In the Panama Bight, two different seasonal surface circulation patterns
coincide with a strong mean sea level variation, as observed from 27 years
of absolute dynamic topography (ADT) and the use of self-organizing maps.
From January to April, a cyclonic gyre with a strong southwestward Panama
Jet Surface Current (PJSC) dominates the basin circulation, forced by the
Panama surface wind jet that also produces upwelling, reducing sea surface
temperature (SST), increasing sea surface salinity (SSS) and causing an ADT
decrease. From June to December, the Choco surface wind jet enhances SST,
precipitation and river runoff, which reduces SSS, causing an ADT rise, which in turn forces a weak circulation in the bight, vanishing the PJSC. Interannual
variability in the region is strongly affected by El Niño–Southern Oscillation (ENSO); however this
climatic variability does not modify the seasonal circulation patterns in
the Panama Bight. In contrast, the positive (negative) ENSO phase increases
(decreases) SST and ADT in the Panama Bight, with a mean annual difference
of 0.9 ∘C and 9.6 cm, respectively, between the two conditions,
while its effect on SSS is small. However, as the strong seasonal SST, SSS
and ADT ranges are up to 2.2 ∘C, 2.59 g kg-1 and 28.3 cm,
the seasonal signal dominates over interannual variations in the bight.
Ministerio de Ciencia e InnovaciónPID2021-123352OB-C31; TED2021-130949B-I00Introduction
The Pacific Ocean covers about half of the Earth's oceans' surface and thus
with ocean–atmosphere coupled processes affects the entire planet's
climate (e.g., Xue et al., 2020). In the eastern tropical Pacific
(hereinafter ETP), the westward North Equatorial Current (NEC) and South Equatorial Current (SEC) as well as the eastward North Equatorial Countercurrent (NECC) form the main circulation (Kessler, 2006; Wooster, 1959; Wyrtki, 1966). The
Panama Bight is placed on the easternmost side of the ETP, bordered by
Central and South America, north of the Equator (Fig. 1). Although the
circulation of the large subtropical gyres affects the Panama Bight, due to
its sheltered position, local factors dominate its seasonal circulation and
ocean properties (Rodríguez Rubio et al., 2007), with the potential to
affect a wider region.
The circulation in the Panama Bight has been described in terms of a
reversing oceanic gyre, forced by monsoon-like winds with a cyclonic
circulation during the boreal winter and an opposite anticyclonic
circulation during the boreal summer (Devis-Morales et al., 2008;
Rodríguez-Rubio et al., 2003). However, a weakening of the cyclonic
circulation during summer has also been proposed (Chaigneau et al., 2006;
Dimar, 2020; Kessler, 2006; Stevenson, 1970). This is an important issue
that needs to be clarified, as an opposite seasonal circulation will affect
ocean–atmosphere processes in the bight such as precipitation, river runoff,
the mixed layer depth, ocean vertical stratification, sea level and coastal
dynamics, among others. Additionally, these physical factors affect
chlorophyll a, the phytoplankton growth and biodiversity in the area
(Corredor-Acosta et al., 2020).
Map of the eastern tropical Pacific (ETP) west of 100∘ W. The 200, 1000 and 3000 m isobaths are shown in gray. The limits of the
Panama Basin (Bight) are shown in red (yellow). The cyan box displays the
northern area used to compute the Niño 1+2.
El Niño–Southern Oscillation (ENSO) severely affects ocean and
atmospheric dynamics in the ETP at irregular timescales. During the positive ENSO phase (El Niño), the southern trade winds weaken, with
consequences for the coastal upwelling on the Peruvian coasts. Additionally, the
western Pacific warm pool migrates towards the east, raising the sea surface
temperature (SST) and sea level while deepening the thermocline, which
affects the biological productivity and hydrological cycle toward South America's
coast. By contrast, the negative ENSO phase (La Niña) enhances the
normal conditions, lowering the SST and sea level while shoaling the
thermocline toward the east (Cabarcos et al., 2014; Grados et al., 2018;
Kessler, 2006; Trenberth, 1997).
Surface dynamics in the ETP east of 120∘ W are more complicated
than in the central Pacific, as meridional flow interacts with prevailing
zonal currents such as the NECC and SEC. For example, the geostrophic
eastward NECC extends towards Central America, merging with the cyclonic
circulation around the Costa Rica Dome (Fig. 2), with large variability
related to changes in the Papagayo wind jet, which occurs at weekly
timescales. Additionally, the westward SEC is mainly observed in two main lobes,
about 3∘ S and 3∘ N (Kessler, 2006).
Climate in the Panama Bight is driven by the meridional translation of the
Intertropical Convergence Zone (ITCZ) throughout the year. From December to
April, the ITCZ moves southward, reaching its southernmost position at
∼1∘ N (Dimar, 2020; Poveda et al., 2006; Villegas
et al., 2021). During this season, northern trade winds from the Caribbean Sea
cross the Isthmus of Panama through orographic gaps, forming the Panama surface
wind jet that affects the Panama Bight ∼400 km towards the
Equator (Chelton et al., 2000; Rueda Bayona et al., 2007). The stress from
this jet produces distinctive curl dipoles (Kessler, 2006), forcing a
cyclonic circulation (counterclockwise) in the Panama Bight, as a response
to the sea level drop due to divergence of surface waters to the west,
causing upwelling. In this region, upwelling forces SST reduction and sea surface salinity (SSS)
increase, due to the upwelling of colder and saltier Subtropical Surface
Water (STSW; Fiedler and Lavín, 2006). This gyre, which Chaigneau
et al. (2006) named the Panama Bight Cyclonic Gyre (PBCG), is formed by the
northward Colombia Coastal Current (Fig. 2), a westward current in the
Panama Gulf, a south-southwestward current at ∼81∘ W (Panama Jet Surface Current) and an eastward current closing the gyre at
∼2.5∘ N (Devis-Morales et al., 2008;
Rodríguez-Rubio et al., 2003).
Between May and October, the boreal summer, the ITCZ reaches its
northernmost position, north of the Panama Isthmus. In this season, the
Choco (CHorro del Occidente COlombiano) surface wind jet dominates the area
(Poveda and Mesa, 2000). This jet is produced by the southern trade winds that
cross the Equator, rotating towards the northeast due to the Coriolis effect and
the atmospheric pressure and SST meridional gradients. These gradients
occur between the warmer waters in the Panama Bight (lower atmospheric
pressure) and the equatorial Cold Tongue (higher atmospheric pressure)
formed by coastal and equatorial upwelling and advection of cooler water
from the Peru Current (Hastenrath and Lamb, 2004; Zheng et al., 2012). Large
SST seasonality in the Cold Tongue also drives the zonal SST gradient across
the equatorial Pacific Ocean, which has important impacts on global climate
(Karnauskas et al., 2009).
