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Revista de Biología Tropical, ISSN: 2215-2075, Vol. 73(S1): e64044, enero-diciembre 2025 (Publicado Mar. 03, 2025)
Climate Change Scenarios in the Southern Caribbean
region of Central America
Eric J. Alfaro1,2,3 *; https://orcid.org/0000-0001-9278-5017
Hugo G. Hidalgo1,2,4; https://orcid.org/0000-0003-4638-0742
Paula Marcela Pérez-Briceño1,5 ; https://orcid.org/0000-0002-7217-8495
Blanca Calderón-Solera1; https://orcid.org/0009-0002-8035-2067
1. Centro de Investigaciones Geofísicas (CIGEFI), Universidad de Costa Rica, San José, Costa Rica; erick.alfaro@ucr.ac.cr
(*Correspondence); hugo.hidalgo@ucr.ac.cr; paula.perez@ucr.ac.cr; blanca.calderonsolera@ucr.ac.cr
2. Escuela de Física, Universidad de Costa Rica, San José, Costa Rica.
3. Centro de Investigación en Ciencias del Mar y Limnología (CIMAR), Universidad de Costa Rica, San José, Costa Rica.
4. Centro de Investigación en Matemática Pura y Aplicada (CIMPA), Universidad de Costa Rica, San José, Costa Rica.
5. Escuela de Geografía, Universidad de Costa Rica, San José, Costa Rica.
Received 27-IX-2024. Corrected 24-I-2025. Accepted 28-I-2025.
ABSTRACT
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 reduc-
tion 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 hydro-
meteorological 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.
Key words: precipitation; air surface temperature; climate change; downscaling; scenarios.
https://doi.org/10.15517/rev.biol.trop..v73iS1.64044
SUPPLEMENT
2Revista de Biología Tropical, ISSN: 2215-2075 Vol. 73(S1): e64044, enero-diciembre 2025 (Publicado Mar. 03, 2025)
INTRODUCTION
Warming is already significant in Central
America (CA) and the Caribbean (Alfaro-Cór-
doba et al., 2020; Arias et al., 2021; Castellanos
et al., 2022; Hidalgo et al., 2019; Jones et al.,
2016; Stephenson et al., 2014) and may be mag-
nified even further in the future (Almazroui et
al., 2021; Arias et al., 2021; Castellanos et al.,
2022; Hidalgo et al., 2013; Hidalgo et al., 2017;
Imbach et al., 2018). For CA, a decrease in
precipitation is projected causing an increase in
the severity and frequency of agricultural and
ecological drought events, especially during the
second half of the twenty-one century (Almaz-
roui et al., 2021; Arias et al., 2021; Castellanos
et al., 2022). This reduction in precipitation
increases aridity, makes the region vulnerable
and puts it at risk due to the reduction in the
supply of water resources in the isthmus (Cas-
tellanos et al., 2022).
The Caribbean slope of CA generally has
a humid tropical climate, type Af according
to the Köppen classification, whose character-
istics are regular raining conditions almost in
all months, therefore there is no well-defined
dry season. The average temperature is above
18 °C every month, astronomical winter does
not occur, and annual rainfall is abundant and
exceeds evaporation (Fallas & Oviedo, 2003;
Galvin 2007). However, when using other cli-
mate classifications with higher spatial scale
such as those of Thornthwaite or Hargreaves
(Pérez-Briceño et al., 2017), this slope may
present important spatial variations, mainly
due to the complex topography of the terrain
(Quesada-Román & Pérez-Briceño, 2019) and
the action of multiple oceanic and atmospheric
RESUMEN
Escenarios de Cambio Climático en la Región del Caribe Sur de América Central
Introducción: El calentamiento ya es significativo en América Central y el Caribe y puede magnificarse aún más
en el futuro. Se proyecta también una disminución en la precipitación, aumentando la aridez regional.
