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<= span lang=3DES>https://doi.org/10.37815/rte.v35n2.1054

<= span lang=3DES>Artículos originales

 

Metodología pa= ra localizar la Zona de Convergencia<= span style=3D'letter-spacing:-.3pt'> Intertropical usando velocidad de vi= ento

Methodology to locate the Intertropical Convergence Zone using wind velocity

Jorge Bravo1 https://orcid.or= g/0009-0006-0466-9328, Jesus Portilla1 https://orcid.or<= /span>g/0000-0003-4985-4325 1Escuela Politéc= nica Nacional, Quito, Ecuador

jdbravome@gmail.com, jesus.portilla@epn.ed= u.ec

 =


 =

=

Esta obra está b= ajo una licencia internacional Crea= tive Commons Atribución-NoComercial 4.0

.


Enviado: 2023/07/15 Aceptado: 2023= /08/22 Publicado: 2023/10/15


 


Resumen

La local= ización de la Zona de Convergencia Intertropical = (ITCZ) se puede establecer en funci&oacut= e;n de la convergencia de corrientes de vientos alisios provenientes del hemisferio norte y sur. Esta zona tiene una gran relevancia climática pues inci= de directamente en la distribución de precipitaciones en las regiones ecuatoriales y tropicales a escala global. En este trabajo se desarrolla una metodología para localizar la ITCZ, empleando un algoritmo de convergencia en el campo vectorial de velocidades de viento obtenido del mo= delo de reanálisis atmosférico ERA Interim. Esta metodología resulta ú= nica, pues parte de un concepto robusto de análisis de gradientes y<= span style=3D'letter-spacing:-.5pt'> consigue un rango de la localización de la ITCZ en períodos de tiempo registrados históricamente, l= o cual no se ha encontrado en estudios previos. La metodología consiste en = una serie de algoritmos individuales que permiten limpiar, segmentar y depurar = los datos de convergencia hasta obtener mapas que simplifican su visualizaci= ón empleando MATLAB®. S= e obtienen como resultado rangos estadísticos de la localización de la ITCZ en cada mes de 41 años de datos disponibles, patrones de desplazamiento meridional, y también mapas de contraste entre la localización mensual acumulada y la localización en meses de años en los que se manifestó El Niño Oscilación del Sur (ENSO) para mostrar una posible correlació= n.

 

Palabras <= /span>clave: vientos alisios, ENSO, ITCZ, dinámica atmosférica, reanálisis.<= /p>

 

 

 


<= /o:p>


 

Abstract

The locat= ion of the Intertropical Convergence Zone= (ITCZ) can be established as a function of convergence between<= span style=3D'letter-spacing:-.15pt'> tropical trade winds from the Northern and Southern Hemispheres. This zone has<= span style=3D'letter-spacing:-.75pt'> a great impact on the weather since<= span style=3D'letter-spacing:-.75pt'> it directly affects the distribution of precipitation in equatorial and tropical regions on a global scale. This document elaborates on the development of a methodology to locate the ITCZ using a convergence algorithm in the vector field of wind velocity obtained from<= span style=3D'letter-spacing:-.65pt'> the ERA Interim reanalysis model. This methodology is unique because it relies on a robust conce= pt of gradient analysis and achieves a location range for ITCZ in defined periods of time registered historically, which has not been developed in previous studies. The methodology consists of a series of individual algorithms that a= llow for the cleaning, segmentation, and debugging of convergence data, until simplified maps for visualization are obtained using MATLAB®. The results are statistical ranges of the location of the ITCZ in each month of the 41 years of data available, meridional shift patterns, and contrast maps between cumulative monthly location and the location on month= s of years where El Niño Southern Oscillation (ENSO) was present to show possible correlation.

 

Keywords: trade winds, ENSO, ITCZ, atmospheric dynamics, reanalysis.

