Hydrologic and climate models predict variables through a simplification of the underlying complex natural processes. Model development involves minimising predictive uncertainty. Predictive uncertainty arises from three broad sources which are measurement error in observed responses, uncertainty of input variables and model structural error. This thesis introduces ways to improve predictive accuracy of hydroclimatic models by considering input and structural uncertainties. The specific methods developed to reduce the uncertainty because of erroneous inputs and model structural errors are outlined below. The uncertainty in hydrological model inputs, if ignored, introduces systematic biases in the parameters estimated. This thesis presents a method, known as simulation extrapolation (SIMEX), to ascertain the extent of parameter bias. SIMEX starts by generating a series of alternate inputs by artificially adding white noise in increasing multiples of the known input error variance. The resulting alternate parameter sets allow formulation of an empirical relationship between their values and the level of noise present. SIMEX is based on the theory that the trend in alternate parameters can be extrapolated back to the notional error free zone. The case study relates to erroneous sea surface temperature anomaly (SSTA) records used as input variables of a linear model to predict the Southern Oscillation Index (SOI). SIMEX achieves a reduction in residual errors from the SOI prediction. Besides, a hydrologic application of SIMEX is demonstrated by a synthetic simulation within a three-parameter conceptual rainfall runoff model. This thesis next advocates reductions of structural uncertainty of any single model by combining multiple alternative model responses. Current approaches for combining hydroclimatic forecasts are generally limited to using combination weights that remain static over time. This research develops a methodology for combining forecasts from multiple models in a dynamic setting as an improvement of over static weight combination. The model responses are mixed on a pair wise basis using mixing weights that vary in time reflecting the persistence of individual model skills. The concept is referred here as the pair wise dynamic weight combination. Two approaches for forecasting the dynamic weights are developed. The first of the two approaches uses a mixture of two basis distributions which are three category ordered logistic regression model and a generalised linear autoregressive model. The second approach uses a modified nearest neighbour approach to forecast the future weights. These alternatives are used to first combine a univariate response forecast, the NINO3.4 SSTA index. This is followed by a similar combination, but for the entire global gridded SSTA forecast field. Results from these applications show significant improvements being achieved due to the dynamic model combination approach. The last application demonstrating the dynamic combination logic, uses the dynamically combined multivariate SSTA forecast field as the basis of developing multi-site flow forecasts in the Namoi River catchment in eastern Australia. To further reduce structural uncertainty in the flow forecasts, three forecast models are formulated and the dynamic combination approach applied again. The study demonstrates that improved SSTA forecast (due to dynamic combination) in turn improves all three flow forecasts, while the dynamic combination of the three flow forecasts results in further improvements.

Developing remote sensing instrumentation allows obtaining information about an area rapidly and with low costs. This fact offers a challenge to remote sensing algorithms aimed at extracting information about an area from the available re&not / mote sensing data. A very typical and important problem being interpretation of satellite images. A very efficient approach to remote sensing is employing discrim&not / inant functions to distinguish different landscape classes from satellite images. Various methods on this direction are already studied. However, the efficiency of the studied methods are still not very high. In this thesis, we will improve efficiency of remote sensing algorithms. Besides we will investigate improving boundary detection methods on satellite images.

The response of the tropical Atlantic Ocean to wind stress forcing on seasonal and interannual time scales is examined using an ocean data assimilation product from the Geophysical Fluid Dynamics Laboratory (GFDL), and an ocean general circulation model which incorporates a three dimensional flux correction technique to correct biases of the mean state of the ocean. On a seasonal time scale, we investigated the impact of the annual migration of the ITCZ on the exchange pathways of the northern tropical Atlantic. The results indicate that seasonal variation of the zonal slope of the thermal ridge along the boundary between the north equatorial countercurrent and north equatorial current in response to changes in the ITCZ controls, to a large extent, the amount of water participating in the equatorial circulation. These changes can be explained in terms of a simple dynamical model where local Ekman pumping dominates thermocline variation in the western part of the basin, and Rossby wave adjustment comes into play in the eastern basin. On an interannual time scale, we examined the upper heat budget of the equatorial Atlantic in order to identify the key mechanisms by which wind-driven ocean dynamics control SST variability during the onset and peak phases of the Atlantic zonal mode. It is found that, in contrast with Pacific ENSO, both Bjerknes and Ekman feedbacks act together to force the zonal mode, although their relative importance and dominance depend on season and location.