Objetivo: Estudiar los gradientes latitudinales observados y proyectados para la precipitación y la temperatura
en tres localidades del Caribe Sur de América Central: Bluefields (Nicaragua), Limón (Costa Rica) y Bocas
del Toro (Panamá) y caracterizar sus cambios futuros y determinar si existen diferencias o similitudes en una
dirección norte-sur.
Métodos: Los datos mensuales de precipitación (P) y temperatura (T) de los Modelos de Circulación General de
1979 a 2099, fueron descargados del repositorio WRF. Los datos de los modelos seleccionados del repositorio
fueron sometidos a un ajuste de escala estadístico de tipo delta para llevarlos a una resolución de 1 x 1 km.
Estos modelos son parte de la última generación del Proyecto de Intercomparación de Modelos Acoplados-Fase
6 utilizado por el Panel Intergubernamental sobre Cambio Climático. Los datos necesarios para la corrección
de sesgos se obtuvieron del reanálisis ERA5. Los datos mensuales de P y T se descargaron de 1979 a 2014 en
diferentes resoluciones espaciales nativas y las climatologías con resolución espacial de 1 x 1 km a escala global
se obtuvieron de los datos de WorldClim.
Resultados: Los escenarios muestran que algunas regiones pasarían de muy húmedas a húmedas, con base en
fuertes reducciones en la precipitación y el calentamiento a finales del siglo XXI. Este aumento esperado en la
aridez tendrá impactos en la ecología y los servicios ecosistémicos, la agricultura, el consumo humano debido a
una reducción en la disponibilidad de agua per cápita y la generación hidroeléctrica.
Conclusiones: La generación de escenarios de Cambio Climático de alta resolución es necesaria porque América
Central es una región caracterizada por una importante complejidad topográfica, variedad de usos del suelo y
ocurrencia espacial de desastres hidrometeorológicos. Esta variabilidad intrínseca sugiere que las estrategias
locales de gestión y planificación de riesgos deben diseñarse con un enfoque altamente específico para cada
localidad o región. Esto implica que, incluso en zonas geográficamente cercanas entre sí, las medidas adoptadas
pueden no necesariamente ser transferibles debido a las diferencias en las proyecciones climáticas, como se
encontró para las tres comunidades cercanas en la región costera del Caribe Sur de América Central.
Palabras clave: Precipitación, temperatura superficial del aire, Cambio Climático, ajuste de escala, escenarios.
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forcing forces acting on varied time and space
scales (Durán-Quesada et al., 2020; Maldonado
et al., 2018; Orozco-Montoya & Penalba, 2023).
In spite of the fact that the CA Caribbean
slope does not show a well-defined dry season,
drought periods, along with warmer tempera-
tures, occur modulated by climate variability.
An example is the recent event from 2020 to
2024 reported by the Costa Rican Meteorologi-
cal Institute (Madriz, 2023).
According to Alfaro et al. (2024), the
Central American Caribbean slope is located
windward of the trade winds which are intrin-
sically connected with atmospheric pressure
variations of the North Atlantic Subtropical
High or NASH (Taylor & Alfaro, 2005). These
trade winds are very consistent all year round
and have a northeast-east component over
the isthmus that interacts with the complex
topography of the region (Alfaro et al., 2018;
Amador et al., 2016; Durán-Quesada et al.,
2020; Hidalgo et al., 2015; Sáenz & Amador,
2016). In addition, they transport moisture
from the Caribbean Sea (CS) to the Eastern
Tropical Pacific (ETPac) (Durán-Quesada et
al., 2010; Durán-Quesada et al., 2017). Much of
this moisture is used by different precipitation-
producing mechanisms, associated with rainfall
in this slope (e.g. Hidalgo et al., 2015).