 

 

Introducción

Muchas características del = clima tropical son manifestaciones de la convergencia dinámica <= span style=3D'letter-spacing:-.1pt'>y termodinámica de los hemisferios norte y sur. Esto incluye el afloramiento ecuatorial (proceso por el cual agua fría profunda y rica en nutrientes asciende a la superficie), co= nvección troposférica profunda (fenómeno térmico que permite el movimiento y mezcla de paquetes de aire a diferentes alturas de la atmósfera), una estructura compleja de corrientes oceánicas y uno de los esquemas de viento más estables del planeta, como lo son los vientos alisios (Waliser & Gautier, 1993). De todos los fenómenos que caracterizan a la región tropical, la característica que representa de forma más clara la intersección de los dos hemisferios es la Zona de Convergencia Intertropical, ITCZ por sus siglas en inglés, cuyo análisis es fundamental para entender la interacción entre la temperatura superficial del<= span style=3D'letter-spacing:-.6pt'> océano, precipitación, <= /span>y presión a nivel del mar. La característica principal de la ITCZ es el alto contenido de vapor= de agua en el aire, el cual al ll= egar a una mayor altura se enfría y provoca intensas lluvias, liberando humedad acumulada (Wallace & Hobbs, 2006).

 

La<= /span> diferencia en presió= ;n atmosférica, generada por la variación de temperatura <= /span>al receptar radiación solar en las diferentes latitudes del planeta, da lugar al movimiento de corrientes de aire (Marshall & Plumb, 2008). Las corrientes de aire se conocen como ‘vientos’ y siempre se desplazan desde una zona de presión alta a una zona de presión baja, y mientras mayor sea la diferencia de presiones, más fuerte será la velocidad del viento. Diferentes regiones de la superficie terrestre reciben difere= ntes cantidades de radiaci&oacu= te;n, esto debido a la diferencia en el á= ngulo de incidencia, pero también al movimiento rotacional y estacional del planeta, variadas reflectividades de las superficies propias de las regiones y diversos factores de contaminación= . De esta manera, la temperatura en distintas latitudes también varía. La región polar se caracteriza por ser una zona de baja temperatura y alta presión, mientras que la región ecuatorial se muestra com= o zona de alta temperatura y baja presión (Barry, 2003).

 

La diferencia de presión atmosférica y temperaturas produce una serie de patrones globales de circulación, los cuales componen el modelo de circulación atmosférica global, en el cual se establecen los ciclos de circulación como celdas de convección atmosférica. Existen tres celdas de convección atmosférica en cada hemisferio: Hadley, Ferrel y Polar (Barry, 2003).


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En la Figura 1 se puede observar= un esquema simplificado de la disposición de estas celdas en el planeta y el esquema de flujo de viento en la troposfera. Este estudio se enfoca en la región de la celda de convección atmosférica Hadley, = que se caracteriza porque el aire cálido de la zona ecuatorial se eleva, creando un cinturón de baja presión, y mientras el aire se eleva va perdiendo calor, lo que resulta en condensación y da origen a las nubes y lluvia que se distribuyen al= norte y sur de la líne= a ecuatorial. En las latitudes 30ºN y 30ºS, el aire frío y seco desciende, creando una región de alta presión que se caracteriza por ser despejada, y cuando alcanza la superficie de la tierra,= se mueve hacia el trópico o hacia los polos (Barry, 2003). Por este mot= ivo, en la zona ecuatorial hay largas cadenas de viento que transportan calor y humedad y dan lugar a la ITCZ, que es una f= ranja compuesta por nubes de convección profunda ubicadas en una gran cade= na longitudinal (Schneider et al., 2014).

 

Figura 1

Esquema de las celdas de convección atmosférica


 

Encontrar la localización d= e la ITCZ y sus propiedades migratorias usando datos de viento ha representado un desafío en el pasado por varias razones. Primero, las observaciones = directas de la capa límite de vientos tropicales han sido escasas. Adicionalmente, las predicciones de rango corto, que usualmente proveen los datos para el anál= isis en predicción numérica del clima, han sido tradicionalmente m= enos exitosas en los trópicos que en las altitudes medias debido a la complejidad de la circulación tropical. Finalmente, las restriccione= s de balance aplicadas en la asimilación de datos presentaban poca información de viento del campo total de datos en los trópicos (Žagar et al., 2005). Para el estudio de la dinámica y climatología del flujo superficial en las áreas ecuatoriales,= se han empleado datos de embarcaciones, sensores superficiales y sensores de altura; posteriormente se desarrollaron los reanálisis de datos y en años recientes se han tenido observaciones continuas de vectores de viento en la superficie oceánica gracias a dispersómetros basados en espacio, particularmente el escater&oacut= e;metro (Žagar et al., 2011). Los análisis y pronósticos en los trópicos han mejorado significativamente en los últimos años, esto gracias a que se han desarrollado nuevas metodologías, más completas, que cuentan con la implementación de modelos variacionales para asimilaci= ón de datos, como los filtros de ensamble de Kalman, el 3D-Var y 4D-Var , los mismos que permiten mejorar las condiciones<= span style=3D'letter-spacing:-.1pt'> iniciales de pronóstico y su ajuste respecto al tiempo (Daescu, 2008; Houtekamer & Mitchell, 1998).

