Masuda et al.
Progress in Earth and Planetary Science(2023) 10:38
https://doi.org/10.1186/s40645-023-00571-1

PAPER WITH FULL DATA ATTACHED

Progress in Earth andPlanetary Science

Open AccessPossible link between temperaturesin the seashore and open ocean waters of Peru
identified by using new seashore water data
Shuhei Masuda1* , Masato Kobayashi2, Luis Alfredo Icochea Salas3 and Gandy Maria Rosales Quintana3,4

Abstract
The linkage between environmental conditions in the coastal ocean and the open sea varies greatly by region. It
is important to clarify, on an area-by-area basis, what coastal monitoring information reveals about the open oceanand how much predictive information for the open ocean may be applicable to the coastal ocean. The Pacific
Ocean off the coast of Peru is a monitoring area for the El Niño/La Niña, an oceanic–atmospheric phenomenonof global importance. However, there are not many reliable data along the Peruvian coast. We deployed a networkof 6 logger sites along the Peruvian coast during 2017–2020 and compiled a useful, high-resolution dataset of watertemperatures. We examined a possible link between temperatures in the coastal waters of Peru and the open sea
by comparing the new dataset with historical temperatures in the open ocean. We confirmed that monthly mean
anomalies of seashore water temperatures in coastal Peru were strongly correlated with those of open ocean sea
surface temperatures. With one exception, the correlation coefficients ranged from 0.80 to 0.92 and were significant
at p < 0.01. This result suggested that data obtained from monitoring along the Pacific coast of Peru could be used
to indicate the state of the open ocean and that El Niño forecasts for the open ocean could be applied to coastal
forecasting as well. Spectral analysis revealed that the periods of changes of seashore water temperature peaked at 80
and 120 days in the region north of 5° S. This result suggested that coastal monitoring might capture intraseasonal
dynamics of equatorial Kelvin waves. The absence of clear peaks south of 5° S implied that equatorial wave energy did
not penetrate far into off-equatorial regions along the Peruvian coast on intraseasonal timescales.
Keywords Climate change, Peruvian coast, Water temperature, Observation data
1 Introduction
The ocean plays a major role in climatic phenomena that
occur on annual-to-decadal timescales. Among these
phenomena are El Niño events, during which a 2–3 °C*Correspondence:
Shuhei Masuda
smasuda@jamstec.go.jp
1
Research Institute for Global Change, Japan Agency for Marine-Earth
Science and Technology (JAMSTEC), Yokosuka, Japan
2
Yokohama College of Commerce, Yokohama, Japan3
Department of Fisheries, Universidad Nacional Agraria La Molina, La
Molina, Peru
4
Department of Ocean Science, Tokyo University of Marine Scienceand Technology, Tokyo, Japan

rise in sea surface temperature in the eastern tropical
Pacific Ocean may induce major changes in atmosphericcirculation that result in abnormal weather and natural
disasters on a global scale (e.g., Kovats et al. 2003; Philander 2004). An accurate understanding of oceanic variability is therefore critical to elucidating and predicting
medium- to long-term climate changes related to El Niño
events.
Agriculture, fisheries, and other socioeconomic activities are concentrated in coastal regions. Numerous
international programs launched since the end of thetwentieth century have conducted intensive oceanographic observations to accurately understand oceanographic changes. Those efforts have included the World© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.