Sediment core MD02-2550 from Orca Basin located in the northern Gulf of Mexico (GOM) provides a high-resolution early Holocene record of climatic and hydrologic changes from ~10.5 to 7 thousand calendar years before present (ka). Paired analyses of Mg/Ca and δ18O on the planktonic foraminifer Globigerinoides ruber (white variety, 250-355 μm) sampled at ~ 20 year resolution were used to generate proxy records of sea surface temperature (SST) and the δ18O of seawater in the GOM (δ18OGOM). The Mg/Ca-SST record contains an overall ~1.5 °C warming trend from 10.5 to 7 ka that appears to track the intensity of the annual insolation cycle and six temperature oscillations (0.5-2 °C), the frequency of which are consistent with those found in records of solar variability. The δ18OGOM record contains six ~ 0.5 ‰ oscillations from 10.5 to 7 ka that bear some resemblance to regional hydrologic records from Haiti and the Cariaco Basin, plus a -0.8 ‰ excursion that may be associated with the “8.2 ka event” recorded in Greenland air temperatures. The δ18OGOM record, if interpreted as a salinity proxy, suggest large salinity fluctuations (> 2 ‰) reflecting changes in evaporation-precipitation (E-P) and Mississippi River input to the GOM. Percent Globigerinoides sacculifer records from three cores in the GOM exhibit remarkably coherent changes, suggesting episodic centennial-scale incursions of Caribbean waters. Spectral analysis of the Mg/Ca-SST and the δ18OGOM time series indicate that surface water conditions may be influenced by solar variations because they share significant periods of variability with atmospheric Δ 14C near 700, 200, and 80-70 years. Our results add to the growing body of evidence that the sub-tropics were characterized by significant decadal to centennial-scale climatic and hydrologic variability during the early Holocene.

Physical processes in the Gulf of Mexico (GOM) and mesoscale (10-300 km) processes associated with the Loop Current are fairly well known. However, little is known about the physical/ biological interactions of the frontal boundary system of the Loop Current. Zooplankton abundance and distribution was determined at 28 stations in the vicinity of the Loop Current. Species richness was high at all stations. Copepods comprised 60% of the total zooplankton collected. Oithona plumifera, Nannocalanus minor and Euchaeta marina were the most abundant copepods. Chaetognaths and ostracods were also very abundant and made up 11 and 5 % respectively of the zooplankton total. Total zooplankton abundance was higher at the boundary of the LC than it was inside the LC but not significantly different from abundances outside of the LC. Stations in the western Gulf of Mexico and on the western boundary had the highest abundances of zooplankton overall. The chlorophyll concentrations at the chlorophyll maximum were higher at the boundary of the LC than inside the LC. Physical-biological processes associated with the frontal boundary of the LC appear to influence the abundance and distribution of zooplankton in the GOM.