 

En este trabajo se propone una metodología que permita determinar la ubicación de la ITCZ mediante el análisis de los datos de velocidad del viento proporcion= ados por el Centro Europeo de Predicción del Clima de Rango Medio, ECMWF por sus siglas en inglés. Su archivo de reanálisis, ERA Interim, ha sido depurado de errores y presenta una mejor asimilación de


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datos me= diante el uso de nuevos parámetros para facilitar= su procesamiento (Dee et al., 2011). Aportando con la metodología para ubicar la ITCZ se busca conseguir = un contraste a estudios previos de su localización, bifurcaciones y zon= as dobles de convergencia, y su variabilidad respecto al tiempo.

 

El uso acertado de la predicci&oac= ute;n meteorológica y datos climáticos permite una mejor planificación para varios sectores que aprovechan el océano p= ara actividades económicas y permite establecer medidas preventivas cont= ra fenómenos naturales como El Niño-Oscilación del Sur (ENSO) (Adams et al., 1995). En estudios anteriores se ha realizado un análisis histórico en el cual se observa que el desplazamiento hacia el norte de la ITCZ se ve acompañado de gradientes zonales de temperatura que responden a la presencia de ENSO (Koutavas et al., 2006), adicionalmente se ha observado que una de las zo= nas de mayor dificultad para la predicción de ENSO es la zona ecuatorial= del Océano Pacífico este, una zona donde la ITCZ tiende a present= ar bifurcaciones y variabilidad anómala (Mu et al., 2014; Sulca et al., 2018). Aunque es usual que el estudio de ENSO esté relacionado con u= na variación de la relación permanente que existe entre la intensidad de los vientos alisios y la temperatura superficial del océano zonal (McPhaden et= al., 2006), es posible que la localización de la ITCZ presente anomalías que permitan identificar la presencia de condiciones de ENSO o brinden un parámetro adicional para mejorar la calidad predictiva de este fenómeno= .

 

 

Datos y Métodos

Los= datos <= span lang=3DES style=3D'letter-spacing:-.1pt'>empleados en este estudio permiten obtener información completa y verificada de las condiciones atmosfér= icas, oceanográficas y climáticas a escala global. Los modelos de reanálisis atmosf&eacut= e;rico consisten en una combinación de observacion= es y medidas, con modelos físicos de predicción meteorológica. De esta manera, se obtiene como resultado un conjunto global de datos con mayor consistencia y menos vacíos en el tiempo. La mayor ventaja de este método es que emplea todas las variables posibles para<= span style=3D'letter-spacing:-.75pt'> construir un mapa global de datos, y con ello se consigue increme= ntar tanto la resolución como la calidad de las predicciones (Be= rrisford et al., 2011).

 

Para realizar un modelo de reanálisis se requiere establecer condiciones in= iciales de los parámetros a evaluar y formular un modelo físico que tome en consideración el comportamiento de las variables atmosfé= ricas en el tiempo. Las condiciones iniciales se obtienen de observaciones y mediciones de distintos instrumentos distribuidos en el Sistema Global de Observaciones, y se combinan con predicciones meteorológicas de corto rango. Esto permite ajustar los modelos predictivos luego de verificar sus resultados con el clima real (Berrisford et al., 2011). En este proceso se emplean métod= os de asimilación de datos variacionales que permiten obtener una estructu= ra de datos dinámica, que elimina errores de predicción y retroalimenta constantemente la base de datos inic= ial para mejorar la calidad de los resultados (ECMWF, 2017).

 

Actualmente se usan los datos de velocidad de viento a una altura de 10m, los mismos que se extraen de la base del ECMWF y tienen el respaldo de haber sido empleados en diversos proyectos para monitorear el cambio climático, planteamiento de decisiones estratégicas en indu= stria y gobiernos, y propuestas de desarrollo de energía renovable (ECMWF, 2017). En este proyecto se usaron principalmente datos del modelo ERA Interim, que tienen una resolución espacial de 79 km, con intervalos temporales de 6 horas. Adicionalmente se plantea el uso futuro del modelo ERA 5, el cual tiene una resolución de 31km e intervalos temporales de 3 horas y además presenta mejoras significativas en calidad de los datos gracias a los avances en el método de reanálisis empleado por el ECMWF.