Masuda et al. Progress in Earth and Planetary Science(2023) 10:38

Ocean Circulation Experiment (WOCE) and the global
deployment of Argo floats to monitor a wide range oftemperature and salinity changes in the ocean (e.g.,
Roemmich and Gilson 2009; Johnson et al. 2022). Tropical regions, in particular, have been intensively monitored to better understand the major global impacts ofthe El Niño/La Niña Southern Oscillation (ENSO) (e.g.,
McPhaden and Hayes 1989; McPhaden et al. 2019). However, climatic variations can differ greatly in the open
ocean and in coastal environments (e.g., Santos et al.
2012).
Previous observational studies of tropical climatechange have involved deployment of organized observation networks in the open ocean to capture phenomena on the scale of ocean basins. Most of the Argo float
and moored-buoy arrays have been deployed west of the
Galápagos Islands. Systematic oceanographic observations have been comparatively rare near the equatorial
coast of South America (for instance, the NINO1 + 2
region bounded by 0°–10° S and 90°–80° W), which is anarea closely associated with coastal influences of El Niño
events and with the socioeconomic activities of Peru.
However, few coastal/seashore datasets have beenorganized or published thus far, and the study of coastaland open ocean links has been constrained by the paucityof relevant data. The technical difficulty of monitoring
via remote sensing in coastal areas has also increased thevalue of publicly available in situ data.
In this study, we used autonomous logger instruments
to monitor water temperatures along the Peruvian coast
for three years to obtain for the first time systematic
observations at strategic locations along the northerncoast of Peru and to publish the data as an open dataset. This article is intended to describe the details of theobservations we have made and the basic properties ofthe data. In addition, we describe interannual changes
of water temperature in the seashore waters that wererevealed by the data, and we discuss the relationships
between these changes and corresponding changes in the
open ocean. In addition, we use catch data to shed somelight on the relationship between marine environmental
conditions and catches.

2 Construction and Content
2.1 New in situ monitoring temperatures

Seashore water temperatures were continuously monitored from 2017 to 2020 at six locations along the coastof Peru using data loggers with built-in thermometers(automatic temperature measurement recorders, HOBO
U22-001). These data loggers were installed at Mancora(4° 06′ S, 81° 04′ W), Cabo Blanco (4° 14′ S, 81° 13′ W),
Talara (4° 34′ S, 81° 16′ W), Paita (5° 04′ S, 81° 08′ W; also
known as Tierra Colorada), Chicama (7° 41′ S, 79° 26′

Page 2 of 12

Fig. 1 Data logger locations. Map showing locations of data loggers
deployed for this study in Peru (see Table 1)

Table 1 Logger and EN4 locations for study sites on the
Peruvian coast
Pier Name and location

Logger location

EN4 location

Lat. (S)Long. (W)

Lat. (S)Long. (W)

Mancora4° 06′

81° 04′

4°

81°

Cabo Blanco

4° 14′

81° 13′

4°

81°

Talara4° 34′

81° 16′

5°

81°

Paita

5° 04′

81° 08′5°81°

Chicama

7° 41′

79° 26′

8°

79°

Callao12° 04′77° 10′12°77°W), and La Punta in Callao (12° 04′ S, 77° 10′ W) (Fig. 1,
Table 1). The spatial intervals ranged from 20 to 50 km
between the first four of these loggers and around 500 km
between Paita, Chicama, and Callao. The water temperatures at depths of approximately 2–8 m were acquired at
15-min intervals at each location. To ensure a continuous
dataset, we replaced each logger every six months before
the internal memory of the logger was filled. The sensors
were calibrated using a thermostatic water bath at theJapan Agency for Marine-Earth Science and Technology.
The data can be freely downloaded for use from https://​www.​jamst​ec.​go.​jp/​goorc/​data/​peru_​smasu​da/. The dataconsist of six zip files, each of which contains approximately six months of logger data obtained from February
2017 to February 2020. Each folder contains files (TXT
and MS Excel formats) named after the location whereMasuda et al. Progress in Earth and Planetary Science(2023) 10:38

the data were collected. The files contain the dates, times,
sensor temperatures, and corrected temperatures.
2.2 Data used in analyses
2.2.1 Open‑ocean observation datasets

We relied on quality-controlled subsurface ocean temperatures from the EN4.2.1.f.analysis.g10.201812 (EN4)
dataset. We used temperatures at depths of 5.02 m and
207.4 m to represent temperatures at the surface and at
a depth of 200 m, respectively. EN4 has been compiledby the Hadley Centre of the UK Meteorological Office(Good et al. 2013) and is based on the World Ocean
Database 2009 (WOD09) along with Argo float data fromthe Argo Global Data Assembly Centers (GDACs) andquality-controlled, complementary data from the UnitedStates National Oceanographic Data Center compiled by
the Arctic Synoptic Basin-wide Oceanography project
and the Global Temperature and Salinity Profile Program. Details of the EN4 dataset have been summarized
by Good et al. (2013) and references therein (e.g., Ingleby
and Huddleston 2007; Gronell and Wijffels 2008; Guinehut et al. 2009). It should be noted that the logger data
and EN4 dataset are completely independent; that is, theEN4 dataset does not contain any logger data and viceversa.
We identified variations of the 15 °C and 20 °C isotherms in the open ocean by using the National Oceanicand Atmospheric Administration (NOAA) daily subsurface temperature dataset from mooring buoys of the
TAO/TRITON array at 0° N, 110° W (at depths of 1, 20,
40, 60, 80, 100, 120, 140, 180, 300, and 500 m) and 0° N,
95° W (at depths of 1, 5, 10, 15, 20, 25, 40, 45, 60, 80, 100,
120, 140, 180, 300, and 500 m).