Several studies like Alfaro et al. (2024),
Garro-Molina et al. (2023), Kaufmann &
Thompson (2005) and Orozco-Montoya &
Penalba (2023) describe the annual cycle of
precipitation and air surface temperature in the
Caribbean slope of southern CA. Precipitation
climatology shows two maxima, one in Novem-
ber and another secondary one in July, with
an absolute minimum value during the month
of March. This minimum value in March is
explained by the little convergence of humidity
observed in the region between the months of
January and May (Alfaro, 2002). The rainiest
months are November and December. During
these months, the increase in the magnitude of
the trade wind over the region begins (Alfaro
et al., 2018; Amador et al., 2016), as well as
the incursion of cold fronts into the Caribbean
Sea also (Amador et al., 2006; Chinchilla et al.,
2016; Chinchilla et al., 2017; Zárate-Hernández
et al., 2013), which favors the occurrence of
rains and extreme events in the Caribbean
slope. The month of July presents a secondary
maximum, in accordance with a maximum in
the convergence of humidity in the Caribbean
Sea (Alfaro, 2002) and the absolute maximum
of the Caribbean Low-Level Jet (see for example
Amador, 2008; Maldonado et al., 2018; Ugalde,
2022), which favors the mechanism proposed
by Hidalgo et al. (2015). This mechanism is a
precipitation source over the Central American
Caribbean coast. A secondary minimum is also
observed during the months of September and
October (SO). During these months the mag-
nitude of the trade wind is minimal (Alfaro,
2002; Alfaro et al., 2018; Amador et al., 2016;
Sáenz & Amador, 2016; Taylor & Alfaro, 2005)
which does not favor the transport of moisture
towards the Central American isthmus (Durán-
Quesada et al., 2010; Durán-Quesada et al.,
2017), also observing the occurrence of equa-
torial westerlies (trades of the southern hemi-
sphere) with a southwest component over the
ETPac. During this two-month period of SO is
also when the highest probability of occurrence
of tropical cyclones occurs in the CS, near CA
(Alfaro & Quesada, 2010; Alfaro et al., 2010),
favoring the occurrence of synoptic westerlies
over the region, due to the induced circulation
of the ETPac towards the CS associated with
the low pressures characteristics of these types
of systems (Hidalgo et al., 2020; Hidalgo et
al., 2022; Peña & Douglas, 2002). It should be
noted that this particular atmospheric configu-
ration does not favor rains in the CS, especially
in the southern part of the isthmus. The circu-
lation can also be induced by the position of
some tropical cyclones in the ETPac (Hidalgo et
al., 2020; Hidalgo et al., 2022), with September
being the month with the highest occurrence of
named tropical cyclones in the ETPac (Amador
et al., 2016).
The monthly average air surface tempera-
ture presents also two maxima in the months of
May and September, in which the intensity of
the trade winds is weaker (Alfaro et al., 2018;
Amador et al., 2016); therefore, since there is
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not much moisture transported towards the CS
(Durán-Quesada et al., 2010; Durán-Quesada
et al., 2017), there are fewer clouds and more
solar radiation (Alfaro, 2002), which increases
the surface air temperature. The lowest aver-
age monthly air surface temperature occurs in
January, during the boreal winter, the rainiest
season in which there is also the peak of cold
front occurrences in the CS (Amador et al.,
2006; Chinchilla et al., 2016; Chinchilla et al.,
2017; Zárate-Hernández et al. 2013).
Central American climate presents north-
south and east-west contrasts; high spatial
resolution scenarios are important to guide
local decision maker processes, suggesting that
local risk management and planning strate-
gies should be designed with a highly spe-
cific approach to each location or region. This
implies that, even in geographically nearby
areas, the measures taken may not necessarily
be transferable due to differences in local cli-
mate projections (Hidalgo et al., 2023).
To analyze these variations, the objec-
tive of this work is to study the observed and
projected latitudinal gradients for precipitation
and temperature in three Southern Caribbean
locations of CA: Bluefields (Nicaragua), Limón
(Costa Rica) and Bocas de Toro (Panamá). The
idea is to characterize their future changes and
determine if there are differences or similarities
in a north-south direction.