The Choco jet transports large quantities of moisture inland (3774 m3 s-1), forcing a high freshwater contribution during this season and making the Colombian coast one of the rainiest locations on the Earth
(Fiedler and Lavín, 2006; Poveda et al., 2006; Poveda and Mesa, 2000;
Tsuchiya and Talley, 1998). The coastline geometry together with the Choco
jet produces surface water convergence and thermocline deepening in the
Panama Bight, raising SST and absolute dynamic topography (ADT) and producing an anticyclonic (clockwise)
circulation (Devis Morales, 2009; Fiedler and Talley, 2006;
Rodríguez-Rubio et al., 2003). However, there is no consensus on this
circulation pattern (Chaigneau et al., 2006; Kessler, 2006).
Strong El Niño events in the altimetry era occur during 1997–1998 and
2015–2016. In this phase, the SST gradient between the equatorial Cold Tongue
and the Panama Bight is reduced, reducing the Choco wind jet, moisture inshore
transport and precipitation and also generating other complex ocean–atmosphere
interactions (Fiedler and Talley, 2006; Poveda et al., 2006), which vary
locally and depend on each event's characteristics (Dimar, 2020).
However, it seems that in the Panama Bight, negative or weakly positive ENSO
events do not significantly change the thermal ocean structure; conversely,
strong positive ENSO increases the ocean's heat content and sea level (Devis
Morales, 2009).
In this context, we review here the seasonal circulation in the Panama Bight
from absolute dynamic topography (ADT), as it includes the mean ocean
currents (mean dynamic topography, MDT), as well as temporal sea level
variability, as represented by altimeter-derived sea level anomalies (SLAs).
Additionally, we extend the circulation assessment to the ETP (east of
100∘ W) to examine the connection between the Panama Bight and
equatorial geostrophic currents. We also study ENSO effects on the Panama
Bight sea level and determine if this forcing has a significant effect on
the seasonal circulation. To understand the steric component of sea level
variations, we also assess SST and sea surface salinity (SSS) variability in
the region at seasonal and interannual timescales.
Data and methods
Daily maps of ADT from the global ocean gridded L4 product with a
0.25∘×0.25∘ resolution for the 1993–2019 period are
used (https://data.marine.copernicus.eu/product/SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057/description, last
access: February 2023). This data set is based on a stable two-satellite
constellation, enhancing the stability and homogeneity of the sea level
record; therefore the product is dedicated to the monitoring of the sea
level long-term evolution for climate applications. This product gave us
better results in coastal regions such as the Panama Gulf when compared to
the L4 product that merges the measurement from all altimeter missions
available (different number of altimeters available over time). Geostrophic
currents from the two-satellite product are used, which are computed using a
nine-point stencil methodology for latitudes outside the ±5∘ N
band (Arbic et al., 2012). In the equatorial band, the Lagerloef et al. (1999) methodology introducing the β-plane approximation is used.
Additionally, we download the MDT (CNES-CLS18) and geostrophic currents,
corresponding to 1993–2012, which were calculated by merging information
from altimeter data as well as GRACE and GOCE gravity field and oceanographic in situ
measurements (Mulet et al., 2021).
Monthly SST and SSS fields with the same time span and spatial resolution
as ADT were obtained from Copernicus
(https://resources.marine.copernicus.eu/product-detail/MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012/INFORMATION, last access:
November 2022), merging in situ and satellite observations from different
projects (Guinehut et al., 2012). We derive TEOS-10 conservative
temperature (Θ) and absolute salinity (SA) using the GSW toolbox
version 3.06 (McDougall and Barker, 2011).
The Oceanic Niño Index (ONI) is used to assess ENSO events.
Positive/negative ENSO events are identified by five consecutive anomalies of 3-month
running SST mean computed in the Niño 3.4 region (5∘ N–5∘ S, 170–120∘ W) that are above/below a
threshold of +0.5 ∘C/-0.5∘C. Anomalies are computed
from 30-year periods, which change every 5 years to account for ocean global
warming. Monthly series of SST anomalies based on the ERSST.v5 product (Huang et
al., 2017) were downloaded from https://origin.cpc.ncep.noaa.gov/data/indices/ (last access: November
2022). SST anomalies refer to the 1991–2020 period.
To find the months in the 1993–2019 period in which a positive or negative
ENSO phase occurred, we use the SST anomaly series from the Niño 1+2
(0–10∘ S, 90–80∘ W) and
Niño 3 (5∘ N–5∘ S, 150–90∘ W)
oceanic regions following the same methodology used by ONI. The former
region is used as an indication of ENSO's local effect on the Panama Bight,
as this region covers the equatorial Cold Tongue, whose SST variations
affect the Choco surface wind jet. The El Niño 3 region is used to
assess if results stand with an ENSO index representative of the central
equatorial Pacific. First comparison between indices shows that El Niño
1+2 has a strong response to La Niña events, while El Niño 3 is
much less responsive to continental influences (Hanley et al., 2003).
We use two different methodological approaches to assess the regional ocean
dynamics. All months are classified in one of the three ENSO-related
conditions: normal, positive and negative (Table 1 and Fig. 6f). When El
Niño 1+2 is used, normal conditions are the most frequent (58.9 % of
occurrence). For positive and negative conditions, all monthly means are
computed using between two and eight values, with negative ENSO being more
frequent (25.6 %) than the positive ENSO condition (15.4 %). When the El
Niño 3 region is used to classify the ENSO conditions, all monthly means
are computed using between three and nine values. The frequency of occurrence is similar to that of El Niño 1+2, with 60.2 %, 25.3 % and
14.5 %, respectively (Fig. A2f in the Appendix).
Niño 1+2 distribution of months for the three conditions:
normal, El Niño (positive ENSO) and La Niña (negative ENSO). In bold
is the number of months used to assess seasonal differences.
Computing monthly means under the three ENSO-related conditions, based on
the Niño 1+2 SST index, assesses seasonal ADT, SST and SSS spatial
anomalies, as well as geostrophic currents in the ETP and Panama Bight. For
example, from the 27 available values in March (Table 1), 18 are used to
compute the March normal condition, 3 for the positive ENSO conditions and 6 for the
negative ENSO conditions. Anomalies are computed by subtracting the
1993–2019 spatial mean from the individual monthly data using all data in
the ETP (66.5 cm, 26.6 ∘C and 33.8 g kg-1).
