Ángel Garikoitz Muñoz

Background: Vector-borne diseases transmitted by Aedes mosquitoes such as dengue, Zika, and chikungunya pose significant public health challenges worldwide in the wake of climate change. However, while their transmission is related to specific temperature, rainfall or humidity conditions, the overall role of large-scale climate drivers on the emergence of these diseases is not so well understood. Establishing the most important timeframes for Aedes-borne disease prediction and identifying specific climate patterns that drive its emergence can be key in the development of climate-based prediction systems for these arboviruses. Methods: The response of the climate component of Aedes-borne disease transmission to large-scale climate patterns is explored through their respective climate variability indices. A preliminary timescale decomposition analysis assesses the primary timescales over which climate processes condition disease transmissibility, while later correlation and causality analyses identify the most relevant climate drivers for Aedes-borne diseases. Findings: Year-to-year variations in temperature and precipitation have a significant impact on the environmental suitability for Aedes-borne diseases in the tropics, whereas seasonal fluctuations have a stronger impact in temperate regions instead. Overall, while some climate indices such as Niño 3.4, Tropical North Atlantic and the Indian Ocean Basin Mode stand out as modulators of climate-driven disease risk, it is the effect of multiple climate patterns, through compound interactions, that ultimately shape climate-based transmission dynamics. Interpretation: Aedes-borne disease transmission is susceptible to multiple climate factors, including long-term climate change and seasonal variability. The results of this study highlight the multi-faceted role of climate patterns in disease emergence, and their potential applicability in the development of climate-based early warning systems for Aedes-borne disease outbreaks. These findings can be integrated into an actionable disease prediction system using multiple climate patterns as predictors, which can ultimately better inform public health strategies to manage outbreaks.

Aedes -borne diseases, such as dengue, Zika, and chikungunya, pose a significant threat to millions of people worldwide each year. Considering the relationships between the emergence of these diseases and anthropogenic climate change, it has become imperative for health authorities to maintain detailed surveillance of key environmental variables that can trigger epidemic episodes. While disease transmission is generally conditioned by multiple socio-economic factors, environmental suitability for vectors and viruses to proliferate is a necessary—although not sufficient—condition that needs to be closely monitored. To this end, the Aedes-borne dIsease monitoring of environmental suitability (AIMES) system improves on the disease transmissibility framework of AeDES, the most up-to-date system of this kind in the current literature. AIMES broadens both the temporal and spatial scope of its predecessor, while simultaneously enhancing the quality of the environmental suitability index. It provides continuously updated monthly historical values derived from multiple observational references, enabling a robust quantification of the observational uncertainty.

The seasonal cycle of rainfall in Mesoamerica exhibits complex features such as the Midsummer Drought (MSD) that greatly influence local agriculture and motivate diagnosing the potential of its subseasonal prediction. This study evaluates state‐of‐the‐art S2S forecast systems for their representation of the rainy season through their climatology and forecast skill. These models reasonably capture the main features of the rainy season, such as onset and retreat dates, as well as the MSD characteristics. However, they overestimate the MSD duration by 5–10 days. Forecast skill for weekly precipitation is low beyond week 2, whereas rainy season onset and retreat remain predictable past 25 days, especially for the European Center for Medium‐Range Weather Forecasts and United Kingdom Met Office forecasts. MSD onset forecasts are more skillful than MSD end forecasts, suggesting different modulating processes for each date. Because of the significant influence of the El Niño‐Southern Oscillation (ENSO) on the variability of MSD, we evaluate its effects on predictability and forecast skill. La Niña increases precipitation and enhances predictability and prediction skill of precipitation from June to September, whereas El Niño reduces mean rainfall and forecasts are more skillful during the MSD‐periods. These results highlight ENSO's role in subseasonal forecasting and suggest that ENSO phase information could help improve decision‐making in planning sowing dates and regional water resource management.