MATERIALS AND METHODS
Data: Monthly precipitation (P) and tem-
perature (T) data from General Circulation
Models (GCMs) from 1979 to 2099, shown in
Table 1, were downloaded from the Weather
Research & Forecasting model data reposi-
tory. (WRF, https://esgf-node.llnl.gov/projects/
cmip6/) to perform a statistical downscaling
process and thus bring them to a resolution of
1 x 1 km. These models are part of the latest
generation of the Coupled Model Intercom-
parison Project-Phase 6 (CMIP6; Balaji et al.,
2018; Eyring et al., 2016) used by the Intergov-
ernmental Panel on Climate Change (IPCC).
Models were selected from the repository that
proved to have less P and T biases in the region
according to Almazroui et al. (2021). With
respect to this, the biases in precipitation and
temperature of different models with respect to
several observed datasets are shown. The selec-
tion of a subset of the best 9 models from a total
of 31 is first based in a bias analysis consider-
ing a threshold of 1.5 standard deviations (± 1.5
STD) about the multimodel mean bias for the
historical period. Models with bias exceeding
the threshold do not pass this first test and are
not candidates for selection in the subset set of
best models (Almazroui et al. 2017a; Almaz-
roui et al., 2017b). The second step consists
of testing the pattern correlation coefficient
Table 1
General Circulation climate models (GCMs) were used in the study.
Data Ye ar s Spatial Resolution
ERA5 1979–2014 0.25° x 0.25°
WorldClim 1979–2014 0.00833° x 0.00833°
ACCESS 1979–2099 1.875° x 1.25°
AWI 1979–2099 0.9375° x 0.9375°
CAMS 1979–2099 1.25° x 1.12149°
EC - EARTH3 1979–2099 0.703125° x 0.703125°
EC - EARTH3-Veg 1979–2099 0.703125° x 0.703125°
MP1 1979–2099 0.9375° x 0.9350616°
GFDL 1979–2099 0.703125° x 0.703125°
UKESM1 1979–2099 1.875° x 1.25°
The first two products (ERA5 and WorldClim) correspond to monthly data that were used for the statistical downscaling
change procedure and the rest to the GCMs.
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(PCC) and the root mean square error (RMSE)
for annual mean temperature and precipitation
for each candidate model and the observations.
For temperature (precipitation), models with
an RMSE of less than 2 °C (1 mm day-1) and
a pattern correlation above 0.96 (0.60) are con-
sidered for selection. Models that satisfy both
criteria are then added to the set of selected
models (Almazroui et al., 2021).
The data necessary for bias correction were
obtained from the ERA5 reanalysis database
(Hersbach et al., 2020) through the Coper-
nicus Climate Change Service (Copernicus
Climate Change Service, 2018), corresponding
to the fifth generation of the climate model
and weather from the European Center for
Medium-Range Weather Forecasts (ECMWF).
Monthly precipitation (P) and temperature (T)
data were downloaded from 1979 to 2014 at a
spatial resolution of 0.25o x 0.25º (hereinafter
considered the “coarse scale”). The coarse scale
domain corresponds to that shown in Fig. 1.
Monthly P and T climatologies at 1 x 1
km spatial resolution and at global scales were
obtained from WorldClim data (Fick & Hij-
mans, 2017) in http://ccafs-climate.org. A mask
was produced for the region under study (com-
munities of Bluefields, Limón and Bocas del
Toro, Southern Caribbean region of Central
America) and climatologies were sliced using
the geographic information system software
ArcGIS Pro under a license of the University of
Costa Rica. These data are used in the process
of downscaling and for determining the clima-
tologies of the sites of interest.
Downscaling: The method used for down-
scaling is that of Navarro-Racines et al. (2020)
with the addition of bias correction according
to the following methodology:
a. The data from all the models shown in
Table 1 (with the exception of the Worl-
dClim data) were interpolated to the
Fig. 1. Location of the three communities considered: A) Bluefields, Nicaragua, B) Limón, Costa Rica and C) Bocas de Toro,
Panama.
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resolution of the ERA5 reanalysis to per-
form the bias correction that consists of
imposing the average and standard devia-
tion of the historical period (1979–2014,
baseline) from the reanalysis to the stan-
dardized data of the models.
b. The 1 x 1 km climatologies of the World-
Clim data are used as a basis for using the
delta method of bias correction and downs-
caling. The procedure consists of expres-
sing the percentiles of each of the grid
observations in terms of anomalies with
respect to their respective annual averages.