Regionally averaged time series are obtained for the ETP, the Panama Bight and
the Cold Tongue for the three ENSO-related conditions. Here, the ETP is
defined as nodes between 5∘ S–15∘ N and 76–100∘ W. The Panama Bight limits are 1.875–9.125∘ N and 81.125–77.125∘ W. These limits
were selected to assess the particular local dynamics, which differ from the
rest of the ETP. The Cold Tongue region is placed between 1.125–5.125∘ S and 81.125–88.375∘ W. Note that
the Panama Bight and Cold Tongue regions have the same number of nodes
(Fig. 4).
The second methodological approach uses self-organizing maps (SOMs) in order
to confirm previous results in the ETP. SOMs are a statistical tool used to
compress the information contained in a large amount of data into one single
set of maps (Kohonen, 1982), reducing the high-dimensional feature space of
input data to a lower-dimensional network of units called neurons. SOM
analysis has been used in the oceanography context in several studies (Liu
et al., 2006; Hernández-Carrasco and Orfila, 2018; López et al.,
2022). Learning processes are carried out by an interactive presentation of
the input data to a preselected neuronal network, which is modified during
the iterative process. Each unit is represented by a weight vector with a
number of components equal to the dimension of the input data. During each
iteration, the neuron whose weight vector is the closest to the presented
sample input data vector, called the best-matching unit (BMU), is updated
together with its topological neighbors towards the input sample. When the
probability density function of the input data is approximated by SOMs, and
each unit is associated with that reference pattern that has a number of
components equal to the number of variables in the data set, the training
process finishes. The size of the neural network is an important parameter
to take into account to maximize the quality of the SOM analysis. The
determination of the size of the neural network is empirical and somewhat
subjective (Morales-Márquez et al., 2021).
We chose the number of neurons of the network after testing several sizes of
the map to check that the cluster structures are shown with sufficient
resolution and statistical accuracy. In our case, we have selected for both
the temporal and spatial patterns a 3×2 map (six neurons) configuration using
monthly data from 1993 to 2019 for the ADT and the zonal (U) and meridional (V)
geostrophic velocities in the entire ETP region. The trend and annual and
semi-annual cycles were estimated by use of linear regression fitted to the
monthly time series obtained from the temporal SOMs. Errors were estimated at
the 95 % confidence level. The percentage of explained variance was
calculated from the ratio of the residual variance over the variance of the
original series after subtracting the mean and trend of the time series,
therefore only accounting for the seasonal cycle. A residual time series was
obtained after removing the trend and seasonal cycle. We assessed a causal
relationship between the monthly residuals and ENSO, using the Niño 3
SST time series and evaluating their correlation coefficient with a
significance level of p<0.01. We used El Niño 3 since SOM analysis was
performed with data from the entire ETP. In the case of the spatial analysis
the evolution of a particular pattern is provided by the BMU for each sample, while in the temporal domain the analysis of the neurons provides temporal
patterns, and the BMU is used to localize in space the temporal variability,
identifying regions of similar co-variability patterns (Orfila et al.,
2021).
Mean dynamic topography (MDT) anomalies (color scale) and
associated geostrophic circulation (vectors) representative of the 1993–2012
period for the (a) eastern tropical Pacific (ETP) east of 100∘ W
and (b) the Panama Basin. MDT anomalies are computed subtracting the ETP
regional mean (67.1 cm). South Equatorial Current (SEC), North Equatorial
Counter Current (NECC), Costa Rica Dome (CRD), Colombia Coastal Current
(CCC) and Panama Jet Surface Current (PJSC) are shown.
Results and discussion
To assess the seasonal circulation in the Panama Bight and its interannual
variability, we first analyze the mean circulation from the MDT. As a second
step, in Sect. 3.2 we describe the seasonal circulation based on normal
months where ENSO conditions were not dominant. We further assess the
circulation under positive and negative ENSO conditions in Sect. 3.3 in
order to observe differences from the normal circulation patterns. In both
the seasonal and interannual circulation assessment, we analyze the relationship
between sea level variations and steric changes associated with SST and SSS
variability. In Sect. 3.4 we analyze regionally averaged ADT, SST, SSS and
current speed time series to describe their seasonal behavior under
normal, positive and negative ENSO conditions. Finally, in Sect. 3.5 SOMs are used to verify the previously described circulation patterns. In all the
sections, we first assess the ETP (east of 100∘ W) in order to
contextualize the Panama Bight dynamics, which are described in more detail.
Mean circulation in the Panama Bight from MDT
Geostrophic currents associated with the MDT (the averaged difference
between the mean sea surface and the geoid for the 1993–2012 period) in the
ETP portray the SEC as a strong westwards current between 0–4∘ N, distinguishable west of ∼85∘ W
(Fig. 2a). The SEC results from geostrophic currents produced by a positive
MDT anomaly in the 3 to 5∘ N band and a negative MDT
anomaly south of 0.5∘ N, producing a ∼15 cm
meridional sea level gradient. The NECC can be identified as an eastward
current, less intense than SEC, between 5 and 7∘ N and
reaching ∼90∘ W, where it starts a
counterclockwise rotation around the Costa Rica Dome, which corresponds to a
MDT bowl (upwelling), forced by the Papagayo surface wind jet (Fig. 2a).
East of 100∘ W, this circulation responds to a MDT gradient
between the positive anomaly in the 3 to 5∘ N band and
the negative MDT anomaly at ∼9∘ N. These
circulation patterns coincide with the description given by Kessler (2006).
The mean circulation in the Panama Basin (Fig. 2b) differs from the
predominant zonal circulation at the same latitudinal band west of
∼84∘ W. In this area, the Panama surface wind jet
produces a MDT bowl (upwelling) in the Panama Gulf. The most representative
mean circulation feature is a counterclockwise rotation that dominates the
northern part of the Panama Bight (east of ∼81∘ W).
This circulation shows a strong northward coastal current known as the
Colombia Coastal Current (2.5–7.5∘ N), a cyclonic current around
the Panama Gulf and the southwestward Panama Jet Surface Current
(Devis-Morales et al., 2008). The cyclonic rotational gyre closes with
weaker eastward currents at ∼4.5∘ N. Therefore, the
mean circulation in the region shown by MDT coincides with the dominant
circulation at the beginning of the year when the Panama wind jet affects
the Panama Bight. Note that the Panama Jet Surface Current extends
southward, connecting with the SEC at ∼2.5∘ N–82∘ W.