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La metodología combina el análisis matemático de datos con herramientas de visualización <= /span>computacional y estadística para estimar la localización de la ITCZ en diferentes rangos de tiempo. Para el= lo se crean algoritmos independientes, funciones y herramientas visuales, y se emplea como herramienta computacional el programa MATLAB®. La metodología consta de 3 pasos: el algoritmo de convergencia del viento, la agrupación y depuración de datos de convergencia, y el análisis estadístico de la localización de la ITCZ.

 

Diferentes proyectos meteor= ológicos, ambientales y energét= icos han hecho uso de la ciencia de datos en combinación con modelos numéricos y da= tos satelitales para obtener resultados estadísticos (Gagne, 2016; García-Rojo, 2004).

 

Algoritmo de convergencia de viento

Para desarrollar el algoritmo de convergencia se consideran las características físicas que constituyen a la ITCZ, las cuales indican que es el resultado de la convergencia entre corrientes = de viento alisios, opuestos en sentido meridional, debido a la diferencia de presión en diferentes latitudes. Esto permi= te aprovechar los datos de viento (velocidad a 10m de altitud), que se observan graficados en la Figura 2, y son provistos por los modelos de reanál= isis para detectar esta convergencia en la inversión del sentido de flujo= del viento. Esta inversión se identifica a través de un algoritmo= de gradiente de imagen, el cual de forma simplificada, encuentra el cambio de sentido en cada celda de la malla donde se ubica el campo vectorial de velocidades = (Mlsna & Rodriguez, 2009), y se aplica de manera bidireccional para evitar sesgos o desplazamientos de los resultados. Adicionalmente se emplea un criterio de ponderación, considerando que la velocidad del viento sea baja, sobre todo en su compone= nte meridional, pues esta es otra característica de la ITCZ (Back & Bretherton, 2005).

 

Figura 2

Campo vectorial de velocidades de viento a 10m de altitud

 

90

 

 

 

 

 =

60

 

 

 

 

 

30

 

 

 

 

 

3D"Cuadro0

 

 

 

 

 

-30

 

 

 

 

 

-60

 


 

 

 =

-90


 

 

 =

<= span lang=3DES style=3D'font-size:3.5pt;mso-bidi-font-size:11.0pt;font-family:"A= rial",sans-serif; mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Rom= an"; letter-spacing:-.5pt;mso-font-width:110%'>0        &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;       45        &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;    90


 

 

 =

135        &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p; 180

Longitude


 

 

 =

225        &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p; 270        &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p; 315        &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p; 360


 =

Por lo tanto,= el algoritmo considera 3 condiciones para identificar puntos de convergencia:

 

      =    Rapidez de viento menor a 10m/s.

      =    Cambio de direcci&oacut= e;n en la velocidad, representado por= la inversión del sentido entre celdas adyacentes de la malla gaussiana.


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El resultado del algoritmo es una = matriz de puntos de convergencia, como se puede observar en la Figura 3. Estos puntos serán el punto de partida para la agrupación y depuraci&oacut= e;n de datos.

 

Figura 3

Puntos de convergencia resultantes del= algoritmo

 

 

 

30°N=

 

 

 

 

 

 

 

 =

3D"Cuadro 

 

 

 

 

 

 

 =

30°S=

 

 

 


 

 

 =

120°E

 

 =

150°E

 

 =

180°E

 

 =

210°E

 

 =

240°ELongitude


 =

Agrupac= ión y depuración de datos de convergencia

La agrupación de datos consiste en reducir la redundancia evitando perder la tendencia de los puntos de converge= ncia; es decir, se busca tener menos puntos que puedan mantener la misma información sobre la localización de la ITCZ.

