The 2018 outbreak of dengue in the French overseas department of Réunion was unprecedented in size and spread across the island. This research focuses on the cause of the outbreak, asserting that climate played a large role in the proliferation of the Aedes albopictus mosquitoes, which transmitted the disease, and led to the dengue outbreak in early 2018. A stage‐structured model was run using observed temperature and rainfall data to simulate the life cycle and abundance of the Ae. albopictus mosquito. Further, the model was forced with bias‐corrected subseasonal forecasts to determine if the event could have been forecast up to 4 weeks in advance. With unseasonably warm temperatures remaining above 25°C, along with large tropical‐cyclone‐related rainfall events accumulating 10–15 mm per event, the modeled Ae. albopictus mosquito abundance did not decrease during the second half of 2017, contrary to the normal behavior, likely contributing to the large dengue outbreak in early 2018. Although subseasonal forecasts of rainfall for the December–January period in Réunion are skillful up to 4 weeks in advance, the outbreak could only have been forecast 2 weeks in advance, which along with seasonal forecast information could have provided enough time to enhance preparedness measures. Our research demonstrates the potential of using state‐of‐the‐art subseasonal climate forecasts to produce actionable subseasonal dengue predictions. To the best of the authors' knowledge, this is the first time subseasonal forecasts have been used this way.

The 2018 outbreak of dengue in the French overseas department of Réunion was unprecedented in size and spread across the island. This research focuses on the cause of the outbreak, asserting that climate played a large role in the proliferation of the Aedes albopictus mosquitoes, which transmitted the disease, and led to the dengue outbreak in early 2018. A stage-structured model was run using observed temperature and rainfall data to simulate the life cycle and abundance of the Ae. albopictus mosquito. Further, the model was forced with bias-corrected subseasonal forecasts to determine if the event could have been forecast up to 4 weeks in advance. With unseasonably warm temperatures remaining above 25°C, along with large tropical-cyclone-related rainfall events accumulating 10-15 mm per event, the modeled Ae. albopictus mosquito abundance did not decrease during the second half of 2017, contrary to the normal behavior, likely contributing to the large dengue outbreak in early 2018. Although subseasonal forecasts of rainfall for the December-January period in Réunion are skillful up to 4 weeks in advance, the outbreak could only have been forecast 2 weeks in advance, which along with seasonal forecast information could have provided enough time to enhance preparedness measures. Our research demonstrates the potential of using state-of-the-art subseasonal climate forecasts to produce actionable subseasonal dengue predictions. To the best of the authors' knowledge, this is the first time subseasonal forecasts have been used this way.

<p>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 and more frequent travels. 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 once they are back in the country 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, environmental suitability for presence of mosquitoes and viruses becomes one of the most important pieces of information for decision makers in the health sector. Here, we develop a subseasonal to seasonal monitoring and forecasting system for environmental suitability of transmission of Aedes-borne diseases for the US, Central America, the Caribbean and northern South America, using multiple calibrated ento-epidemiological models, climate models, and quality-controlled temperature observations. We show that the predictive skill of this new system is higher than that of any of the individual models, and illustrate how a combination of deterministic and probabilistic forecasts can inform key prevention and control strategies.</p>

<p>Beginning July 2020, the Niño 3.4 index crossed below the threshold to La Niña conditions and remained below a -0.4 sea surface temperature anomaly through the spring of 2023, impacting agriculture, livelihoods, and communities around the world. What caused this prolonged La Niña event and why was it sustained? How did the interaction between the different modes of climate variability influence the event? The internal dynamics of ENSO, the Indian Ocean Dipole, and the Madden-Julian Oscillation are studied here through a non-linear approach utilizing compositing techniques and both linear and non-linear wave superposition to identify what caused and prolonged the 2020-2023 La Niña event.</p>

<p>Stakeholders in all socio-economic sectors require reliable forecasts at multiple timescales as part of their decision-making processes. Although basing decisions mostly on a particular timescale (e.g., weather, subseasonal, seasonal) is the present <em>status quo</em>, this approach tends to lead to missing opportunities for more comprehensive risk-management systems (Goddard et al. 2014).</p><p> </p><p>While today a variety of forecasts are produced targeting distinct timescales in a routine way, these products are generally presented to the users in different websites and bulletins, often without an assessment of how consistent the predictions are across timescales. Since different models and strategies are used at different timescales by both national and international seasonal and subseasonal forecasting centers (Kirtman et al. 2014, Kirtman et al. 2017, Vitart et al. 2017), and skill is different at those timescales, it is key to guarantee that a physically consistent “bridging” between the forecasts exists, and that the cross-timescale predictions are overall skilful and actionable, so decision makers can conduct their work.</p><p> </p><p>Here, we propose and explore a new methodology –that we call the <strong>X</strong><sub><strong>it</strong></sub> (“cross-it”) operator– based on the Liang-Kleeman information flow (e.g., Tawia Hagan et al. 2019) and wavelet spectra and entropy (e.g., Zunino et al. 2007), to “bridge” forecasts at different timescales in a smooth and physically-consistent manner.</p><p> </p><p>In summary, the <strong>X</strong><sub><strong>it</strong></sub> operator (1) conducts a wavelet spectral analysis (e.g., Ng and Chan 2013, Zunino et al. 2007) and (2) a non-stationary time-frequency causality analysis (e.g., Tawia Hagan et al. 2019, Liang 2015) on forecasts at different timescales to assess cross-timescale coherence and physical consistency in terms of various sources of predictability. In principle, the approach permits to identify which “intrinsic” periods/scales (<em>i</em>) in the timescale continuum (<em>t</em>) are more suitable for the bridging to occur, and/or which ones can produce more skillful forecasts, by pointing to particular target times—i.e., potential windows of opportunity (Mariotti et al. 2020)—in the forecast period where wavelet entropy (uncertainty) is lower.</p><p> </p><p>While the first component of the <strong>X</strong><sub><strong>it</strong></sub> operator, i.e., the wavelet spectral and entropy analysis (Zunino et al. 2007), is designed to identify the optimal time-frequency bands for cross-timescale bridging, the fact that two forecast systems (e.g., a subseasonal and a seasonal) exhibit significant wavelet coherence does not imply that bridging those systems will provide physically-consistent predictions. The second component of the <strong>X</strong><strong>it</strong> operator, i.e., the non-stationary causality analysis (Tawia Hagan et al. 2019), is thus designed to assess physical consistency of the bridging by analyzing the causal link between different climate drivers (acting at different timescales) and the forecast variable of interest.</p>