Seasonal circulation in the Panama Bight under normal (no ENSO)
conditions
We assess monthly ADT and associated geostrophic circulation based on
normal ENSO conditions (Table 1), first in the ETP east of 100∘ W
and later in the Panama Bight. Based on the annual observed behavior
(Fig. A1), we show results from 2 representative months (Fig. 3b
and e). March represents the circulation from January to April, when the
Panama wind jet dominates the basin's dynamics. November represents the
circulation from June to December, when the Choco wind jet is dominant.
In the ETP positive ADT anomalies are observed from June to December in the
band between 1 and 7∘ N, extending toward the Colombian
coast (Fig. 3e). In this area, a weak circulation dominates the Panama
Bight east of ∼84∘ W, in response to the positive
ADT anomaly (Fig. 4f). This circulation shows northward current around
80–82∘ W with speeds below 20 cm s-1 (Fig. 4h). However,
the coastal counterclockwise circulation formed by the Colombia Coastal
Current (north of ∼4∘ N at the latitude of the San
Juan River mouth) and westward circulation in the Panama Gulf are observed
with speeds over 20 cm s-1. In this season, the Choco wind jet forces
convergence of warm water toward the coast (Fig. 5e). Salinity is reduced due
to the increase in precipitation and river outflow (Fig. 5k), reducing
surface density and raising ADT in the Panama Bight (black line in Fig. 6a, c and d) and forcing the relatively weaker circulation in the bight. During
this season, a clear meridional gradient in the ETP separates cold and
saltier waters to the south from warm and fresh waters to the north. In
addition, upwelling forced by the Panama and Papagayo surface wind jets is
weak as a small signature can be distinguished in SST and SSS.
Monthly average of ADT anomalies (color scale in centimeters) and
associated geostrophic circulation (vectors) for the eastern tropical
Pacific (ETP). Seasonal changes are indicated within 2 months: March (a, b, c) and November (d, e, f). ENSO-related conditions (based on Niño
1+2) shown are normal (b, e), negative ENSO (a, d) and positive ENSO (c, f).
Each condition has a different color range to highlight the ADT regional
gradients, responsible for the geostrophic circulation. ADT anomalies are
computed subtracting the ETP regional mean during the 1993–2019 period (66.5 cm). The regional average of the ADT anomalies for each month is shown in
the upper right of each panel, which coincides with time series values in
Fig. A2a.
From January to April, the Papagayo and Panama wind jets strengthen due to
the northern trade winds' intensification in the Caribbean. The circulation in
the Costa Rica Dome (negative ADT anomaly) extends southwestward (Fig. 3b), weakening the positive ADT anomaly in the 1–7∘ N
band, as is clearly seen in the other season. As a consequence, an anticyclonic
circulation (positive ADT anomaly) appears between the Costa Rica Dome and
Panama Bight cyclonic circulations. Additionally, colder and saltier surface
waters also indicate the upwelling intensification forced by these two
surface wind jets. In this season the SST and SSS meridional gradients
between the Panama Bight and the Cold Tongue weaken when compared to the
other season (Fig. 5b and h). Note a warming during these months of the
Cold Tongue, observed at the south of the ETP, with small seasonal changes
in its salinity, except for a northern coastal migration.
Monthly averaged ADT anomalies (color scale in centimeters) and associated
geostrophic circulation (vectors) for the Panama Basin. Seasonal changes are
indicated within 2 months: March (a, b, c) and November (e, f, g).
ENSO-related conditions (based on Niño 1+2) shown are normal (b, f),
negative ENSO (a, e) and positive ENSO (c, g). Each condition has a different
color range to highlight the ADT regional gradients, responsible for the
geostrophic circulation. ADT anomalies are computed subtracting the ETP
regional mean during the 1993–2019 period (66.5 cm). Geostrophic vectors and
their speed (color scale in centimeters per second) for normal conditions are included
for the same months (d, h) with 20 and 40 cm s-1 contours included. In
(a) the yellow line indicates the Panama Bight area used to compute
regionally averaged time series shown in Fig. 6a to d.
In this season, the Panama Bight is dominated by a strong cyclonic
circulation (Fig. 4b), in response to the ADT drop (Fig. 6a black line),
forced by the Panama wind jet (which produces Ekman transport to the west)
and the corresponding upwelling intensification (SST decreases, and SSS
increases, as seen in Fig. 5b and h). The SSS increases in the bight due to
precipitation and river outflow reduction during this season, contributing
to the ADT drop (Fig. 6). The relatively stronger cyclonic circulation is
composed of the Panama Jet Surface Current, extending from the Azuero
Peninsula (7.2∘ N, 81∘ W) on the southern
coast of Panama to the southwest, with speeds >40 cm s-1
(Fig. 4d), turning into a westward flow at ∼85∘ W
(Fig. 3b) and merging at ∼90∘ W with the SEC. A limb
of the Panama Jet Surface Current turns into an eastward flow
at ∼2–4∘ N, reaching the coast. At this latitude, the
northward Colombia Coastal Current is distinguishable, with speeds
>40 cm s-1 between ∼4∘ N and the Panama
Gulf, where currents become westward. Interestingly, the Colombia Coastal
Current north of ∼4∘ N and cyclonic circulation in
the Panama Gulf are permanent features throughout the year, with seasonal
variations in their speed.
Seasonal ADT, SST and SSS variations in the Panama Bight are strong and
respond to local dynamics, which differ from those from the ETP (Figs. 6
and A2 comparison). Seasonality in the spatially averaged ETP
time series results from a combination of different ocean patterns observed
in this region such as the SEC, NECC, Costa Rica Dome, southern Cold Tongue and
circulation in the Panama Bight. Therefore, in Sect. 3.5 we explore ETP
circulation patterns and their seasonality using SOM analysis.
Monthly averaged anomalies of sea surface temperature (SST; a to
f) in degrees Celsius and sea surface salinity (SSS; g to l) in grams per kilogram
for the eastern tropical Pacific (ETP). Seasonal changes are indicated
within 2 months: March (a, b, c and g, h, i) and November (d, e, f and j, k, l).
ENSO-related conditions (based on Niño 1+2) shown are negative ENSO
(first column), normal (second column) and positive ENSO (third column).
