[1] Sea-level trends and their forcing have been investigated in the Caribbean Sea using altimetry and tide gauge time series from 19 stations. The basin average sea-level rise is 1.7 6 1.3 mm yr À1 for the period 1993-2010. Significant spatial variability of the trends is found. The steric variability above 800 m combined with the global isostatic adjustment explains the observed trends for the altimetry period in most of the basin. Wind forcing changes causes the trends in the southern part of the basin, modulating the sea level through changes in the ocean circulation. The longest time series (102 years) of Cristobal shows a trend of 1.9 6 0.1 mm yr À1 insignificantly different from the global mean sea-level rise for the twentieth century. By contrast Cartagena, a world heritage site, has a large trend (5.3 6 0.3 mm yr À1 ) significantly affected by local vertical land movements. Stations dominated by the steric contribution have smaller trends ($1.3 6 0.2 mm yr À1 ). Sea-level trends at tide gauges are not affected by atmospheric pressure changes or by the open ocean steric contribution at most stations. Decadal variability in the sea-level trends can partly be explained by steric and wind variability. The decadal variability in the trends is not spatially coherent. Interannual sea-level variability accounts for one third of the total sea-level variability and can be partly explained by the influence of El Niño-Southern Oscillation at different time and spatial scales. No correlation with the North Atlantic Oscillation is found.

[1] The seasonal sea level cycle has been investigated in the Caribbean Sea using altimetry and tide gauge time series from 27 stations and is characterized by large spatial variability. The coastal annual harmonic has amplitudes that range from 2 cm to 9 cm, peaking between August and October and semi-annual harmonic with maximum amplitude of 6 cm, with most stations peaking in April and October. The coastal seasonal sea level cycle contributes significantly at most areas to sea level variability and can explain the sea level variance up to 78%. The barometric effect on the coastal sea level seasonal cycles is insignificant in the annual component but dominant at 9 stations in the semi-annual cycle. The seasonal sea level cycle from 18 years of altimetry confirm the results obtained from the tide-gauges and allow us to identify some dominant sea level forcing parameters in the annual and semi-annual frequencies such as the Panama-Colombia gyre driven by the wind stress curl and the Caribbean Low Level Jet modulating the sea level in the northern coast of South America and linked to the local upwelling. The seasonal sea level cycle in the Caribbean Sea is unsteady in time, with large variations in amplitude and phase lag at most of the stations, where the 5-year amplitude in the coastal annual cycle can change over 6 cm in a 24 year period. The seasonal sea level cycle peaks about October when the probability of coastal impacts increases, especially in the northern coast of South America where the range is larger.

Sea level extremes in the Caribbean Sea are analyzed on the basis of hourly records from 13 tide gauges. The largest sea level extreme observed is 83 cm at Port Spain. The largest nontidal residual in the records is 76 cm, forced by a category 5 hurricane. Storm surges in the Caribbean are primarily caused by tropical storms and stationary cold fronts intruding the basin. However, the seasonal signal and mesoscale eddies also contribute to the creation of extremes. The five stations that have more than 20 years of data show significant trends in the extremes suggesting that flooding events are expected to become more frequent in the future. The observed trends in extremes are caused by mean sea level rise. There is no evidence of secular changes in the storm activity. Sea level return periods have also been estimated. In the south Colombian Basin, where large hurricane-induced surges are rare, stable estimates can be obtained with 30 years of data or more. For the north of the basin, where large hurricane-induced surges are more frequent, at least 40 years of data are required. This suggests that the present data set is not sufficiently long for robust estimates of return periods. ENSO variability correlates with the nontidal extremes, indicating a reduction of the storm activity during positive ENSO events. The period with the highest extremes is around October, when the various sea level contributors' maxima coincide.

[1] The tidal signal and its long-term variation in the Caribbean Sea is analyzed on the basis of hourly records from thirteen tide gauges five of which span more than 20 years. The seven larger tidal constituents are studied, namely the fortnightly term M f , diurnal K 1 , O 1 , and P 1 , and semidiurnal M 2 , S 2 and N 2 . The 18.61 year nodal modulation is clearly identifiable in almost all the examined constituents of lunar origin. However, its signal in M 2 is less clear while it is almost imperceptible in N 2 , where the 8.85 year cycle caused by the eccentricity of the Moon's orbit and the orientation of its major axis variation dominates the long-term variability. The effect of the nodal variation in the amplitude and phase lag of the various tidal constituents is in agreement (within the 95% error limits) with the theoretical gravitational estimate, with the exception of the 8.85 year cycle in N 2 , where larger values are found. Overall, in the Caribbean the net effect of the low frequency cycles can change the maximum tidal range from 16.5% to 23.5% in a nodal cycle. Although the Caribbean is a micro-tidal environment this still results in changes of the range of up to 8.4 cm. Significant, spatially coherent trends are found for the amplitude of S 2 (2.3 to 8.8 mm/cy).

Sumário: 1. Introdução; 2. Dados; 3. Testes e resultados para retornos de curto prazo; 4. Retornos de médio a longo prazo; 5. Conclusão. Palavras-chave: passeio aleatório; eficiência; sazonalidade; não linearidade; anomalia. Códigos JEL: C12; C22 e G14.Este artigo testa duas versões do modelo de passeio aleatório para os preços de carteiras de ações no mercado brasileiro. Evidências contrárias a tal modelo foram observadas nos horizontes diário e semanal, caracterizados por persistência. As evidências foram mais fracas em períodos mais recentes. Foram também encontradas sazonalidades diárias, incluindo o efeito segunda-feira, e mensais. Adicionalmente, foi observado um padrão de assimetria de autocorrelações cruzadas de primeira ordem entre os retornos de carteiras de firmas agrupadas segundo seu tamanho, indicando, no caso de retornos diários e semanais, que retornos de firmas grandes ajudam a prever retornos de firmas pequenas. Evidências de não-linearidades nos retornos foram observadas em diversos horizontes de tempo. This paper tests two versions of the random walk model for portfolios of Brazilian stocks. It found evidence of persistency in daily and weekly returns, rejecting the random walk models. Those evidences were weaker in recent periods. The paper also found a Monday effect, and other seasonality effects for monthly returns. Additionally there were asymmetric first-order cross-correlations on portfolios ranked by size, with large firm returns predicting small firm returns. Nonlinearities in returns were also detected at several time horizons.

A variety of wildfire models are currently used for prescribed fire management, fire behaviour studies and decision support during wildfire emergencies, among other applications. All these applications are based on predictive analysis, and therefore require careful estimation of aleatoric and epistemic uncertainties such as weather conditions, vegetation properties and model parameters. However, the large computational cost of high-fidelity computaional fluid dynamics models prohibits the straightforward utilization of traditional Monte Carlo methods. Conversely, low-fidelity fire models are several orders of magnitude faster but they typically do not provide enough accuracy and they do not resolve all relevant phenomena. Multifidelity frameworks offer a viable solution to this limitation through the efficient combination of high-and low-fidelity simulations. While high-fidelity models provide the required level of accuracy, low-fidelity simulations are used to economically improve the confidence on estimated uncertainty. In this work, we assessed the suitability of multifidelity methodologies to quantify uncertainty in wildfire simulations. A collection of different multifidelity strategies, including Multilevel and Control Variates Monte Carlo, were tested and their computational efficiency compared. Fire spread was predicted in a canonical scenario using popular simulators such as the Wildland-Urban Interface Fire Dynamics Simulator (WFDS) and FARSITE. Results show that multifidelity estimators allow speedups in the order of 100× to 1000× with respect to traditional Monte Carlo.