In the first phase of this project, four decades of monthly resolved geochemical variations from two massive heads of Montastraea were used to explore the reproducibility of the geochemical signal in these two corals from Looe Key, Florida. The coral d18O and d13C records of the two corals have statistically indistinguishable mean values, which is not the case for the coral Sr/Ca records implying that nonenvironmental factors are influencing coral Sr/Ca. Calibration equations relating coral geochemistry variations to environmental variations at Looe Key are different from previously published equations for Montastraea. These calibration differences are not related to growth-related kinetic effects, but may reflect variations in seawater chemistry in the coastal waters of the Florida Keys. Additional studies are needed to identify the causes of the observed geochemical variability. In the second phase of this study, fourteen decades of monthly resolved geochemical variations in another Montastraea coral from Looe Key, Florida were compared to records of sea-surface temperature (SST). Coral Sr/Ca and d18O variations have a weak relationship with variations in SST and skeletal extension rates; however, many events in the Sr/Ca and d18O records are coincident with anomalies in SST, growth, or precipitation. Strong coupling exists between Sr/Ca and d18O in both anomaly and mean annual perspectives, which reflects the combined influence of SST and growth related processes on the geochemical signal. Separating these impacts proved to be problematic due to modest agreements with each forcing variable. In the final phase of this study, geochemical records from three, mid-Holocene(~5 ka) fossil Montastraea corals from the Dry Tortugas, Florida were compared with geochemical records from modern Montastraea corals from the same region to investigate temporal changes in climate. Stable isotopic records show significant changes through time, which can be interpreted in terms of environmental variation; however, large inter-coral variability between modern specimens of Montastraea precludes meaningful assessment of Sr/Ca. The pattern and mean d18O values in the fossil corals reflects changes in both temperature and salinity are reminiscent of centennial-scale variability present in other records from this region.

Hydrologic and climate models predict variables through a simplification of the underlying complex natural processes. Model development involves minimising predictive uncertainty. Predictive uncertainty arises from three broad sources which are measurement error in observed responses, uncertainty of input variables and model structural error. This thesis introduces ways to improve predictive accuracy of hydroclimatic models by considering input and structural uncertainties. The specific methods developed to reduce the uncertainty because of erroneous inputs and model structural errors are outlined below. The uncertainty in hydrological model inputs, if ignored, introduces systematic biases in the parameters estimated. This thesis presents a method, known as simulation extrapolation (SIMEX), to ascertain the extent of parameter bias. SIMEX starts by generating a series of alternate inputs by artificially adding white noise in increasing multiples of the known input error variance. The resulting alternate parameter sets allow formulation of an empirical relationship between their values and the level of noise present. SIMEX is based on the theory that the trend in alternate parameters can be extrapolated back to the notional error free zone. The case study relates to erroneous sea surface temperature anomaly (SSTA) records used as input variables of a linear model to predict the Southern Oscillation Index (SOI). SIMEX achieves a reduction in residual errors from the SOI prediction. Besides, a hydrologic application of SIMEX is demonstrated by a synthetic simulation within a three-parameter conceptual rainfall runoff model. This thesis next advocates reductions of structural uncertainty of any single model by combining multiple alternative model responses. Current approaches for combining hydroclimatic forecasts are generally limited to using combination weights that remain static over time. This research develops a methodology for combining forecasts from multiple models in a dynamic setting as an improvement of over static weight combination. The model responses are mixed on a pair wise basis using mixing weights that vary in time reflecting the persistence of individual model skills. The concept is referred here as the pair wise dynamic weight combination. Two approaches for forecasting the dynamic weights are developed. The first of the two approaches uses a mixture of two basis distributions which are three category ordered logistic regression model and a generalised linear autoregressive model. The second approach uses a modified nearest neighbour approach to forecast the future weights. These alternatives are used to first combine a univariate response forecast, the NINO3.4 SSTA index. This is followed by a similar combination, but for the entire global gridded SSTA forecast field. Results from these applications show significant improvements being achieved due to the dynamic model combination approach. The last application demonstrating the dynamic combination logic, uses the dynamically combined multivariate SSTA forecast field as the basis of developing multi-site flow forecasts in the Namoi River catchment in eastern Australia. To further reduce structural uncertainty in the flow forecasts, three forecast models are formulated and the dynamic combination approach applied again. The study demonstrates that improved SSTA forecast (due to dynamic combination) in turn improves all three flow forecasts, while the dynamic combination of the three flow forecasts results in further improvements.