 

Para este propósito se crea= un algoritmo de agrupación que busca centroides para un grupo de puntos, que deben encontrarse dentro de una distancia máxima. La distancia máxima se calcula dentro del algoritmo en función de la distribución normal de la distancia entre dos puntos para todos los datos de convergencia. Se realizan varias iteraciones de esta agrupación, hasta que no existan puntos que se encuentren dentro de la distancia máxima, y que puedan agruparse. Dejando como resultado un esquema de puntos completamente equivalente al esquema inicial, pero con una cantidad reducida de datos, como puede observa= rse en la siguiente Figura 4.

 

Figura 4

Resultado = de algoritmo de agrupación de puntos de convergencia

30°N 

 

 

 

 =

20°N=

 

 

 

 

 =

10°N=

 

 

 

 

 =

3D"Cuadro 

 

 

 

10°S=

 

 

 

 

 =

20°S=

 

 

 

 

 =

30°S=


 =

180°E

195°E

210°E

240°E

Longitude


255°E

 

Pos= teriormente se realiza una segmentación de los datos, considerando una línea = de base. Para identificar la línea de base se preseleccionan las líneas que se encuentran en el Pacífico oriental con mayor cercanía al continente americano. Esto debido a que existe una gran influencia de la región m= ontañosa en los flujos de viento de la región (Xu et al., 2005). Adicionalmente, la bifurcación = de la ITCZ con la Zona de Convergencia del Pací= fico Sur (SPCZ) se manifiesta en un rango de longitudes más distante al continente (Bellucci et al., 2010)(Vincent, 1994). Partir desde una longitud próxima al continente facilita la identificación de la ITCZ en regiones de bifurcación.

 

Finalmente, luego de tener la línea de base como guía, es posible realizar una conexión de líneas adyacentes, siem= pre y cuando cumplan el criterio de unidireccionalidad y en caso de bifurcaciones, se una la línea que tenga mayor proximidad dentro de un rango predefinido bajo el cri= terio de distribución de distancia entre puntos. La línea obtenida = como localización de la ITCZ para cada intervalo de tiempo, no es de gran utilidad por sola, pues únicamente<= span style=3D'letter-spacing:-.75pt'> contiene datos de un tiempo específico, por este motivo se debe agrupar de forma mensual, como se observa en la Figura 5, para poder obtener información relevante usa= ndo estadística.

 

Figura 5

Visualización de líneas= de convergencia agrupadas para un mes

<= !--[if gte vml 1]> 20°N

 

 

 

 =

10°N

 

 

 

 =

3D"Cuadro=

 

 

 

 =

10°S

 

 

 

 

<= span lang=3DES style=3D'font-size:5.0pt;mso-bidi-font-size:11.0pt;font-family:"A= rial",sans-serif; mso-bidi-font-family:"Times New Roman";letter-spacing:-.2pt'>20°S


 =

180°E


195°E


210°E


225°E


240°E&nb= sp;            =      255°E            = ;      270°E            = ;      285°E            = ;      300°E

Longitude


 =

An&aacu= te;lisis estadístico de localizaci&= oacute;n de la ITCZ

Par= a realizar el análi= sis estadístico inicialmente se emplearon diagramas de cajas = con los <= span lang=3DES>datos de convergencia acumulados de forma mensual. De esta forma = se esperaba observar las zonas don= de existe acumulación de puntos en un rango bien definido. Sin embargo, la amplitud de los rangos intercuartílicos y la dispersión de datos por valores atípicos hacen que este análisis no sea tan robusto, como se puede apreciar en la siguiente figura.

 

Figura 6

Diagrama <= /span>de cajas para un mes de datos de convergencia acumulados

 

20°N 

 

 

10°N=

 

 

 =

3D"Cuadro 

 

 =

10°S=

 


 

 =

20°S

 

 

180°E


 

 

195°E

 

 

210°E

 

 

225°E

 

 

 

Longitude


 

 

= 240°E

 

En esta sección se añ= ;ade únicamente un diagrama de caja mensual como ejemplo. Sin embargo, se han obtenido resultados para los 12 meses, de los 41 años= de datos disponibles en el modelo de reanálisis ERA Interim. Para facilitar su acceso digital, los gráficos se encuentran disponibles en la dirección: https://doi.org/10.6084/m9.figshare.19102898.v1.