The observed warming in the Western Mediterranean (WMed) region over recent decades is projected to continue, outpacing the global average, making the region a prominent climate change hotspot. Even within this relatively small area, the combination of natural climate variability and anthropogenic climate change creates significant spatial variation in extreme climate events. This study analyzes mean seasonal temperature and precipitation trends in the WMed using ECMWF Reanalysis v5 (ERA5) data from 1951 to 2020 taking into consideration the climatic heterogeneity of the region. A non‐hierarchical K‐Means clustering method was applied to delineate nine climatically homogeneous regions within the WMed. A timescale decomposition analysis was then conducted to disentangle long‐term, decadal, and interannual variability components of seasonal data. Results indicate that long‐term trends explain most of the total observed variance in temperature (∼65%), whereas interannual natural variability dominates observed precipitation (∼60%). These patterns vary seasonally, with the strongest warming trends along the Mediterranean coast of the Iberian Peninsula and northern Africa in summer and the most significant drying trends in the southwestern Mediterranean during both summer and winter. The influence of different modes of climate variability in the observed trends is assessed and appears to contribute to winter drying, but no evidence was found of its influence on warming trends. This study lays a foundation for future climate change detection and attribution efforts in the WMed, emphasizing the need for sub‐regional analysis due to the region's pronounced heterogeneity.

Development and dissemination of seasonal forecasts are integral components of the climate services provided by numerous meteorological services worldwide, offering estimates of meteorological variables on a seasonal time scale to aid local warning systems and decision‐making processes. The World Meteorological Organization (WMO) recommends that operational seasonal forecasts be objective and that the process be traceable and reproducible, including the selection and calibration of models. Following these guidelines, the Chilean Meteorological Service (Dirección Meteorológica de Chile, DMC) has implemented the next generation of seasonal forecasts, NextGen‐Chile. This new forecast system is based on a multi‐model ensemble using state‐of‐the‐art general circulation models (GCMs) from the calibrated North American Multi‐Model Ensemble (NMME) project. The forecasts from the GCMs are calibrated using a canonical correlation analysis‐based regression with a homogenised dataset of ground stations. The system is completed with two statistical models built using canonical correlation analysis on sea surface temperature (SST) in the ENSO and the Southwestern Pacific regions. Individually calibrated GCMs and statistical models are combined by weighing their hindcast skill to construct the final calibrated multi‐model ensemble (CMME) prediction. A verification analysis of probabilistic re‐forecasts during 2019–2021 has been performed, adding an average‐based ensemble forecast (CMME‐Mean). The CMME models outperformed the individual models in discrimination and showed less seasonal variability in performance than the individual models, adding consistency to the forecast. All metrics analysed during the verification process were maximised in the central region of Chile, which could be attributed to the high concentration of ground stations in the central region and the definition of a central region‐centred domain for the CCA calculation. Looking into the near future of NextGen‐Chile, a Flexible Seasonal Forecast is introduced as a more comprehensive approach for seasonal forecasts, allowing users and stakeholders to access information beyond the tercile seasonal forecast approach.

The observed warming in the Western Mediterranean (WMed) region over recent decades is projected to continue, outpacing the global average, making the region a prominent climate change hotspot. Even within this relatively small area, the combination of natural climate variability and anthropogenic climate change creates significant spatial variation in extreme climate events. This study analyzes temperature and precipitation trends in the WMed using ECMWF Reanalysis v5 (ERA5) data from 1951 to 2020 taking into consideration the climatic heterogeneity of the region. A non-hierarchical K-Means clustering method was applied to delineate nine climatically homogeneous regions within the WMed. A timescale decomposition analysis was then conducted to disentangle long-term, decadal, and interannual variability components of seasonal data. Results indicate that long-term trends explain most of the total observed variance in temperature (~65%), while interannual natural variability dominates observed precipitation (~60%). These patterns vary seasonally, with the strongest warming trends along the Mediterranean coast of the Iberian Peninsula and northern Africa in summer and the most significant drying trends in the southwestern Mediterranean during both summer and winter. The influence of different modes of natural climate variability in the observed trends is assessed and appears to contribute to winter drying, but no evidence was found of its influence on warming trends. This study lays a foundation for future climate change detection and attribution efforts in the WMed, emphasizing the need for sub-regional analysis due to the region’s pronounced heterogeneity.

