Eric Jose Alfaro Martínez

This article has three objectives: 1) modeling the climate-dengue relationship at the smallest administrative division (districts) using high-resolution data; 2) use of an objective algorithm for the selection of predictors that results in parsimonious models, cross-validated to prevent overfitting; and 3) using estimates from CMIP6 climate models to provide mid-century (2035-2065) potential dengue incidence projections under a pessimistic scenario (SSP5-8.5) with seasonal windows actionable by region in Costa Rica to guide preparedness decisions. Results show that temperature and precipitation data are significantly related to dengue incidence. Projections of dengue cases for mid-century show increments of up to 42 more cases in some districts compared to the historical scenario. It should be remembered that ultimately dengue variations and change are related to climatic and non-climatic factors and the results presented here represent a future potential increase of the dissemination of the disease, based on the projected climate change of the most pessimistic scenario.

The Standardized Precipitation Index (SPI) is widely used to determine drought severity worldwide. However, inconsistencies exist regarding its application in warm, humid tropical climatic zones. Originally developed for temperate regions with a continental climate, the index may not adequately reflect drought conditions in tropical environments where rainfall regimes differ substantially. This study identifies the following two principal reasons why the traditional calculation method fails to characterize drought severity in tropical domains: first, the marked humidity contrast between the consistently humid rainy season and the rest of the year, and second, the diverse drought types in tropical regions, which include both long-term and short-term events. Using data from meteorological stations in Mexico’s humid tropics and comparing them with temperate regions, the study demonstrates significant discrepancies between SPI-based drought classifications and actual precipitation patterns. Our analysis shows that the abundant precipitation during the rainy season causes biases in longer time scales integrated into multivariate drought indices. Considerations are established for adapting the SPI for decision makers who monitor drought in humid tropics, with specific recommendations on time scale limits to avoid biases. This work contributes to more accurate drought monitoring in tropical regions by addressing the unique climatic characteristics of these environments.

Introduction: Warming is already significant in Central America and the Caribbean and may be magnified even further in the future. A decrease in the precipitation is projected, increasing also regional aridity. Objective: To study observed and projected latitudinal gradients for precipitation and temperature in three Southern Caribbean locations of Central America: Bluefields (Nicaragua), Limón (Costa Rica) and Bocas del Toro (Panamá) and to characterize their future changes and determine if there are differences or similarities in a north-south direction. Methods: Monthly precipitation (P) and temperature (T) data from General Circulation Models from 1979 to 2099, were downloaded from the WRF repository. Data from the selected models from the repository were subjected to a delta-type statistical downscaling to bring them to a resolution of 1 x 1 km. These models are part of the latest generation of the Coupled Model Intercomparison Project-Phase 6 used by the Intergovernmental Panel on Climate Change. The ground-truth data necessary for bias correction were obtained from the ERA5 reanalysis. Monthly P and T data were downloaded from 1979 to 2014 at different native spatial resolutions and climatologies at 1 x 1 km spatial resolution at global scales were obtained from WorldClim data. Results: Scenarios show that some regions would go from very humid to humid, based on strong reductions in precipitation and warming at the end of the 21st century. This expected increase in the aridity is going to have impacts on ecology and ecosystem services, agriculture, human consumption due to a water availability reduction per capita and hydroelectric generation. Conclusions: Generation of high spatial Climate Change scenarios is necessary because Central America is a region characterized by significant topographic complexity, land use variety and spatial occurrence of hydrometeorological disasters. This intrinsic variability suggests that local risk management and planning strategies must be designed with a highly specific approach to each locality or region. This implies that, even in areas geographically near to each other, the measures taken may not necessarily be transferable due to differences in climate projections, as it was found for the three nearby communities in the Southern Central American Caribbean coastal region.