Anomalies are computed subtracting the ETP regional mean during the
1993–2019 period (26.6 ∘C and 33.8 g kg-1). The regional
average of the anomalies for each month is shown in the upper right of each
panel, which coincides with time series values in Fig. A2c, d.
We also compare geostrophic currents' seasonality from ADT with the annual
MDT (Fig. 2). West of ∼84∘ W, ADT and geostrophic
circulation from July to December coincide with the annual MDT circulation.
In contrast, the ADT and circulation in the Panama Bight from January to
April coincide with the annual MDT, especially due to the presence of the
Panama Jet Surface Current.
Variations in the Panama Bight seasonal circulation related to ENSO
In this section we assess the influence of ENSO on the ETP and Panama Bight
seasonal ocean dynamics. Only 2 years corresponding to the strong positive
ENSO condition of 1997–1998 and 2015–2016 present this phase during January
and February (Fig. 6f). Although this is a small number of outputs, we
believe that even for these months, results accurately indicate El Niño
conditions since there are two dominant seasonal dynamics in all the
variables assessed, which are observed in the similar patterns from January
to April or from May to December. For example, in the first quarter of the
year, the 12 available positive ENSO months (Table 1) show very similar
ocean dynamics during this season.
In the ETP, El Niño events increase SST, while the negative phase
reduces it; in contrast, a small interannual variation is observed in
SSS (Figs. 6 and A2). As a consequence, changes in temperature
dominate a region-wide sea level rise during El Niño and sea level drop
during La Niña in the entire ETP, including the Panama Bight (Fig. 6).
We assess if such sea level changes affect the seasonal circulation patterns
observed under normal conditions (Sect. 3.2). At this point we want to remark
that surface currents respond to ADT gradients and not to basin-wide sea
level variations. Therefore, we highlight ADT gradients under all the
ENSO-related conditions shown in Figs. 3 and 4 by shifting the ADT
color limits but maintaining a 35 cm range, defined from the ADT variability under normal conditions. For negative/positive ENSO, ADT color limits are
shifted -5 cm/+10 cm with respect to the normal months.
Both El Niño and La Niña seasonal circulation patterns in the ETP
are very similar to the circulation observed in normal months. From June to
November, the relatively higher ADT anomalies in the 1 to
7∘ N band, extending to the Colombian coast, persist (Fig. 3).
A weaker circulation in the Panama Bight and vanishing of the Panama Jet
Surface Current are also observed (Fig. 4). Similarly, from January to
April, the Papagayo and Panama wind jets' forcing is observed under all ENSO
conditions, including the Costa Rica Dome, the strong cyclonic circulation
in the Panama Bight and the anticyclonic circulation between them.
Therefore, a distinctive seasonal circulation pattern dominates the Panama
Bight regardless of the ENSO-related ADT mean shifts. In addition, large
interannual differences in the Panama Bight circulation speed are not
observed (Fig. 6b).
We also assess if the small interannual variations observed in the seasonal
circulation stand when other ENSO indexes are used. For this purpose, we
use the Niño 3 index to determine positive and negative ENSO anomalies.
Although positive/negative ENSO months show large differences between the
two indices (comparison of Figs. 6f and A2f), the seasonality of
the geostrophic currents does not change, showing small interannual
variations in both cases. This indicates that our results stand even if ENSO
variability is assessed from an open-ocean region.
The seasonal circulation patterns in the Panama Bight, as well as the small
variations related to ENSO, coincide with results from Chaigneau et al. (2006), which were based on 25 years of satellite-tracked drifter
trajectories. However, a study from Corredor et al. (2011) reports
statistical differences in surface current speed calculated in four
sub-regions placed in the ETP, for September to November. Although their
methodology uses total currents, estimated as the sum of Ekman and surface
geostrophic currents, such statistical differences in the current speed
might exist in some specific areas. For this reason we report small
differences in the ENSO-related circulation patterns in both the ETP and
Panama Bight.
Monthly spatially averaged values in the Panama Bight for
1993–2019, differentiating three ENSO-related conditions (based on Niño
1+2) – normal (black), negative (blue) and positive (red) – showing each
time series annual mean. (a) Absolute dynamic topography (ADT) anomalies in
centimeters. (b) Geostrophic current speed in centimeters per second. (c) Sea surface temperature
(SST) anomalies in degrees Celsius. (d) Sea surface salinity (SSS) anomalies in
grams per kilogram. Anomalies are computed subtracting the corresponding regional
and temporal mean (value shown in the top of each panel). (e) Monthly SST
difference between the warm Panama Bight and the Cold Tongue areas shown in
Fig. 5. (f) Matrix indicating the monthly ENSO-related condition between
1993–2019, based on the Niño 1+2 region (Sect. 2). Normal months in
white, negative and positive ENSO conditions in blue and red, respectively.
Regionally averaged seasonal and interannual variations in the Panama
Bight
The assessment of seasonal and interannual regionally averaged ADT
variations is important to evaluate sea level extremes that severely affect
the coastal zone. Results in the previous section demonstrate that ENSO
does not significantly affect ADT gradients in the study area and thus does
not affect the dominant seasonal circulation in the ETP and Panama Bight.
However, ENSO affects the ADT regional mean, which we study in more detail
here. As previously mentioned, the ETP 27-year regional ADT mean is 66.5 cm
above the geoid, while for the Panama Bight this value is 70.0 cm. The spatially averaged time series of ETP (Fig. A2) result from the combination
of different circulation patterns (Fig. 2). Interannual variations in the mean ADT are crucial to understand ENSO effects on regional sea level. The
higher ADT mean corresponds to the El Niño conditions (9.1 cm), while
the lower is for La Niña (-3.8 cm). This difference is clearly related
to the warmer SST under positive ENSO conditions and relatively colder SST under
negative ENSO conditions. Small interannual changes are observed in SSS,
which do not significantly modify the seasonal signal. Additionally, in the ETP,
differences in geostrophic velocities are not noticeable under the three
ENSO-related conditions. The annual-mean differences under the three conditions
are below 0.3 cm s-1 (all of them around 23.1 cm s-1). These
results support the small impact on circulation (mean speed) due to ENSO
variability in the ETP.
The regionally averaged ADT time series in the Panama Bight responds to the
two distinctive oceanic circulation patterns observed throughout the year
(Fig. 4) whose seasonality is not strongly affected by ENSO (Fig. 6).