Submitted by Maria Medeiros (maria.dilva1@ufcg.edu.br) on 2018-08-14T11:48:17Z No. of bitstreams: 1 RONI VALTER DE SOUZA GUEDES - TESE (PPGMet) 2016.pdf: 13257786 bytes, checksum: 624133c9b10421f7ba2d7cf8d0eacf79 (MD5) / Made available in DSpace on 2018-08-14T11:48:17Z (GMT). No. of bitstreams: 1 RONI VALTER DE SOUZA GUEDES - TESE (PPGMet) 2016.pdf: 13257786 bytes, checksum: 624133c9b10421f7ba2d7cf8d0eacf79 (MD5) Previous issue date: 2015-12-18 / CNPq / A identificação e classificação de áreas susceptíveis à ocorrência de eventos críticos, chuvosos ou secos, tornaram-se uma necessidade frequente no contexto da variabilidade climática, responsável por muitos desastres naturais em diversos países do mundo. O diagnóstico com base nos impactos meteorológicos, agrícolas e hidrológicos pode ser aferido através de índices climáticos. O Índice de Precipitação Padronizado (SPI) foi desenvolvido para diagnosticar e categorizar a variabilidade da precipitação com base em diferentes escalas temporais. A aplicação da metodologia do SPI para 57 postos distribuídos sobre o estado de Pernambuco, Nordeste do Brasil, com séries de 1963 a 2015, foi capaz de destacar e classificar as principais anomalias das chuvas através da sua intensidade e duração. As escalas menores do SPI (mensal e trimestral) indicaram o início e tendência de cada evento; a escala semestral identificou o comportamento do período chuvoso e as escalas anual e bienal definiram os eventos mais fortes e duradouros. Foram diagnosticados eventos positivos e negativos nas categorias de fraco, moderado, severo e extremo. Foram analisados os eventos que ocorreram de forma mais generalizada e, portanto, mais significativos. Foram destacados os eventos chuvosos críticos de 1963, 1973, 1984 e os eventos secos de 1993, 1998 e 2012. A análise de agrupamento utilizando a métrica de Ward foi aplicada aos SPIs para delimitar dois grupos bem definidos para qualquer escala temporal do SPI. A divisão do estado de Pernambuco ficou assim: Grupo 1, do Litoral ao Agreste e o Grupo 2 representando todo o Sertão. Os valores das anomalias de temperatura da superfície do mar foram correlacionados com cada escala do SPI e usados como entrada nos modelos baseados em Redes Neurais Artificiais (RNA) para predizer as variações deste índice na área de estudo. Os resultados mostraram que o modelo apresentou uma boa previsão com o padrão de comportamento da escala trimestral do SPI, e não obteve o mesmo nível de desempenho para as escalas mensais e semestrais, porém, o modelo de RNA conseguiu absorver a tendência dos valores destas escalas e encontrar uma boa associação. / The identification and classification of areas susceptible to critical events be it rainy or dry events, has become a frequent need in the current context of climate variability, esponsible for natural disasters in several countries in the world. The diagnosis based on meteorological, agricultural and hydrological impacts can be measured by climatic indices. The Standardized Precipitation Index (SPI) was developed to categorize and make the diagnostic the variability of the rainfall based on different temporal scales. The application of SPI methodology to 57 stations distributed about the state of Pernambuco, Northeastern Brazil, for the years 1963 to 2015, was able to highlight and rank the main anomalies of rainfall through its intensity and duration. Smaller scales the SPI (monthly and quarterly) indicated the start and trend of each event, the semiannual scale identified the behavior of rainy period and the annual and biennial scales it defined the strongest and most enduring events. Positive and negative events were diagnosed in the scale categories: low, moderate, severe and extreme. Were analyzed the events that occurred more widely and thus more significant. Were highlighted the critical rainfall events of 1963, 1973, 1984 and the dry events of 1993, 1998, 2012. The cluster analysis using the metric of Ward was applied to SPIs to delimit the two well-defined groups to any timescale of the SPI. The division of Pernambuco state was as follows: Group 1, from Coast to Agreste and Group 2 represents the entire Sertão. The values of the temperature anomalies of the sea surface were correlated with each SPI scale and used as input in models based on Artificial Neural Networks (ANN) to predict the variations of this index in the study area. The results showed that the model had a good forecast with the standard of behavior of the quarterly SPI scale, but did not get the same level of performance for the monthly and semi-annual scales, but the model the ANN was able to absorb the trend of the values of these scales and find a good association.