The evaluation of climate models is a crucial step in climate studies. It consists of quantifying the resemblance of model outputs to reference data to identify models with superior capacity to replicate specific climate variables. Clearly, the choice of the evaluation indicator significantly impacts the results, underscoring the importance of selecting an indicator that properly captures the characteristics of a “good model”. This study examines the behaviour of six indicators, considering spatial correlation, distribution mean, variance and shape. Monthly data for precipitation, temperature and teleconnection patterns in Central America were utilized in the analysis. A new multicomponent measure was selected based on these criteria to assess the performance of 32 CMIP6 models in reproducing the annual seasonal cycle of these variables. The top six models were determined using multicriteria methods. It was found that even the best model reproduces one derived climatic variable poorly in this region. The proposed measure and selection method can contribute to enhancing the accuracy of climatological research based on climate models.

Se determina el inicio y término de la estación lluviosa (IELL y TELL respectivamente), además de la duración de la estación seca y lluviosa (DES y DELL respectivamente), con datos de precipitación en rejilla sobre Guanacaste para el periodo 1950-2020 conformado por el acople de dos conjuntos de datos, el primero es el conjunto Climate Hazards group Infrared Precipitation with Stations (CHIRPS) y el segundo siendo el reanálisis ERA5 al que se le aplicó un ajuste de escala para extender la cobertura temporal de los datos de CHIRPS. Se caracterizó su IELL, TELL, DELL y DES con estadísticos como la mediana, desviación estándar, rango inter cuartil y tendencias. Además, se relacionaron con fuentes de variabilidad climática como El Niño-Oscilación del Sur (ENOS), la temperatura superficial del océano Atlántico Tropical Norte (ATN) y la corriente en chorro de bajo nivel del Caribe (CLLJ, por sus siglas en inglés). Los datos representan adecuadamente la climatología del IELL, TELL, DELL, DES y las precipitaciones de la región, además de capturar la variabilidad asociada a la CLLJ y al acople de los océanos (ATN-ENOS), pero no la asociada al ATN y ENOS individualmente. Aunque no se pudo determinar si esto se debe a los datos de ERA5 o al método de reducción de escala usado, por lo que se recomienda verificar si ERA5 captura tal variabilidad oceánica al realizar el mismo análisis en un periodo de tiempo de 1980-2020 donde se tiene mayor disponibilidad de observaciones como estaciones meteorológicas o CHIRPS que ya ha sido validada.

Precipitation and temperature extremes from daily data indexed using the CLIMDEX methodology were calculated over the Central American region. The data comprises the coarsened versions of the Climate Hazards and Infrared Precipitation with stations (CHIRPs) and the corresponding data set for temperature (CHIRTs) from the year 1981 to 2020 and 1983 to 2016, respectively. The objective is to detect trend patterns in extremes in recent periods, use novel statistical techniques for assessing the trend significance and study the monthly and annual trends for each of the indices. Trends of extreme temperature indices show more consistent, robust and widespread significant results according with the observed warming of the region. Significant extreme precipitation indices trends are more localized, and therefore harder to analyse, but it seems that one robust result from several indices is the trend toward more intense extreme precipitation events in Costa Rica. The findings of this work suggest possible impacts in human and environmental systems across the region.

The rainfall regime is a critical factor in the Yucatan Peninsula, as the spatial and multiannual variability of rainfall is a major concern, particularly for crops. Variability in the rainy season was examined considering the onset and demise of the annual rainy season, the total rain volume, the rainfall season duration and the intense precipitation events recorded in meteorological stations (1978–2020). We analysed individual time series and calculated the long‐term trend. Additionally, we explored the relationship between each summer rainfall characteristic and several oceanographic indices using multivariate techniques. We also developed a Trans‐Isthmic Index from the relationship between the El Niño–Southern Oscillation and the Atlantic Multidecadal Oscillation. This index allows for determining the effect of the overall influence of the ocean on climate. The timeseries analysis revealed a high interannual variability and long‐term positive trends concerning the duration of the rainy season with earlier onset and later demise, and the total rainfall volume and also a positive trend for the occurrence of heavy precipitation suggesting a shift in intra‐annual patterns. Spatially, the analysis revealed clusters of stations with a similar variation, probably related to the AMO, NIÑO3.4 or TII indices. The spatial pattern was confirmed by analysing CHIRPS gridded precipitation data. Our results show that wetter conditions are associated with lower temperatures in the equatorial Pacific and warmer conditions in the Atlantic.