Page 3 of 12(e.g., McPhaden and Yu 1999). The ocean conditions inthis area are complex. Figure 2 shows the climatologicaldistribution of sea surface temperature and two cross sections of subsurface water temperature compiled from theEN4 dataset. A two-layer structure is evident in this area.
The main thermocline is on approximately the 15 °C isotherm, and upwelling occurs near the coast in responseto the permanent easterly trade winds. This upwelling
brings up nutrients from the deep sea to the surface
and makes the coastal waters a rich fishing ground (e.g.,
Chavez et al. 1998; Chavez and Messié 2009; Qin et al.
2016; Espinoza-Morriberón et al. 2019). Under the influence of this structure of the water temperature and sea
surface pressure, the current system consists of a deep
eastward Equatorial Undercurrent and its subsurface
branches (e.g., Lukas 1986; Johnson et al. 2002; Montes
et al. 2010; Kuntz and Schrag 2018; Rosales Quintana
et al. 2021) and surface currents that are dominated byan eastward Equatorial Countercurrent off the equator in
the northern hemisphere and a westward South Equatorial Current in the southern hemisphere side. The subsurface Peru–Chile Undercurrent flows poleward along
the coastline in the Southern Hemisphere. The equatorial
waves that propagate with the El Niño/La Niña phenomenon transfer energy eastward in equatorial Kelvin waves(mostly changes in potential energy resulting from vertical movement of the main thermocline, e.g., Goddard
and Philander 2000), transfer energy westward via equatorial Rossby waves, and transfer energy meridionally via
coastal Kelvin waves that travel from the equatorial bandto subtropical regions along the continental coastline.

4 Results and discussion
4.1 Monthly data features

2.2.2 Fisheries data

For comparison with the daily seawater temperaturedataset, we obtained a dataset of daily anchovy landings
from the Peruvian Sea Institute (IMARPE) from November 2019 to February 2020. Data were available only for
Chicama and Callao.
Fishing vessels in Peru are classified as small artisanal
vessels (capacity < 32.6 ­m3), small-scale vessels (capacity < 32.6 ­m3), and large-scale vessels (capacity > 32.6 ­m3).
Data were not available for the first class of vessels, which
rely only on manual techniques. We estimated the total
landing as the sum of the catch landed by small-scale andlarge-scale vessels.

3 Regional settings
Our study area is at the eastern edge of the equatorial
Pacific, where the equatorial Kelvin waves involved in thedevelopment of El Niño events change their morphology

Here we analyze how consistent the monthly averagesof our new data were with existing open ocean data. We
compiled separate datasets that consisted of monthly
arithmetic average temperatures and daily mean temperatures. We applied a 48-h filter to the data to remove thediurnal tides before making the compilations (Thompson
1982).
The monthly average logger data feature interannual
variability with amplitudes comparable to the typicalannual variability, which exhibits a peak around 26 °C
in February–March (Fig. 3a). The calibrated values of
the water temperature tended to be higher closer to theequator; from north to south these were 23.5 ± 1.6 °C at
Mancora, 21.1 ± 1.6 °C at Cabo Blanco, 20.0 ± 2.5 °C at
Talara, 18.7 ± 2.8 °C at Paita, 17.3 ± 2.1 °C at Chicama, and16.6 ± 1.4 °C at Callao. At Chicama and Callao, seasonaland spatial variations were relatively small.
The typical annual variability in the logger records
consists of higher temperatures during austral summerMasuda et al. Progress in Earth and Planetary Science(2023) 10:38