The lowest ADT occurs from January to April, indicating the sub-regional sea
level drop due to the strong cyclonic circulation forced by the Panama wind
jet. The upwelling intensification during these months is clearly observed
in the SST drop and SSS increase. In contrast, from June to December
higher ADT results from warmer and fresher surface waters as a consequence
of the dominant Choco surface wind jet. The ADT seasonal range is 20.3 cm in the
Panama Bight during normal months, which coincides with the seasonal cycle
previously reported from tide gauges and from a regression fitted to
altimetry data (Dimar, 2020, chap. 5). Large seasonal differences are
also observed in the geostrophic currents' velocity. Stronger currents occur
from January to April, while slower and less variable currents occur from June to
December. Therefore, the cyclonic circulation associated with the Panama
wind jet is also the fastest. The seasonal speed range under normal conditions is
16.6 cm s-1, between February and July, thus over half the mean speed
(Fig. 6b).
ADT seasonality is similar for all ENSO-related conditions; therefore
seasonality dominates over the interannual sea level shifts in the Panama
Bight (Fig. 6a). In this region, the ADT annual mean is higher (6.4 cm)
under El Niño conditions and lower during La Niña (-3.3 cm),
which is a smaller difference than what was observed for the ETP. Note that
the ADT annual mean during El Niño is 2.8 cm lower in the Panama Bight
than in the ETP due to a weaker ocean warming in the bight (0.56 ∘C below the ETP annual mean). The largest seasonal ADT range is found during El
Niño in the Panama Bight (28.3 cm), as a consequence of a larger
temperature increase from October to December due to the El Niño peak at the
end of the year. Additionally, currents' mean speed and seasonality in the Panama
Bight are very similar under the three ENSO-related conditions (Fig. 6b), indicating that ENSO phenomena affect mainly the sub-regional sea level and
not the circulation patterns (mean speed).
SSS has a strong seasonal cycle (up to 2.59 g kg-1 during El Niño)
with small interannual variations in the Panama Bight (Fig. 6d). This was
unexpected as the literature indicates that ENSO affects local precipitation
and river runoff, which will also affect SSS. To explore the reasons behind
the small interannual SSS variations, we assess the SST spatial gradient
between the southern Cold Tongue and the Panama Bight (areas shown in Fig. 5), as this gradient modulates the Choco surface wind jet.
SST monthly means in the Cold Tongue show larger seasonal variability than
SST in the Panama Bight (Fig. A2e), with warmer months from January to
April (ITCZ at its southernmost position produces weaker upwelling in the
Cold Tongue). Under normal ENSO conditions, only in March is the SST in the Panama
Bight colder than the SST in the Cold Tongue. In both areas, El Niño
(La Niña) conditions show warmer (colder) SST; however, shifts from
normal conditions are larger in the Cold Tongue. Note that the coldest SST in
the Cold Tongue occurs in September under La Niña conditions, which
would indicate stronger upwelling as a consequence of stronger southern
trade winds.
From May to December the SST differences between the Panama Bight and Cold
Tongue are larger (Fig. 6e), which coincides with the intensification of
the Choco surface wind jet in the former area. These results are in
accordance with the literature (Poveda and Mesa, 2000; Hastenrath and Lamb,
2004). Additionally, a larger SST difference between the Panama Bight and the
Cold Tongue occurs during La Niña, with an annual mean of 5.4 ∘C and seasonal range of 8.3 ∘C, while they are smaller under El
Niño conditions (2.9 and 7.3 ∘C). These results
indicate that under positive ENSO conditions, the Choco surface wind jet will
be weaker than under normal conditions (smaller SST differences between the two
areas from May to December), while the opposite will happen under negative ENSO
conditions.
(a) Absolute dynamic topography (cm) and (b) zonal and (c) meridional
geostrophic current (cm s-1) time series from 1993 to 2019, which
results from a 3×2 SOM temporal analysis in the eastern tropical Pacific. (d) Sub-regions represented by the six temporal SOM patterns.
Under El Niño conditions, warmer SST enhances precipitation in the
Panama Bight, which is expected to reduce SSS. However, we speculate that
small ENSO-related SSS variations found in the Panama Bight are due to
compensating mechanisms acting differently in the two seasons. In the first
quarter of the year, positive ENSO strengthens the Panama surface wind jet
(e.g., Sayol et al., 2022), enhancing upwelling in the Panama Bight, which
would increase SSS, which would in turn be reduced (compensated) due to enhanced
precipitation. From May to December, a weaker Choco surface wind jet reduces
the inshore moisture transport to the Panama Bight, therefore compensating
precipitation increase due to warmer SST. The opposite mechanism will occur
under La Niña conditions. Bear in mind that small interannual SSS
variations in the Panama Bight do not necessarily indicate small
ENSO-related variations in coastal precipitation, which is an active topic
of study (e.g., Sayol et al., 2022).
We also assess results from this section using the Niño 3 index (not
shown). We found a larger difference among the ADT annual means for the
three ENSO-related conditions in the Panama Bight. The mean for El Niño
is 7.0 cm, and for La Niña it is -3.6 cm. However, regional ADT and current
speed seasonality is very similar to the results using the Niño 1+2
index. Therefore, we did not find large differences in the results we report
in this section when ENSO variability is assessed from a larger equatorial
area in the open ocean. However, we believe that using the Niño 1+2
index is more appropriate to assess circulation variations in the Panama
Bight, as dynamics in this region (e.g., Cold Tongue) affect our study area
(e.g., Choco wind jet).
Circulation patterns in the eastern tropical Pacific from SOM analysis
Results from the SOM analysis in the temporal domain support previous
findings in the ETP, which indicate that surface currents' strong seasonality
dominates over interannual variations associated with ENSO; in contrast,
ADT is strongly modulated by El Niño (Fig. 7a). The seasonal cycle's
explained variance in the zonal and meridional currents is 21 % and 53 %,
respectively, when the six neurons are averaged, while their correlation mean
with Niño 3 is 0.30 and 0.21, respectively (Table 2). Conversely, the
seasonal cycle's mean explained variance in ADT is only 7 %, while the mean
correlation with Niño 3 is 0.89. Slightly weaker mean correlations are
found with Niño 3.4 (0.81) and Niño 1+2 (0.86).