 

Para contrastar el análisis estadístico de los diagramas de cajas, se aplica otro método,= el cual consiste en obtener un conteo de datos en cada celda de la malla gaussian= a para períodos acumulados de tiempo. De esta manera es posible visualizar dónde e= xiste una gran acumulación de puntos y donde no hay convergencia, como se puede apreciar en la figura 7. Es también una visualización mucho más robusta de la convergencia en diferentes períodos de tiempo, pero a su vez no se consigue distinguir la= ITCZ en zonas de bifurcación= en el Océano Pacífico este. Por lo cual este paso aún se encuentra en desarrollo a fin de pulir los resultados finales.

 

Figura 7

Conteo de datos de convergencia acumulados para= enero por 10 años


 

 

 

 

 

 

 

 

60

 

 

 

 

 

 =

30

 

 

 

 

 

3D"Cuadro0

 

 

 

 

 

-30

 

 

 

 

 

-60


10000

 

 

 =

9000

 

 

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8000

 

 

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7000

 

 

 =

6000

 

 

 =

5000

 

 

 =

4000

 

 

 =

3000

 

 

 =

2000

 

 

 =

1000


 =


 

 =

45        &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;    90


 =

135


 =

180

Longitude


0

= 225        &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p; 270&= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;         315


 

 =

Resultados y Discusión

Mediante la distribución<= span style=3D'letter-spacing:-.75pt'> de los diagramas de cajas se visualiza, en cada mes, el rango de latitudes donde se encuentra la ITCZ, para cada intervalo de longitudes, y además las dimensiones del rango intercuartílico y el rango de límites extern= os resultan útiles como indicadores de la amplitud de la ITCZ. Como se observa en la figura 6, mientras mayor sea el rango intercuartílico (dimensión merid= ional de las cajas azules) y con ello el rango de límites externos, se interpreta = que la amplitud de la ITCZ será mayor en esa región, pues la incertidumbre en su posición es mayor.

 

El uso de una metodología heurística en el desarrollo del estudio permite que los resultados tengan una aproximación distinta en comparación con métodos propuestos por diferentes autores. Aunque se ha planteado anteriormente la relación de la velocidad del viento con la formación de zonas de convergencia, sobre todo estableciendo su relaci&oacut= e;n con la precipitación caracter&= iacute;stica de las mismas, no existía un método de análisis computacional que empleara es= ta variable para localizar la zona de convergencia intertropical (Back & Bretherton, 2005). También es necesario considerar que los modelos de reanálisis brindan datos más precisos y compuestos en comparación con los anteriores esquemas de predicción meteorológica, por lo cual la metodología propuesta plantea criterios robustos para estimar la convergencia y relacionarla con la ubicación de la ITCZ, reduciendo la posibilidad = de una acumulación de errores de cálculo o sesgos en los resulta= dos (Bechtold et al., 2018).


 

Figura 8.

Diagr= amas de caja acumulados para un mismo mes en todos los años con datos disponibles en contraste con a&ntil= de;os con presencia de ENSO

 

20°N

 

 

 =

10°N

 

 

 =

3D"Cuadro=

 

 

 

10°S

 

 

 =

20°S

 

 

 =

20°N

 

 

 =

10°N

 

 

 =

3D"Cuadro=

 

 

 

10°S

 

 

 =

20°S

 

 

 =

20°N

 

 

 =

10°N

 

 

 =

3D"Cuadro=

 

 

 

10°S

 

 

 =

20°S

 

 

 =

20°N

 

 

 =

10°N

 

 

 =

3D"Cuadro=

 

 

 =

10°S

 


 

 =

20°S


 

 

 

180°E


 

 

 

195°E


 

 

 

210°E


 

 

 

225°E&nb= sp;            =       240°E            = ;        255°E            = ;        270°E

Longitud


 

 

 

285°E


 =

Los= diagramas de caja mensuales permiten observar que la distribuci&oa= cute;n de posiciones de la ITCZ<= span style=3D'letter-spacing:-.6pt'> se mantiene mayoritariamente en un rango de latitudes entre 20ºN y 15ºS, y también se nota un desplazamiento hacia el norte de la línea ecuatorial aco= mpañado con una disminución en el rango intercuartílico que se manifiesta de manera paulatina a partir del m= es de abril.


 

Figura 9

Distribución de mediana, rango intercuartílico y de valores extremos.