In other words, for precipitation [1a]:
For temperature [1b]:
Where ΔXi is equal to the anomaly of the
monthly precipitation or temperature data from
the climate models calculated for grid point i,
ΔXFi is the monthly precipitation or tempera-
ture value of the model and ΔXCi the climato-
logical value of that location. The anomaly
map of all stations is then interpolated into the
WorldClim monthly climatology grids, and
that respective climatology for each month is
added, as follows, for precipitation [2a]:
For temperature [2b]:
Where XDFi is equal to the value of the
scaled data and which forms a point on the
high-resolution mesh (1 x 1 km) for calculat-
ing the threat. XOBSi is the value of the weather
at the grid point i obtained from WorldClim,
ΔXIi is the anomaly (fractional for P and addi-
tive for T) interpolated and corrected by biases
mentioned above. The extreme indices were
then calculated as in the following subsections.
In summary, the coarse resolution P and T data
were downscaled, according to the procedure
described in this section, to the fine resolution,
corresponding to the 1 x 1 km locations of
WorldClim grid data. This methodology was
evaluated and used by Mendez et al. (2020)
in their study for Costa Rica and showed
in many cases better results than other bias
correction techniques.
The hazard maps represented by the
changes in P and T, were constructed consid-
ering the climate change maps that represent
the ensemble of eight individual models shown
in Table 1, based on the historical scenario
(1979–2014) compared to the climate for three
different future time horizons, namely: near
(2020–2030), medium (2040–2060) and far
(2079–2099). These horizons were made for the
greenhouse gas concentration scenarios SSP1–
2.6 (optimistic scenario), SSP2–4.5 (medium
scenario) and SSP5–8.5 (pessimistic scenario).
The geovisualization of spatial information
shows the information generated in the cal-
culations carried out above. For this purpose,
we follow the same procedure of Hidalgo et al.
(2023) and several thematic maps were gener-
ated that would allow the different variables to
be synthesized and be able to perceive spatial
relationships or connections visually (Slocum
et al., 2023).
RESULTS
Baseline and scenarios generated
Precipitation: Fig. 2 shows the annual
averages of precipitation in Bluefields, Limon
and Bocas del Toro from the ensemble of eight
GCMs used. In Bluefields the map shows more
precipitation in the southern region, in Limón
the highest precipitation is located in the north-
ern section, and in Bocas del Toro the rainiest
region is near the mountains.
Fig. 3, Fig. 4 and Fig. 5 show the changes
projected in precipitation by the ensemble of
the models for different scenarios and time
horizons in Bluefields, Limon and Bocas del
Toro respectively. To read these figures, the
columns show the horizons of the projections:
2020–2030 (near horizon), 2040–2060 (middle
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Fig. 2. Historical scenario (1979–2014) showing the ensemble of eight General Circulation climate models for precipitation
(mm year-1) in A) Bluefields, B) Limón and C) Bocas del Toro.
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horizon) and 2079–2099 (end of the century
horizon); and in the rows the different scenarios
with respect to their possible radiative forcings
at the end of the century associated with socio-
economic development scenarios: SSP1–2.6
(optimist scenario), SSP2–4.5 (intermediate
scenario), SSP5–8.5 (pessimistic scenario). So,
as you can observe in Fig. 3 for Bluefields, the
first scenario shows a significant increase in
precipitation of around 30 %, in the second sce-
nario in the last period there is a decrease of 10
% of precipitation compared to historical. And
Fig. 3. Precipitation change percentage of SSP scenarios in relation to historical (1979–2014) for different time horizons in
Bluefields.
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in the last scenario in the 2079–2099 period the
decrease is around 30 % in the whole region
of study.
In the case of Limón (Fig. 4) the spatial
pattern is similar between scenarios and peri-
ods, but here the decrease of precipitation at the
end of the century for the pessimistic scenario
is around 40 % in a region that is currently
dedicated to banana crops.