Page 4 of 12

Fig. 2 Schematic view of the climatological ocean state off Peru. The distribution of sea surface temperature and cross sections of subsurface watertemperature from the EN4 dataset during the period 2017–2020. The white arrows indicate major currents, and the magenta arrows denote oceanic
waves related to ENSO energetics(December–February) than during winter (June–August).
This pattern is clearer in the four southernmost locations
from Talara to Callao (4° S to 12° S), partly because they
are farther from the variations of the Equatorial Front,
which is generally located north of Paita (e.g., Fiedler andTalley 2006; Grados et al. 2018). The monthly anomalies
from this seasonal pattern, expressed as standard deviations, were similar at the six sites: 0.8 °C for Mancora,
1.0 °C for Cabo Blanco, 1.1 °C for Talara, 1.5 °C for Paita,
1.5 °C for Chicama, and 0.9 °C for Callao (Fig. 3b). The
average ratio of interannual to seasonal variation (anomaly values vs seasonal variation) of 0.79 indicates that thesurface water temperature reflected almost equivalent
forcings of the annual cycle and interannual variability.
The EN4 water temperatures and their anomalies forthe locations nearest to the loggers (Table 1) are shownin Fig. 3c and d, respectively. It should be noted thatthe 1-degree spatial resolution of the EN4 dataset did
not resolve some of the closely spaced logger points(Fig. 4). The water temperatures at the southernmost
EN4 point, corresponding to Callao (12° S, 77° W),
were lower than the temperatures at the rest of the EN4
points. Although the temperatures at the six loggers
fluctuated around the same range of 16–28 °C, theirmeans differed from those in the EN4 dataset, whichwere 21.1 °C at Mancora and Cabo Blanco (in the 4° S,
81° W cell), 20.6 °C at Talara and Paita (in the 5° S, 81°
W cell), 20.8 °C at Chicama (in the 8° S, 79° W cell), and19.6 °C at Callao (in the 12° S, 77° W cell). The standard
deviations ranged between 2.6 and 3.0 °C.
Each temporal evolution of the EN4 dataset was similar to that of the logger data. The temperature anomalies from the seasonal cycle were 1.2 °C at Mancora
and Cabo Blanco, 1.2 °C at Talara and Paita, 0.9 °C at
Chicama, and 1.1 °C at Callao (Fig. 3d). The low average ratio of interannual to seasonal variations of theanomaly values (0.49) was due to the relatively large
annual variations in the EN4 dataset. Because EN4 is anMasuda et al. Progress in Earth and Planetary Science(2023) 10:38

Page 5 of 12

Fig. 3 Time series of monthly surface water temperature (SWT) at six sites along the Peruvian coast. a Calibrated seashore temperaturefrom the loggers, b seashore temperature anomalies from logger data, c sea surface temperatures from the EN4 dataset, and d seashore
temperature anomalies from the EN4 dataset

Fig. 4 Climatological sea surface temperature distribution. Map
showing the climatological sea surface temperature distribution
on the EN4 spatial grid at 1° resolution during 2017–2020. The white
rectangle outlines the NINO1 + 2 region

integrated analysis dataset with one-degree grid cells,
we infer that the seasonal component is somewhat
accentuated in areas with sparse data.
The remainder of this analysis focuses on the anomaly
values, which are plotted for each logger site in Fig. 5.
There were strong correlations between EN4 and logger data for five of the six locations. The correlation
coefficients ranged from 0.80 to 0.92 (p < 0.01). At Mancora, the northernmost site, the lack of statistical significance of the correlation coefficient of 0.26 (p < 0.10)
was perhaps due to the fact that the highly variableinteractions of the Equatorial Front off northern Peru
are not well represented in EN4. For the other logger
sites, the monthly average temperatures appeared to
be well synchronized with the NINO1 + 2 sea surface
temperature anomalies (Fig. 5g). It is interesting to notethat the monthly anomalies of the logger data were also
highly correlated with the coastal tide gauge data (0.77
at Talara and 0.70 at Callao, figures not shown). TheseMasuda et al. Progress in Earth and Planetary Science(2023) 10:38

