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Aedes-borne diseases, such as dengue and chikungunya, are responsible for more than 50 million infections worldwide every year, with an overall increase of 30-fold in the last 50 years, mainly due to city population growth, more frequent travels and ecological changes. In the United States of America, the vast majority of Aedes-borne infections are imported from endemic regions by travelers, who can become new sources of mosquito infection upon their return home if the exposed population is susceptible to the disease, and if suitable environmental conditions for the mosquitoes and the virus are present. Since the susceptibility of the human population can be determined via periodic monitoring campaigns, the environmental suitability for the presence of mosquitoes and viruses becomes one of the most important pieces of information for decision makers in the health sector. We present a next-generation monitoring and forecasting system for Ae ̲ des -borne diseases' environmental suitability (AeDES) of transmission in the conterminous United States and transboundary regions, using calibrated ento-epidemiological models, climate models and temperature observations. After analyzing the seasonal predictive skill of AeDES, we briefly consider the recent Zika epidemic, and the compound effects of the current Central American dengue outbreak happening during the SARS-CoV-2 pandemic, to illustrate how a combination of tailored deterministic and probabilistic forecasts can inform key prevention and control strategies. [ABSTRACT FROM AUTHOR]

Potential and real predictive skill of the frequency of extreme rainfall in southeastern South America for the December-February season are evaluated in this paper, finding evidence indicating that mechanisms of climate variability at one time scale contribute to the predictability at another scale; that is, taking into account the interference of different potential sources of predictability at different time scales increases the predictive skill. Part I of this study suggested that a set of daily atmospheric circulation regimes, or weather types, was sensitive to these cross-time scale interferences, conducive to the occurrence of extreme rainfall events in the region, and could be used as a potential predictor. At seasonal scale, a combination of those weather types indeed tends to outperform all the other candidate predictors explored (i.e., sea surface temperature patterns, phases of the Madden-Julian oscillation, and combinations of both). Spatially averaged Kendall's τ improvements of 43% for the potential predictability and 23% for real-time predictions are attained with respect to standard models considering sea surface temperature fields alone. A new subseasonal-to-seasonal predictive methodology for extreme rainfall events is proposed based on probability forecasts of seasonal sequences of these weather types. The cross-validated real-time skill of the new probabilistic approach, as measured by the hit score and the Heidke skill score, is on the order of twice that associated with climatological values. The approach is designed to offer useful subseasonal-to-seasonal climate information to decision-makers interested not only in how many extreme events will happen in the season but also in how, when, and where those events will probably occur. [ABSTRACT FROM AUTHOR]

The physical mechanisms and predictability associated with extreme daily rainfall in southeastern South America (SESA) are investigated for the December-February season in a two-part study. Through a k-mean analysis, this first paper identifies a robust set of daily circulation regimes that are used to link the frequency of rainfall extreme events with large-scale potential predictors at subseasonal-to-seasonal scales. This represents a basic set of daily circulation regimes related to the continental and oceanic phases of the South Atlantic convergence zone (SACZ) and wave train patterns superimposed on the Southern Hemisphere polar jet. Some of these recurrent synoptic circulation types are conducive to extreme rainfall events in the region through synoptic control of different mesoscale physical features and, at the same time, are influenced by climate phenomena that could be used as sources of potential predictability. Extremely high rainfall (as measured by the 95th and 99th percentiles) is associated with two of these weather types (WTs), which are characterized by moisture advection intrusions from lower latitudes and the Pacific Ocean; another three WTs, characterized by above-normal moisture advection toward lower latitudes or the Andes, are associated with dry days (days with no rain). The analysis permits the identification of several subseasonal-to-seasonal scale potential predictors that modulate the occurrence of circulation regimes conducive to extreme rainfall events in SESA. It is conjectured that a cross-time scale interaction between the different climate drivers improves the predictive skill of extreme precipitation in the region. [ABSTRACT FROM AUTHOR]

Understanding what causes weather‐related stresses that lead to crop failures is a critical step toward stabilizing global food production. While there are many sources of weather‐related stresses, the 30–60 days of Madden‐Julian Oscillation (MJO) is the dominant source of subseasonal climate variability in the tropics, making it a potential—but as of yet unexplored—source of crop yield anomalies. Here crop models and observational yield statistics are used to assess whether the MJO affects maize yields. We find that the influence of the MJO is widespread; it can increase or reduce maize yields throughout the tropics. In dry, hot environments the MJO can reduce maize yields by reducing precipitation, decreasing soil moisture, and increasing extreme heat, while in wetter, cooler environments—where water stress is less common—MJO‐forced decreases in rainfall bring increases in solar radiation that benefits maize yields. These results provide a pathway to develop actionable early warnings using subseasonal forecasts. Key Points: The MJO affects maize yields in regions throughout the tropics and subtropicsIn dry hot environments the MJO contributes to crop failures by reducing precipitation, decreasing soil moisture, and increasing heat stressIn sufficiently wet environments MJO‐induced decreases in rainfall reduces cloud cover, increases solar radiation, and benefits crop yields [ABSTRACT FROM AUTHOR]