern region of Brazil, for three present climate periods with a comparison between precipitation data and data of the regional Model RegCM3 and an analysis of future climate tendency. The study also presents the pattern of precipitation variability which operates in the South region o f Brazil. Using the Principal Components Analysis for daily precipitation data filtered in 2/20 day band, 20/100 day band and interannual band, spatial and temporal fields were found which represent the maximum variance of certain variabilities and in these, the variability pattern of precipitation for each season. And with the standard deviation series it was possible to determine the quantity of dry and rainy events present in each variability pattern. The pattern were calculated in three periods: from 1981 to 2007 for the precipitation data by Dr. Liebmann, from 1982 to 2006 for the data of the RegCM3 Model and from 2070 to 2086 for the data of the Model for future climate, being the A2 the scenery for emission used for the last period (considered pessimist). Three variability pattern were found, they were denominated Modo Sul, Modo PR and Modo Niño. / Este trabalho apresenta a análise de tendências de precipitação para a região Sul do Brasil, para três conjuntos de dados com uma comparação entre dados de precipitação e dados do Modelo regional RegCM3 e uma análise de tendência para o clima futuro. O trabalho também apresenta os modos de variabilidade de precipitação que atuam na região Sul do Brasil. Utilizando a análise de componentes principais em dados de precipitação diária filtrados nas bandas 2/20 dias, 20/100 dias e banda interanual foram encontrados campos espaciais e temporais que representam a máxima variância de determinadas variabilidades, e neles foram detectados os modos de variabilidade de precipitação para cada estação do ano. E com as séries de desvio padrão foi possível determinar a quantidade de eventos secos e chuvosos presentes em cada modo de variabilidade. Os modos foram calculados para três períodos: de 1981 a 2007 para os dados de precipitação do Dr. Liebmann, de 1982 a 2006 para os dados do Modelo RegCM3 e de 2070 a 2086 para os dados do Modelo para o clima futuro, sendo que o cenário de emissão utilizado para este ultimo período foi o A2 (considerado pessimista). Foram encontrados três modos de variabilidade denominados Modo Sul, Modo PR e Modo Niño.

Submitted by Emanuel Varela Cardoso (emanuel.varela@ufcg.edu.br) on 2018-05-14T20:21:01Z No. of bitstreams: 1 ADRIANA DE SOUZA COSTA – DISSERTAÇÃO (PPGMET) 2016.pdf: 6197666 bytes, checksum: 77b57b9618b2649efa4e6fbe2526f94f (MD5) / Made available in DSpace on 2018-05-14T20:21:01Z (GMT). No. of bitstreams: 1 ADRIANA DE SOUZA COSTA – DISSERTAÇÃO (PPGMET) 2016.pdf: 6197666 bytes, checksum: 77b57b9618b2649efa4e6fbe2526f94f (MD5) Previous issue date: 2016-02-29 / Capes / O Estado do Maranhão está localizado numa zona de transição entre o semiárido nordestino, a Amazônia quente e úmida e os chapadões do Brasil central, dando ao Estado características peculiares. Embora, o Estado não se encontre no contexto do polígono das secas, por apresentar condições climáticas bem definidas, a distribuição espacial e temporal das chuvas são bastantes irregulares, o que submete o sistema agrícola da região a sérios problemas, com impactos econômicos e sociais significativos. Diante dessas particularidades, o objetivo do estudo foi analisar e compreender a variabilidade da precipitação e relacioná-la com a TSM e outros sistemas meteorológicos que influenciem as chuvas no Estado. Para tal, empregou-se o método da Transformada de Ondeletas (TO) para identificar em diferentes escalas de oscilações o sinal da precipitação e da TSM, e assim apontar os sistemas que contribuem nas diferentes nas escalas de tempo. Os resultados mostraram no espectro global de energia da ondeleta que o ciclo anual é o dominante em todas as localidades analisadas. E, que além da escala anual observam-se também interações com as escalas sazonal, intrasazonal, semi-anual, bianual e até decadal. No tocante a TSM do Pacífico Equatorial, a escala anual é mais intensa no setor leste do oceano, decrescendo no sentido leste-oeste, onde a escala decadal se torna mais acentuada. A relação entre a chuva nas regiões homogêneas (RH) do Maranhão e a TSM do oceano Pacífico Equatorial mostrou que existe correlações importantes entre as mesmas. Ou seja, as áreas do Niño que apresentaram as maiores correlações com as RH foram: Niño 3 com a RH1(correlação r = -0,72), com a RH2 (correlação r = -0,65), com a RH3 (correlação r = -0,69), com a RH4 (correlação r = -0,53), e Niño 1+2 com a RH5 (correlação r = -0,52). / The State of Maranhão is located in a transition zone between the semi-arid northeast, the hot and humid Amazon and the plains of central Brazil, giving the state peculiar characteristics. Although the state is not in the polygon the context of drought, have welldefined climatic conditions, the spatial and temporal distribution of rainfall is quite irregular, which submits the agricultural system of the region to serious problems, with significant economic and social impacts . Given these characteristics, the objective of the study was to analyze and understand the variability of precipitation and relate it to the TSM and other weather systems that influence rainfall in the state. To this end, we used the method of Wavelet Transform (TO) to identify different scales fluctuations sign of precipitation and TSM, and thus point the systems involved in the different time scales. The results showed the overall spectrum of the wavelet energy that the annual cycle is the dominant in all analyzed locations. And that in addition to the annual scale also observe up interactions with seasonal scales, intraseasonal, semi-annual, bi-annual and decadal up. Regarding the TSM Equatorial Pacific, the annual scale is more intense in the east of the ocean sector, decreasing from east to west, where the decadal scale becomes more pronounced. The relationship between rainfall in homogeneous regions (HR) of Maranhao and the ocean TSM Equatorial Pacific showed that there is significant correlation between them. Ie areas of Niño with the highest correlations with HR were: Niño 3 with RH1 relationship (r = -0.72), with RH2 relationship (r = -0.65), with RH3 (correlation r = -0.69), as RH4 (correlation r = -0.53) and Nino 1 + 2 with RH5 relationship (r = -0.52).

One important aspect related to the management of water resources under future climate variation is the occurrence of extreme precipitation events. In order to prepare for extreme events, namely floods and droughts, it is important to understand how future climate variability will influence the occurrence of such events. Recent advancements in regional climate modeling efforts provide additional resources for investigating the occurrence of extreme events at scales that are appropriate for regional hydrologic modeling. This study utilizes data from three Regional Climate Models (RCMs), each driven by the same General Circulation Model (GCM) as well as a reanalysis dataset, all of which was made available by the North American Regional Climate Change Assessment Program (NARCCAP). A comparison between observed historical precipitation events and NARCCAP modeled historical conditions over Oregon's Willamette River basin was performed. This comparison is required in order to investigate the reliability of regional climate modeling efforts. Datasets representing future climate signal scenarios, also provided by NARCCAP, were then compared to historical data to provide an estimate of the variability in extreme event occurrence and severity within the basin. Analysis determining magnitudes of two, five, ten and twenty-five year return level estimates, as well as parameters corresponding to a representative Generalized Extreme Value (GEV) distribution, were determined. The results demonstrate the importance of the applied initial/boundary driving conditions, the need for multi-model ensemble analysis due to RCM variability, and the need for further downscaling and bias correction methods to RCM datasets when investigating watershed scale phenomena.