10

 =

8

 =

6

 =

3D"Cuadro4

 =

2

 =

0

 =


-2

Jan

25


Feb


Mar        &= nbsp;  Apr        &= nbsp;  May        &= nbsp; Jun        &= nbsp;   Jul        &= nbsp;  Aug        &= nbsp; Sep        &= nbsp;  Oct        &= nbsp;  Nov        &= nbsp; Dec


 =

20

 =

3D"Cuadro15

 =

10

 =

5

 =

0

Jan        &= nbsp;    Feb        &= nbsp;    Mar        &= nbsp;    Apr        &= nbsp;    May        &= nbsp;    Jun        &= nbsp;    Jul        &= nbsp;    Aug        &= nbsp;    Sep        &= nbsp;    Oct        &= nbsp;    Nov        &= nbsp;    Dec

 =

Esto se observa de forma resumida en la Figura 9, en la cual el uso de valores promedio para la distribución de cuartiles y los límites externos permi= te identificar una tendencia<= span style=3D'letter-spacing:-.75pt'> marcada del desplazamiento m= eridional de la ITCZ, alcanzando su valor más bajo en el mes de marzo, y su valor más alto entre agosto y septiembre. De esta forma, es posible coincidir con lo expuesto por Schneider (2014) y Phi= lander (1996) en que la ITCZ se ubica principalmente sobre la línea ecuator= ial, con puntuales excepciones en los meses de marzo y abril de ciertos añ= ;os. A su vez, la distribución obtenida para la ubicación de la ITCZ en cada mes compone un recurso importante para su análisis, abasteciendo de datos y reduciendo la incertidumbre con otras zonas de convergencia o la existencia de bifurcaciones que mencionan Bellucci (2010)= y Bischoff (2016).

 

Aunque el comportamiento de la ITC= Z es consistente para cada mes a través de los años, existen variaciones abruptas importantes en los años donde se encontró activo el ENSO en la región. En la Figura 8 es posible visual= izar un comportamiento atípico, en referencia<= span style=3D'letter-spacing:-.15pt'> a la distribución acumulada,= entre los meses finales de 1982 hasta los primeros de 1983 y de manera similar entre los año= s de 1997 y 1998. Esto concuerda con lo planteado por Münnich (2005) y Ham (2014) respecto al descenso latitudinal de la ITCZ en relación con el flujo energético en= la zona intertropical y su relación con la presencia del ENSO. También puede atribuirse el = desplazamiento abrupto de la ITCZ a distintos factores medioambientales y de cambio climático como mencionan Hu (2007) y Sulca (2018), lo cual podría explicar la presencia de varios meses atípicos identificados= en las distribuciones mensuales, como son: Enero 1992 y 2016, Febrero 1992, 20= 00, 2010 y 2017, Abril 1996 y 2012,= entre otros.

 

Adicionalmente, en la Figura 10 se= puede observar el desplazamiento de la ITCZ en diferentes meses del año, e= stos gráficos muestran la acumulación de puntos de convergencia pa= ra cada mes, en los 41 años de datos disponibles en el modelo de reanálisis ERA Interim. = Se evidencia que existen desplazamientos más notorios en la regió= ;n del Pacífico oriental que más dista del continente americano y desplazamientos cortos y menos frecuentes, en la región más cercana. También es posible identificar una variaci&oacut= e;n estacional en esta figura, debido a que= en cada año existe un comportamiento similar respect= o a la ubicación en los diferentes


&nbs= p;

meses, c= umpliendo con la tendencia a descender en el primer trimestre del año y luego ascender con fluctuaciones en el se= gundo y tercer trimestre, lo que ya había sido propuesto en estudios realizados por Lashkari (2017) y Keshtgar (2020).

 

Figura 10

Conteo de datos de convergencia acumulados para<= span style=3D'letter-spacing:-.3pt'> registro histórico


 

Al visualizar las distribuciones mensuales de todos los años se identifican meses con un rango intercuartílico a= típico como: Febrero de 1984, 1995, Septiembre de 1982, 1992 y 2015, y Julio de 1994, entre otros. También es posible identificar una reducción del rango intercuartílico en los años posteriores a 1900, con excepciones puntuales, lo cual coincide con las proyecciones


&nbs= p;

de Byrne (2018) respecto a la disminución de la amplitud de la ITCZ en el siglo 20. La presencia p= untual de valores atípicos en la variación del rango intercuartí= lico en los años con presencia del ENSO podría implicar que junto = con el descenso longitudinal también se manifieste un incremento de la amplitud en la ITCZ como consecuencia de este fenómeno.