Here we introduce a demand-driven framework designed to implement climate services in the health sector, with a particular focus on the Caribbean region. Climate services are essential for supporting informed decision-making and response strategies in relation to climate-related health risks. Through collaborative efforts, we are co-producing a climate-driven dengue early warning system (EWS) to target vector-borne diseases effectively. While challenges exist in implementing such systems, EWSs provide valuable tools for managing epidemic risks by predicting potential disease outbreaks in advance. The scarcity of operational climate tools in the health sector underscores the need for increased investment and strategic implementation practices. To address these challenges, a demand-driven framework is proposed, emphasizing strategic planning focused on health intervention development, partnership building, data, communication, human resources, capacity building, and sustainable funding. This framework aims to integrate climate services seamlessly into health systems, thereby enhancing public health resilience and facilitating well-informed decision-making to effectively address climate-sensitive diseases.

En agosto de 2021, el Panel Intergubernamental sobre Cambio Climático (IPCC) presentó nuevos resultados sobre la próxima generación de escenarios de cambio climático, denominado: "Trayectorias Socioeconómicas Compartidas". Estos escenarios expresan los cambios de temperatura y precipitación bajo 4 escenarios de cambio climático durante el resto del siglo XXI, utilizando alrededor de 33 modelos de baja resolución que forman parte del Couple Model Intercomparison Project Phase 6 (CMIP6). El objetivo de utilizar estos resultados fue realizar un downscaling estadístico para obtener escenarios de alta resolución espacial a nivel nacional a lo largo del siglo XXI. Para ello, se utilizaron los estándares de predicción desarrollados por la Organización Meteorológica Mundial (OMM, 2020), que sugieren el uso de varios modelos, utilizando solo aquellos que están calibrados por patrones espaciales, y entregando predicciones en un formato flexible (función de densidad de probabilidad). La próxima generación de escenarios de cambio climático sugiere que la temperatura media aumentará entre 1,1 °C y 2,0 °C en un escenario SSP1-2,6, y entre 3,5 °C y 6,2 °C en un escenario SSP5-8,5 para finales del siglo XXI. con respecto a la climatología de referencia 1981-2010. Los mayores incrementos se encuentran en la región Caribe Sur, Andina Central y Sur, y en gran parte de la Orinoquía y Amazonía. Independientemente del escenario, los volúmenes anuales de precipitación no cambiarán demasiado con respecto al clima actual. Con esos cambios en la temperatura media y la precipitación, la clasificación climática de Lang estima que el Caribe y partes de la región Andina dejarán de ser zonas semiáridas y migrarán hacia condiciones áridas, mientras que áreas de la Amazonía migrarán de superhúmedo a climas húmedos.

Rainfall mechanisms in the Central American Isthmus are controlled by complex physical interactions across spatial and temporal scales, which are reflected on the dynamics of atmospheric circulation patterns affecting the region. However, physical mechanisms and their relationships with thermodynamic distributions connected to overturning circulations remain elusive. Here, a set of six recurrent daily atmospheric patterns, or weather types (WT), is defined using a k‐means++ clustering algorithm on standardized fields of Convective Available Potential Energy (CAPE) and winds at 925, 850, and 200 hPa. The relationships between these weather types, their temporal characteristics, and anomalous distributions of moisture flux divergence, equivalent potential temperature (saturated and unsaturated), and observed rainfall are used to describe physical processes controlling the latter, for all seasons. Regional observed rainfall is analysed from a set of 174 automatic stations from all countries from Mexico to Panama. By modulating vertically integrated moisture fluxes, these weather types, and the different climate drivers linked to them, control the temporal and spatial rainfall characteristics in the region, especially over the Pacific side of the isthmus. During some stages of the regional rainy season, described by two weather types, thermal anomalies in convective quasi‐equilibrium characteristic of the upward branch of the Hadley cell force westerly flow over Central America, enhancing rainfall. While during other stages, the enhancement of the trades and the displacement of convection to the ITCZ area over the eastern tropical Pacific, characteristic of the midsummer drought, diminishes rainfall. This study sets the stage for a better understanding of the mechanistic relationship between these weather types and rainfall characteristics in general, like onset, demise, and duration of rainy seasons. Hence, these results can inform process‐based model diagnostics aiming at bias‐correcting climate predictions at multiple timescales.