Abstract The diurnal cycle is a dominant mode of sea surface temperature (SST) variability in trop-ical oceans, that influences air-sea interaction and climate processes. Diurnal variability of SST generally ranges from ~0.1 to 2.0◦C and is controlled by atmospheric fluxes of heat and momentum. In the present study, the response of intraseasonal variability (ISV) of SST in the Bay of Bengal (BoB) to diurnal atmospheric forcings, during the summer monsoon of 2007, has been examined using an Ocean General Circulation Model (OGCM). The model is based on the Modular Ocean Model Version 4 (MOM4p0), having a horizontal resolution of 0.25◦ and 40 vertical levels, with a fine resolution of 5 m in the upper 60 m. Numerical experiments were conducted by forcing the model with daily and hourly atmospheric forcings to examine the SST-ISV modulation with the diurnal cycle. Additional experiments were performed to determine the relative role of diurnal cycle in solar radiation and winds on SST and mixed layer depth (MLD). Since salinity, which is decisive in SST variability, varies meridionally in the BoB, two locations were selected for analyses: one in the northern bay at 89◦E, 19◦N where salinity is lower and the other in the southern bay at 90◦E, 8◦N where salinity is higher, as well as observations are available from Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) buoy for comparision with model simulation. Diurnal atmospheric forcings modify SST-ISV in both southern and northern bay. SST-ISV in the southern bay, is dominantly controlled by the diurnal cycle of insolation, while in the northern bay, diurnal cycle of insolation and winds have comparable contribution. Diurnal cycle enhanced the amplitude of 3 selected intraseasonal events in the southern bay and 3 out of the 6 events in the northern bay, during the study period. In the southern bay, simulated SST variability with hourly forcing was closer to the observations from RAMA, implying that incorporating the diurnal cycle in model forcing rectifies SST-ISV. Moreover, SST obtained with diurnal forcing consists of additional fluctuations at higher frequencies within and in between intraseasonal events; such fluctuations are absent with daily forcing. The diurnal variability of SST is significant during the warming phase of intraseasonal events and reduces during the cooling phase. Diurnal amplitude of SST decreases with depth; depth dependence also being larger during the warming phase. SST-ISV modulation with diurnal forcing results from the diurnal cycle of upper ocean heat fluxes and vertical mixing. Diurnal warming and cooling result in a net gain or loss of heat in the mixed layer after a day’s cycle. When the retention (loss) of heat in the mixed layer increases with diurnal forcing during the warming (cooling) phase of intraseasonal events, the daily mean SST rise (fall) becomes higher, amplifying the intraseasonal warming (cooling). In the southern bay, SST-ISV amplification is mainly controlled by the diurnal variability of MLD, which modifies the heat fluxes. Increased intraseasonal warming with diurnal forcing results from the increase in radiative heating, due to the shoaling of the daytime mixed layer. Amplified intraseasonal cooling is dominantly con-trolled by the strengthening of sub-surface processes, due to the nocturnal deepening of mixed layer and increased temperature gradients below the mixed layer. In the northern bay, SST-ISV modulation with diurnal forcing is not as large as that in the southern bay. The mean increase in SST-ISV amplitudes with diurnal forcing is ~0.16◦C in the southern bay, while it is only ~0.03◦C in the northern bay. Reduced response of SST-ISV to diurnal forcings in the northern bay is related to the weaker diurnal variability of MLD. Salinity stratification limits diurnal variability of mixed layer in the northern bay, unlike in the southern bay. The seasonal (June - September) mean diurnal amplitude of MLD is ~15 m in the southern bay, while it is reduced to ~1.5 m in the northern bay. Diurnal variability of MLD, spanning only a few meters is not sufficient to create large modifications in mixed layer heat fluxes and SST-ISV in the northern bay. The vertical resolution of the model limits the shallowing of mixed layer to 7.5 m, thus restricting the diurnal variability of simulated MLD.

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