 = ;

Sobre l= os resultados obtenidos tambi&= eacute;n se puede interpretar que existe un patrón muy marcado y constante en la localización de la ITCZ. Como se obser= va en la Figura 10, es posible id= entificar varias zonas de convergencia importantes en= el mapa global, bien definidas y delimi= tadas entre sí, aunque la excepción está en la zona del pacífico oriental, donde varios autores han identificado una zona de bifurcación por la gran masa de agua del océano pacífico que incide en la variabilidad d= e condiciones climáticas de la zona (Bischoff & Schneide= r, 2016; Keshtgar et al., 2020). Es posible afirmar que, en el registro histórico, los meses finales del año presenta= n una menor dispersión de datos, lo cual simplifica la localizaci&= oacute;n de la ITCZ y reduce la incertidumbre; = mientras que en contraste, los meses intermedios tienen un mayor valor para su rango intercuartílico promedio, dispersión y valores extremos por lo que su localización representa una dificultad mayor.

 

Finalmente, es necesario contextualizar que los resultados de esta metodología buscan ser un complemento para una serie de herramientas climatológicas <= /span>de predicción, desarrolladas con la finalidad de integrar un sistema de sop= orte técnico para tomar decisiones respecto a la planificación y desarrollo de operaciones marinas en el país. P= or esto, no se pueden conside= rar los resultados como un punto final, sino al contrario, como un punto de partida para continuar desarrollando un modelo completo que permita emplear la ITCZ como característica predictiva de clima y fenóme= nos atmosféricos y oceánicos. A su vez en el desarrollo de este est= udio se encontraron avances respecto a la localización de la ITCZ en diferentes regiones, los mismos que emplean modelos de datos atmosféricos similares combinados con un procesamiento estadístico, como el trabajo de Žagar (2011), Colna (2017), y Lashkari (2017= ).

.

 

Conclusiones

La elaboración de un algoritmo para el post-procesamiento, filtrado y visualizaci&oac= ute;n de datos de vien= to es posible desde diferentes enfoques; en este estudio el uso de un méto= do heurístico simplificado<= span style=3D'letter-spacing:-.4pt'> permitió que el tiempo de trabajo del algoritmo y la memoria requerida para almacenar los datos fueran cortos. Adicionalmente, el= uso de herramientas gráficas y estadísticas combinadas rep= resentó una gran ventaja para analizar todas las variables necesarias e incrementar la confiab= ilidad de los resultados.

 

En los algoritmos de procesamiento se realizaron va= rias iteraciones a fin de modelar e identif= icar los rangos en los que se ubican diferentes zonas de convergencia. Este planteamiento, junto con herramientas visuales de registro acumulado de tiempo, facilitó la identificación de las variab= les físicas y geográficas que permitieron establecer la localización de la ITCZ, especialmente en zonas como el Océano Pacífico Oriental donde la presencia de bifurcación entre zon= as de convergencia o la doble ITCZ complicaba el análisis.

 

El criterio inicial para encontrar estadísticamente la localización de la ITCZ no es suficientemente robusto, considerando la presencia de valores atípicos y la dificultad de reducir la dispersión de datos al emplear únicamente algoritmos de conexión basados en rangos de distancia y caracterización de líneas de convergencia, por lo cu= al se propuso un contraste estadístico que facilite la visualizaci&oacu= te;n acumulada de datos. Esto ha abierto una nueva perspectiva al estudio, pues = se podrán combinar ambos criterios para permitir delimitar la ITCZ en z= onas de bifurcación y respaldar el análisis con el registro histórico acumulado, que muestra de forma clara la acumulació= n de puntos de convergencia con patrones constantes.


&nbs= p;

Aunque la resolución y confiabilidad de los datos del modelo de reanálisis ERA Interim es buena, sería mejor emplear todos los algoritmos en los datos mejorados del modelo de reanálisis ERA 5, pues esto permitirá incrementar la resolución de los resultados y a su vez obtener mayor precisión en las zonas que pueden ser de gran interés a una escala nacional o regional.

 

 

Reconocimientos

Este trabajo ha sido financiado parcialmente por el Proyecto de Investigación CEDIA IDI XVII-2023-12= -WEC GALAPAGOS y EPN PIGR-2103. Los autores desean expresar su agradecimiento a CEDIA y la Escuela Politécnica Nacional.

 

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