In the case of Bocas del Toro (Fig. 5) the
maximum reduction in precipitation is found
for the pessimistic scenario at the end of the
century horizon. The maximum reduction is
about 31 % of the precipitation for the historical
scenario. In the case of this subregion, the opti-
mist and medium scenarios show combinations
of precipitation increases and decreases, while
the pessimistic show widespread reductions at
Fig. 4. Precipitation change percentage of SSP scenarios in relation to historical (1979–2014) for different time horizons in
Limon.
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the end of the century. This result is consistent
with the region estimates of Almazroui et al.
(2021), where the pessimistic scenario is very
dry at the end of the century compared to the
other scenarios.
Note that in many cases for all scenarios
and subregions there is a general increase in
precipitation during mid-century, while gener-
ally, it is in the second half of the 21st century
that dry conditions are projected (especially for
the SSP5–8.5 pessimistic scenario).
Air near-surface temperature: Fig. 6 shows
the annual averages of air near-surface temper-
ature in Bluefields, Limon and Bocas del Toro.
As expected, topography controls the tempera-
ture means, with higher altitudes reporting the
lower temperatures and vice versa.
Fig. 7, Fig. 8 and Fig. 9 show the changes
projected in air surface temperature by the
ensemble of the models for different scenarios
and time horizons in Bluefields, Limon and
Bocas del Toro respectively. For the three
regions, a spatial pattern can be recognized, the
temperature increases almost uniformly from
horizon to horizon and between scenarios, but
the critical one is at the end of the century in
the SSP5–8.5 where the increase is about 2.7
°C or higher. This is lower than the maximum
warming that is projected to occur in other
more arid regions of Central America, where
temperatures are expected to increase by as
much as 4 oC at the end of the century in the
pessimistic scenario (Hidalgo et al., 2017;
Hidalgo et al., 2023)
DISCUSSION
This work presents high spatial resolu-
tion climate change scenarios, 1 x 1 km, with
a historical baseline from 1979 to 2014 and
with projections up to 2099. These scenarios
were generated from state-of-the-art models
of the AR6 of the Intergovernmental Panel on
Climate Change or IPCC. The results showed
that some regions would go from very humid
to humid, based on strong reductions in pre-
cipitation of 30 % and warming at the end of
the 21st century. This expected increased in
the aridity, is going to have impacts on ecology
Fig. 5. Precipitation change percentage of SSP scenarios in relation to historical (1979–2014) for different time horizons in
Bocas del Toro.
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Fig. 6. Historical scenario (1979–2014) showing the ensemble of eight General Circulation climate models for air surface
temperature (°C) in A) Bluefields, B) Limon and C) Bocas del Toro.
12 Revista de Biología Tropical, ISSN: 2215-2075 Vol. 73(S1): e64044, enero-diciembre 2025 (Publicado Mar. 03, 2025)
Fig. 7. Air surface temperature change of SSP scenarios in relation to historical (1979–2014) for different time horizons in
Bluefields.
and ecosystem services (Moreno et al., 2019),
agriculture (banana plantations for example,
Orozco-Montoya, 2023), human consumption
due to a water availability reduction per capita
(Castellanos et al., 2022) and hydroelectric
generation, which is based mainly on the Carib-
bean slope. Increases in precipitation in the
near future can also have impacts on those
sectors and also cause floods that are already
frequent in the Caribbean slope (Pérez-Briceño
et al., 2016).