Page 6 of 12

Fig. 5 Time series of monthly surface temperature anomalies. Anomalies at a Mancora, b Cabo Blanco, c Talara, d Paita, e Chicama, and f Callao. g
NINO 1 + 2 sea surface temperature anomaly from EN4 dataset

results recall close link to the results of the previous
researches (Clarke 1992; Jigena-Antelo et al. 2023).
We investigated the spatial correlation between the logger data and the EN4 dataset on a monthly and seasonalbasis. We used only the northernmost (Cabo Blanco) andsouthernmost (Callao) logger sites to accommodate thecoarser spatial resolution of the EN4 dataset.
The correlation diagram for the monthly surface temperature anomaly at Cabo Blanco shows the well-known
El Niño spatial pattern (Fig. 6b). In the diagram for the
subsurface (200-m depth), the fact that the area of strong
correlation was more concentrated in the equatorial wave
guide (Fig. 6e) reflects the strong influence of subsurface
equatorial waves and the contribution of the Equatorial
Undercurrent, which bifurcates in the Galápagos Islands
and later feeds the Peru–Chile Undercurrent along the
Peruvian coast (Montes et al. 2010; Rosales Quintana
et al. 2021).
In the correlation diagram for the seasonal component,
the surface temperature seemed to be determined byatmospheric forcing from solar insolation. The northernand southern hemispheres, as expected, were strongly
differentiated (Fig. 6c). At a depth of 200 m, the muchless distinct seasonal cycle reflected more complex
causes (Fig. 6d). In sum, the variations of monthly surface
temperature (Fig. 6a) reflected both subsurface equatorial
wave dynamics and surface thermal forcing. The former
tended to be subject to interannual fluctuations, whereas
the latter was characterized by seasonal fluctuations.
These patterns are by and large consistent with the factthat the ratio of interannual to seasonal variations ranged
from 0.49 to 0.79 (vide supra). The spatial patterns of thesubsurface temperatures and their anomalies were nearly
identical (Fig. 6d and e).
The correlation diagrams for the Callao site were similar to those for Cabo Blanco, except that the patterns ofmonthly anomalies for surface and deep water exhibiteda decay phase during El Niño (Fig. 7b and e). This finding was consistent with the propagation of coastal Kelvin waves from north to south and was corroborated by

Masuda et al. Progress in Earth and Planetary Science(2023) 10:38

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Fig. 6 Spatial correlation of the monthly water temperature. Maps showing spatial correlations (correlation coefficients) of monthly watertemperature between Cabo Blanco (81° W, 4° S) and the Pacific basin from the EN4 dataset for 2010–2019 in relation to a surface temperatures, b
surface anomalies, c surface seasonal cycle, d temperatures at 200 m depth, e anomalies at 200 m depth, and f seasonal cycle at 200 m depth

Fig. 7 Spatial correlation of the monthly water temperature. Same as Fig. 6 but for Callao (77° W, 12° S)

Masuda et al. Progress in Earth and Planetary Science(2023) 10:38

Page 8 of 12

Fig. 8 Spatial lag correlation of the monthly water temperature. Same as Fig. 7b but for correlations with lags from Pacific basin data of a 3 months,
b 1 month, and c 0 month (same as Fig. 7b)

diagrams incorporating a one- and three-month time lagthat showed the well-known ENSO sequence (Fig. 8). The
sea surface temperature anomaly in Callao led the distribution of SST anomalies shown in Fig. 8a and b by threemonths and one month, respectively. The implication isthat sea surface variations in Callao can be partly traced
back to equatorial Kelvin waves associated with ENSO
events. Similarly, Rosales Quintana et al. (2021) havereported that the development of an anomalous flowof the Equatorial Undercurrent in the western Pacific(related to ENSO) may be detectable from the easternmost Pacific, where a strongly correlated signal can bedetected between 30 and 35 days later in the Galápagos
Islands.
Our results suggest that the monthly anomaly of thecoastal surface temperature is dominated by El Niño.
This scenario is consistent with the understanding fromprevious studies of this area, and the changes of watertemperature obtained with the new observations werepartly validated on the basis of monthly averages.

