A workshop was held at the University of the West Indies, Jamaica, in May 2012 to build capacity in climate data rescue and to enhance knowledge about climate change in the Caribbean region. Scientists brought their daily observational surface temperature and precipitation data from weather stations for an assessment of quality and homogeneity and for the calculation of climate indices helpful for studying climate change in their region. This study presents the trends in daily and extreme temperature and precipitation indices in the Caribbean region for records spanning the 1961-2010 and 1986-2010 intervals. Overall, the results show a warming of the surface air temperature at land stations. In general, the indices based on minimum temperature show stronger warming trends than indices calculated from maximum temperature. The frequency of warm days, warm nights and extreme high temperatures has increased while fewer cool days, cool nights and extreme low temperatures were found for both periods. Changes in precipitation indices are less consistent and the trends are generally weak. Small positive trends were found in annual total precipitation, daily intensity, maximum number of consecutive dry days and heavy rainfall events particularly during the period 1986-2010. Correlations between indices and the Atlantic multidecadal oscillation (AMO) index suggest that temperature variability and, to a lesser extent, precipitation extremes are related to the AMO signal of the North Atlantic surface sea temperatures: stronger associations are found in August and September for the temperature indices and in June and October for some of the precipitation indices.

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Using multi-date satellite imagery and field observations, this paper assesses the inferred impacts of September 2017 cyclones on the beaches of Saint-Martin Island. Twenty-two beaches out of 30 predominantly exhibited shoreline retreat, with the highest retreat value (-166.45 m) recorded on the north-eastern coast. While erosion predominated on beaches and at the sand dune front, inner areas generally exhibited accretion, with sand sheets (up to 135 m from the pre-cyclone vegetation line) indicating landward sediment transfer. Natural back-reef beaches exhibited the formation of new beach ridges, marked (up to 2 m) upward growth and alongshore beach extension. The high spatial variability of inferred impacts is attributed to the cyclone's track, coast exposure, beach configuration and, importantly, human-driven environmental change. Whereas vegetation removal exacerbated marine inundation and inhibited the vertical accretion of beaches, shoreline hardening aggravated wave-induced sediment loss while also inhibiting sediment deposition. Four beach response modes are distinguished. Based on findings, we identified three major areas of action for risk reduction and adaptation to climate change. Depending on beach response and site specificities, relocation and the determination of set-back lines, coastal buffer restoration, or engineered structures' upgrading should be prioritized.

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Parametric cyclonic wind fields are widely used worldwide for insurance risk underwriting, coastal planning, and storm surge forecasts. They support high-stakes financial, development and emergency decisions. Yet, there is still no consensus on a potentially “best” parametric approach, nor guidance to choose among the great variety of published models. The aim of this paper is to demonstrate that recent progress in estimating extreme surface wind speeds from satellite remote sensing now makes it possible to assess the performance of existing parametric models, and select a relevant one with greater objectivity. In particular, we show that the Cyclone Global Navigation Satellite System (CYGNSS) mission of NASA, along with the Advanced Scatterometer (ASCAT), are able to capture a substantial part of the tropical cyclone structure, and to aid in characterizing the strengths and weaknesses of a number of parametric models. Our results suggest that none of the traditional empirical approaches are the best option in all cases. Rather, the choice of a parametric model depends on several criteria, such as cyclone intensity and the availability of wind radii information. The benefit of using satellite remote sensing data to select a relevant parametric model for a specific case study is tested here by simulating hurricane Maria (2017). The significant wave heights computed by a wave-current hydrodynamic coupled model are found to be in good agreement with the predictions given by the remote sensing data. The results and approach presented in this study should shed new light on how to handle parametric cyclonic wind models, and help the scientific community conduct better wind, wave, and surge analyses for tropical cyclones.