Zonal currents dominate the circulation in the ETP, with a mean value 1
order of magnitude larger than meridional currents (Table 2). A comparison
between the temporal SOM spatial distribution (Fig. 7d) and main currents
in the ETP (Fig. 2) allows us to identify the second neuron with the SEC
and the fifth neuron with the NECC, extending to the east as part of the
southern limit of the Costa Rica Dome. The former has the strongest westward
flow (-28.1 cm s-1) and the latter the strongest eastward flow (18.3 cm s-1). Additionally, neuron 6 seems to be related to the westward
circulation in the Costa Rica Dome (northern side) and Cold Tongue (west of
89∘ W), characterized by small mean ADT. Note that neurons 2, 4
and 6, which are mainly westward, can reverse and become eastward during
strong El Niño events, such as the one in 1997–1998 (Fig. 7b).
Temporal SOM 3×2 neurons (Neu) of variability for absolute dynamic
topography (ADT) and zonal (U) and meridional (V) currents. Mean, percentage of
explained variance of the seasonal cycle (%EV SC) and significant
correlations (Corr) with Niño 3 are shown. The number of months of
occurrence of the different ENSO conditions (Niño 3) for the six spatial
SOM neurons is included.
ADT Zonal current Meridional current No. of months Mean%EVCorrMean%EVCorrMean%EVCorrNormalNiñoNiña(cm)SC(cm s-1)SC(cm s-1)SCNeu174120.90-0.5440.12-1.131-0.1640140Neu26750.89-28.1110.35-5.263-0.1751045Neu37070.908.9220.240.745–23191Neu46350.89-18.790.40-3.476–17015Neu56660.8918.3230.252.6500.2324115Neu65970.88-9.3150.44-1.5560.3040316Mean/total70.89210.30530.211954782
Neurons 1 and 3 dominate the Panama Bight (Fig. 7d). Neuron 1 is the only
one in which mean (southward) meridional currents are larger than mean zonal
currents. However, both neurons have large seasonality; therefore they
regularly change direction (except U in neuron 3), which agrees with the
seasonal circulation variations forced by the surface wind jet in the Panama
Bight (Fig. 3). In the Panama Gulf, neuron 2 supports the permanent
westward currents shown in Fig. 4.
The six SOM neurons in the spatial domain mainly indicate two different
circulation patterns (Fig. 8a). Neurons 5 and 6 show the dominant
circulation from January to April (Sect. 3.2), when the effect of the
Panama and Papagayo surface wind jets is observed in a stronger Panama Jet
Surface Current and the northern boundary of the Costa Rica Dome. In contrast, the other neurons show the dominant circulation from June to
December. However, neurons 3 and 4 differ from 1 and 2 mainly in the area
south of the Azuero Peninsula, as in the former neurons southward
circulation prevails, while in the latter neurons, northward circulation is
observed. Therefore, neurons 3 and 4 seem to be transitional circulation
patterns. The temporal occurrence, shown in the BMU (Fig. 8b),
corroborates the former description. Neurons 5 and 6 always occur between
January and April (91.7 % of the months), while neurons 1 and 2 occur
mostly between June and November (89.5 % of the months). Neurons 3 and 4
together are observed in 75 months, 65.3 % occurring in May and December,
during the two seasons' transition.
Regional-mean ADT shows large differences between the odd and even neurons.
Even neurons show smaller ADT spatial mean (60.4 to 61.8 cm) than odd
neurons (71.2 to 72.6 cm). This ADT shift corresponds to what is expected
from La Niña and El Niño conditions, respectively (Fig. A2).
Furthermore, odd and even neurons do not show large differences in
circulation (when compared in the same row in Fig. 8a), except for the
strongest westward currents in the Cold Tongue and SEC, as expected from La
Niña conditions. These results corroborate that in the ETP ENSO strongly
affects the mean ADT, but not the circulation seasonality (Sect. 3.3). To
highlight this relation, we combine the BMUs with the prevailing ENSO
condition (Fig. 8b). From 82 months under La Niña conditions, 92.7 %
coincide with even neurons. From 47 months under El Niño conditions,
93.6 % coincide with odd neurons (Table 2). Similar results were obtained
when ENSO variability was calculated with Niño 1+2; however the
relation with the spatial SOM neurons deteriorates, as even neurons coincide
with 84.3 % of La Niña months, while odd neurons coincide with 76 %
of El Niño months.
(a) Spatial patterns of geostrophic speed (color scale) and
currents (vectors in centimeters per second), which result from a 3×2 SOM spatial
analysis in the eastern tropical Pacific, computed from 1993 to 2019; 20 and
40 cm s-1 contours are included. (b) Monthly best-matching unit (BMU) of
SOM patterns, indicating negative (-) and positive (+) ENSO-related
conditions, based on the Niño 3 region.
Summary and final remarks
Seasonal ocean circulation in the Panama Bight has been assessed using 27
years of ADT. We take advantage of this larger time series to build upon the
dispute of the reverse seasonal circulation in the bight (Sect. 2), as
reported in previous works, based on shorter SLA time series and
hydrographic data (Devis-Morales et al., 2008; Rodríguez-Rubio et al.,
2003). We find the northward Colombia Coastal Current (north of
∼4∘ N) and westward circulation in the Panama Gulf
as permanent features, with seasonal differences in their speed. The most
relevant seasonal difference is in the southwestward Panama Jet Surface
Current, which is strong in the first third of the year and vanishes during
the remaining months, when the circulation in the Panama Bight is weaker and
more variable (Fig. 4). Therefore, large seasonal differences are found in
the Panama Bight circulation, which we would not catalogue as a reverse
circulation characterized by an anticyclonic gyre during the boreal summer.
These results are in agreement with Chaigneau et al. (2006), whose
description of the Panama Bight circulation was based on 25 years of
satellite-tracked drifter trajectories.
The mean circulation in the ETP (east of 100∘ W) was analyzed from
the MDT altimetry product (1993–2012 period). West of ∼84∘ W, MDT is similar to the ETP circulation that dominates from
June to December in the ADT. In the Panama Bight, MDT shows the cyclonic
circulation from January to April seen in the ADT, when the Panama surface
wind jet dominates the region (Fig. 2). Therefore, the mean annual
circulation should be used with caution to represent the dominant seasonal
circulation in the Panama Bight.