In addition to the climate change impacts
mentioned above, it is also observed and pro-
jected for Central American coastal regions, an
increase in ocean acidity, sea level and marine
heat waves (Arias et al., 2021; Castellanos et
al., 2022). These put mangroves and coral reef
ecosystems at risk, due to bleaching events;
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Fig. 8. Air surface temperature change of SSP scenarios in relation to historical (1979–2014) for different time horizons in
Limon.
and coastal socio-ecological systems at risk
due also to the observed and projected increase
in coastal flood and erosion (e.g. Lizano &
Pérez-Briceño, 2021). Arias et al. (2021) men-
tion also a slight increase in the annual tropical
cyclone occurrences around Central Ameri-
ca, this could be associated with more direct
and indirect impacts (Peña & Douglas, 2012;
Quesada-Román & Campos-Durán, 2023;
Quesada-Román et al., 2024) due to extreme
precipitation (Hidalgo et al., 2020; Hidalgo
et al., 2022) and strong wind events (Pérez-
Briceño et al., 2016), among the storm surges
associated with the cyclone landings. There
is evidence that all these impacts are already
affecting populations in poverty and their live-
lihoods in the region (Castellanos et al., 2022).
According to Hidalgo et al. (2023), genera-
tion of high spatial climate change scenarios is
necessary because Central America is a region
14 Revista de Biología Tropical, ISSN: 2215-2075 Vol. 73(S1): e64044, enero-diciembre 2025 (Publicado Mar. 03, 2025)
characterized by significant topographic com-
plexity, land use intensity 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 geographi-
cally close areas, the measures taken may not
necessarily be transferable due to differences in
climate projections, as the results show for three
nearby communities in the Southern Central
American Caribbean coastal region.
Cavazos et al. (2024), Ley et al. (2023) and
Quesada-Román (2023) mention that there
is a great need in Central America to create
public policies that address the growing vulner-
ability of the region and the number of critical
ecosystems at risk, as well as mechanisms that
ensure their transparency and effectiveness.
Given the high vulnerability to climate impacts
and the level of emissions in the region, there
is a clear need for a greater focus on adapta-
tion responses, within existing public policy
instruments. The results presented here are
a subset of what needs to happen to help the
region become more proactive in collecting the
necessary data, closing the adaptation gap, and
moving toward the transformations that Cen-
tral America needs to achieve climate-resilient
development, since IPCC reports important
gaps in the Central American production of
scientific literature. On the other hand, action
on adaptation also requires urgent attention to
the fact that the climate surface and aerological
monitoring network through weather stations
in the region is declining. So, there are signifi-
cant opportunities to strengthen collaboration
in these areas between academic institutions
and government agencies responsible for sys-
tematic Earth observation and meteorological
and hydrological monitoring.
Ethical statement: the authors declare that
they all agree with this publication and made
significant contributions; that there is no con-
flict of interest of any kind; and that we fol-
lowed all pertinent ethical and legal procedures
and requirements. All financial sources are fully
Fig. 9. Air surface temperature change of SSP scenarios in relation to historical (1979–2014) for different time horizons in
Bocas del Toro.
15
Revista de Biología Tropical, ISSN: 2215-2075, Vol. 73(S1): e64044, enero-diciembre 2025 (Publicado Mar. 03, 2025)
and clearly stated in the acknowledgments sec-
tion. A signed document has been filed in the
journal archives.
ACKNOWLEDGEMENTS
Authors wish to acknowledge the funding
of this research through the following Vicer-
rectoría de Investigación, Universidad de Costa
Rica grants: B9454 (supported by Fondo de
Grupos), A4906 (PESCTMA), C4226 (Eco-
Salud), B0-810 and C2103. All the authors
acknowledge the funding of UCREA project
C3991 (UCREA) and were partially support-
ed by a grant awarded by the International
Development Research Centre (IDRC), Otta-
wa, Canada, and the Central American Uni-
versity Council (CSUCA-SICA) to the Red
Centroamericana de Ciencias sobre Cambio
Climático (RC4) project (CR-66, C4468, SIA
0054-2, the opinions expressed here do not nec-
essarily represent those of IDRC, CSUCA, or
the Board of Governors). To the UCR research
center CIGEFI for their logistic support during
the data compilation and analysis. EA and HH
thank the UCR School of Physics for giving us
the research time to develop this study. HH
was on sabbatical license during part of the
production of this article. A special thanks to
the student Daniela Amador who collaborated
with the preparation of the cartography and
Marco Acosta Quesada who collaborated with
the figure’s final version.
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