Atmospheric Circulation Types Controlling Rainfall in the Central American Isthmus
International Journal of Climatology, 2022
Rainfall mechanisms in the Central American Isthmus are controlled by complex physical interactio... more 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 6 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 analyzed 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 on-set, demise, and duration of rainy seasons. Hence, these results can inform process-based
model diagnostics aiming at bias-correcting climate predictions at multiple timescales.
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Papers by Eric Alfaro
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 6 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 analyzed 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 on-set, demise, and duration of rainy seasons. Hence, these results can inform process-based
model diagnostics aiming at bias-correcting climate predictions at multiple timescales.
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 6 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 analyzed 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 on-set, demise, and duration of rainy seasons. Hence, these results can inform process-based
model diagnostics aiming at bias-correcting climate predictions at multiple timescales.