The seasonal circulation assessment in the Panama Bight shows that from
January to April, a stronger cyclonic circulation responds to the ADT drop
(Fig. 4), produced by upwelling forced by the Panama surface wind jet,
which also reduces SST and increases SSS (Fig. 6). From June to December,
a weaker circulation responds to the ADT rise (density reduction), produced
by convergence of surface warm waters dragged by the Choco surface wind jet,
which also transport moisture inshore, enhancing precipitation and river
outflow, which reduces SSS. During this season, the Panama Jet Surface
Current is not observed, while it is >40 cm s-1 during the first
third of the year. Therefore, the Panama Bight has a strong seasonal
variation in SST, SSS, sea level and circulation. The variations in these
physical factors affect the biosphere, as chlorophyll a availability in the
basin is modulated by these changes (Corredor-Acosta et al., 2020). In the
ETP seasonal circulation variations do not change as much as in the Panama
Bight (Fig. 2), showing the zonal presence of the SEC and NECC west of
∼90∘ W as permanent features; although the
cyclonic Costa Rica Dome is also a permanent feature, its intensity varies
seasonally (Fig. 8).
We further assess the ENSO effect on seasonal circulation and sea level in the
study area using the coastal Niño 1+2 index. However, if the open
ocean Niño 3 index replaces this index, results are very similar. We
find that the seasonal SST, SSS, sea level and circulation patterns are not
largely modified by positive or negative ENSO phases (Fig. 6). This
coincides with small differences reported in the ETP mean surface
circulation due to ENSO, based on drifter trajectories (Chaigneau et al.,
2006). In contrast, the regionally averaged SST and
sea level of the ETP and Panama Bight are shifted by ENSO, with a mean SST and ADT increase in the
positive phase and a mean SST and ADT decrease in the negative phase. The
mean ADT (SST) difference between the two ENSO conditions is 12.9 cm (1.8 ∘C) in the ETP and 9.6 cm (0.9 ∘C) in the Panama
Bight. Still, seasonal ranges of these two variables in the Panama Bight (up
to 28.3 cm during El Niño and 2.2 ∘C under regular
conditions) dominate over the interannual ADT and SST shifts.
In the western–central tropical Pacific, La Niña (El Niño) reduces
(increases) freshwater flux, affecting interannual SSS variability (Zhang et
al., 2012). In contrast, SSS in the ETP is not affected by ENSO,
maintaining its strong seasonal cycle in the Panama Bight (range up to 2.6 g kg-1 during El Niño). To understand this result, we assess the
SST gradient between the warm Panama Bight and the Cold Tongue, as it
modulates the Choco surface wind jet (Poveda and Mesa, 2000). We speculate
that this small ENSO effect on SSS is due to a local compensating mechanism
in the Panama Bight (Sect. 3.4). ENSO increases the SST, which enhances
precipitation (reduces SSS) the entire year. This effect is compensated
differently in the two observed seasons. In the first third of the year,
ENSO affects the Panama wind jet strength, which enhances upwelling and
increases SSS. In the rest of the year, positive ENSO reduces the Choco wind
jet strength, decreasing inshore moisture transport and precipitation
(increasing SSS). Such complex mechanisms should be studied in more detail
as well as ENSO's influence on coastal precipitation and river runoff.
We use six spatial and temporal neurons obtained from SOM analysis to assess
the ETP circulation, as well as the ADT seasonal variability and its
relation to ENSO conditions. SOM results confirm the different seasonal
circulation patterns in the Panama Bight, which are not strongly affected by
ENSO. Results also support the strong ENSO effect on the ETP mean ADT,
increasing (decreasing) mean sea level during El Niño (La Niña).
The seasonal description of the circulation in the Panama Bight, as well as
the ENSO-related interannual variations, is useful to assess regional
fluctuations in ocean dynamics. These dynamic changes will have implications
for maritime activities such as navigation, but this seasonality and these interannual variations might also affect local climate through ocean–atmosphere
fluxes, determine biosphere cycles, force extreme sea levels and enhance
erosion, affecting coastal communities. For example, sea level extremes
that affect the coastal areas of Panama and Colombia in the
Pacific Ocean will increase their flooding probability in December under El
Niño conditions, when the monthly mean sea level is 18.2 cm higher than
the multiannual ADT mean. Therefore, the study of these air–sea
interactions and their temporal variations and relation with global warming in
the region should be encouraged.
Monthly averaged ADT anomalies (color scale in centimeters) and
associated geostrophic circulation (vectors) for the eastern tropical
Pacific (ETP). ENSO-related normal conditions (based on Niño 1+2).
ADT anomalies are computed subtracting the ETP regional mean during the
1993–2019 period (66.5 cm). The regional average of the ADT anomalies for
each month is shown in the upper right of each panel, coinciding with
the black line in Fig. A2a time series.
Monthly spatially averaged values in the eastern tropical
Pacific for 1993–2019, differentiating three ENSO-related conditions (based
on Niño 1+2) – normal (black), negative (blue) and positive (red) –
showing each time series annual mean. (a) Absolute dynamic topography (ADT)
anomalies in centimeters. (b) Geostrophic current speed in centimeters per second. (c) Sea
surface temperature (SST) anomalies in degrees Celsius. (d) Sea surface
salinity (SSS) anomalies in grams per kilogram. Anomalies are computed subtracting
the corresponding regional and temporal mean (value shown in the top of each
panel). (e) Monthly SST of the warm Panama Bight (PB) and the Cold Tongue
(CT) areas shown in Fig. 5. (f) Matrix indicating the monthly ENSO-related
condition between 1993–2019, based on the Niño 3 region (Sect. 2). Normal
months in white, negative ENSO in blue and positive ENSO in red.
Code availability
Software code is available upon request to the authors.
Data availability
The data sets are available from the corresponding authors on reasonable request.
Author contributions
RRT and AC conceived the idea of the study with the support of EG and CM.
IHC, AO and AC performed SOM assessment. All authors contributed to data
processing, figure preparation and analysis of results. RRT prepared the
manuscript with contributions from AO.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
The present research was carried out in the framework of the AEI
accreditation “Maria de Maeztu Centre of Excellence” given to IMEDEA
(CSIC-UIB) (CEX2021-001198). Alejandro Orfila acknowledges financial support from
the project LAMARCA (PID2021-123352OB-C31), funded by MICIN/AEI/FEDER and UE, and
from the project Tech2Coast (TED2021-130949B-I00), funded by MCIN/AEI and the EU “NextGenerationEU/PRTR”. Ismael Hernández-Carrasco is supported by the
TRITOP project (UIB2021-PD06), funded by the Universidad de las Islas
Baleares – FEDER (UE).
Financial support
This research has been supported by the Ministerio de Ciencia e Innovación (grant nos. PID2021-123352OB-C31 and TED2021-130949B-I00).We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI).
Review statement
This paper was edited by Karen J. Heywood and reviewed by two anonymous referees.
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