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Background: Over the last 5 years (2013-2017), the Caribbean region has faced an unprecedented crisis of co-occurring epidemics of febrile illness due to arboviruses transmitted by the Aedes sp. mosquito (dengue, chikungunya, and Zika). Since 2013, the Caribbean island of Barbados has experienced 3 dengue outbreaks, 1 chikungunya outbreak, and 1 Zika fever outbreak. Prior studies have demonstrated that climate variability influences arbovirus transmission and vector population dynamics in the region, indicating the potential to develop public health interventions using climate information. The aim of this study is to quantify the nonlinear and delayed effects of climate indicators, such as drought and extreme rainfall, on dengue risk in Barbados from 1999 to 2016.Methods and Findings: Distributed lag nonlinear models (DLNMs) coupled with a hierarchal mixed-model framework were used to understand the exposure-lag-response association between dengue relative risk and key climate indicators, including the standardised precipitation index (SPI) and minimum temperature (Tmin). The model parameters were estimated in a Bayesian framework to produce probabilistic predictions of exceeding an island-specific outbreak threshold. The ability of the model to successfully detect outbreaks was assessed and compared to a baseline model, representative of standard dengue surveillance practice. Drought conditions were found to positively influence dengue relative risk at long lead times of up to 5 months, while excess rainfall increased the risk at shorter lead times between 1 and 2 months. The SPI averaged over a 6-month period (SPI-6), designed to monitor drought and extreme rainfall, better explained variations in dengue risk than monthly precipitation data measured in millimetres. Tmin was found to be a better predictor than mean and maximum temperature. Furthermore, including bidimensional exposure-lag-response functions of these indicators-rather than linear effects for individual lags-more appropriately described the climate-disease associations than traditional modelling approaches. In prediction mode, the model was successfully able to distinguish outbreaks from nonoutbreaks for most years, with an overall proportion of correct predictions (hits and correct rejections) of 86% (81%:91%) compared with 64% (58%:71%) for the baseline model. The ability of the model to predict dengue outbreaks in recent years was complicated by the lack of data on the emergence of new arboviruses, including chikungunya and Zika.Conclusion: We present a modelling approach to infer the risk of dengue outbreaks given the cumulative effect of climate variations in the months leading up to an outbreak. By combining the dengue prediction model with climate indicators, which are routinely monitored and forecasted by the Regional Climate Centre (RCC) at the Caribbean Institute for Meteorology and Hydrology (CIMH), probabilistic dengue outlooks could be included in the Caribbean Health-Climatic Bulletin, issued on a quarterly basis to provide climate-smart decision-making guidance for Caribbean health practitioners. This flexible modelling approach could be extended to model the risk of dengue and other arboviruses in the Caribbean region. [ABSTRACT FROM AUTHOR]

The Last Interglacial climatic optimum, ca. 128 ka, is the most recent climate interval significantly warmer than present, providing an analogue (albeit imperfect) for ongoing global warming and the effects of Greenland Ice Sheet (GIS) melting on climate over the coming millennium. While some climate models predict an Atlantic meridional overturning circulation (AMOC) strengthening in response to GIS melting, others simulate weakening, leading to cooling in Europe. Here, we present evidence from new proxy-based paleoclimate and ocean circulation reconstructions that show that the strongest warming in western Europe coincided with maximum GIS meltwater runoff and a weaker AMOC early in the Last Interglacial. By performing a series of climate model sensitivity experiments, including enhanced GIS melting, we were able to simulate this configuration of the Last Interglacial climate system and infer information on AMOC slowdown and related climate effects. These experiments suggest that GIS melt inhibited deep convection off the southern coast of Greenland, cooling local climate and reducing AMOC by ~24% of its present strength. However, GIS melt did not perturb overturning in the Nordic Seas, leaving heat transport to, and thereby temperatures in, Europe unaffected. [ABSTRACT FROM AUTHOR]