Fig. 9 Time series of daily seashore water temperatures at four
Peruvian coastal sites

4.2 Daily data features

To demonstrate the utility of our data, we present here
an analysis of daily average data. We applied a 48-h filter
to the data to remove the signal from diurnal tides before
making the daily compilations (Thompson 1982).
There are many possible sources of variation in dailymean temperature in the ocean, and our data alone cannot be used to identify the dynamics. However, we here
attempt to make a comparison with equatorial waves as
one of the possible causes. We subjected the daily mean
temperature data from four of the six logger sites (Fig. 9)
to a spectrum analysis. The results for the two northernsites, Mancora and Cabo Blanco, showed two clear peaks
at periods of 80 and 120 days (Fig. 10). Those peaks had
characteristics similar to those of the second and firstbaroclinic equatorial Kelvin waves with periods of 70 and
120 days, respectively, reported by Cravatte et al. (2003).

Fig. 10 Spectral analysis of the daily logger data shown in Fig. 9.
Each color denotes Mancora, Cabo Blanco, Chicama, and Callao,
respectively

The frequency shift from 70 to 80 days may be due to thedifferent time periods analyzed by that study (the 1990s)
and this one (e.g., Dewitte et al. 2008). However, theMasuda et al. Progress in Earth and Planetary Science(2023) 10:38

cause of this shift should be addressed by comprehensive
analysis using reanalysis data rather than by speculation.
In contrast, the data for Chicama and Callao did notshow these peaks (Fig. 10). The implication of this discovery, that shorter-period equatorial waves may not leakbeyond the equator, provides insight that may enhance
understanding of the overall ENSO energy budget andthe influences of ENSO on off-equatorial to mid-latitude
oceanic changes.
4.3 Comparison with fisheries data

Coastal Kelvin waves and related changes in water
properties can affect the principal fisheries in Peru
through their influence on fish migration. Figure 11
shows anchovy landings along with daily temperature at the Paita, Chicama, and Callao piers. At the
Paita pier (also known as the Tierra Colorada pier),
water temperature rapidly increased by more than 6 °C
(from 15.2 to 21.8 °C) from 13 to 18 November 2019.
Although there are no catch data at this pier because

Page 9 of 12

the anchovy fleet did not operate near Paita, similar
temperature changes occurred, with decreasing amplitude and a time lag of 5–8 days, at the two sites southof Paita that may have been related to coastal Kelvinwaves. At the Chicama pier (350 km south of Paita),
an abrupt increase of water temperature to about 20 °C
began on 25 November, and the total daily anchovy
catch drastically decreased from around 15,000 t to lessthan 3,000 t. The persistence of warm water temperatures until 17 January may has been related to reduced
daily landings during that period. At the Callao pier(400 km south of Chicama), there was a small temperature increase that began on 28 November. The temperature reached 16.8 °C without a clearly related change
in landings. The estimated speed of propagation of thesignal was 0.51 m/s from Paita to Chicama and 1.15 m/sfrom Chicama to Callao. Because the speed of propagation of a coastal Kelvin wave would generally be around
0.4 m/s (Chelton and Davis 1982), a detailed assessment that took into account factors such as topography

Fig. 11 Fishery data plot. Time series of daily anchovy landings and daily water temperatures at a Paita, b Chicama, and c Callao piersfrom November 2019 to February 2020

Masuda et al. Progress in Earth and Planetary Science(2023) 10:38

and stratification would be necessary to reveal the true
nature of this “propagation”.
It may be worth noting that an increase in the proportion of juveniles at the Callao pier started in January, when the fishing season was closed by the Peruvian
authorities (IMARPE 2020). One cause of the abrupt
increase of water temperature in November 2019 may

Page 10 of 12

have been the changes in the open ocean that wereobserved in the preceding months (Fig. 12). The subsurface water temperatures at 0° N, 110° W, and 0° N, 95° W
reveal a deepening of the 15 °C and 20 °C isotherms thatsuggests the passage of a Kelvin wave in October 2019that reached the Peruvian coast in November. It was firstobserved at 0° N, 110° W on 9 October and reached theFig. 12 NOAA Daily subsurface temperatures at a 0° N, 110° W and b 0° N, 95° W from June 2019 to March 2020. Magenta lines indicate the 15 °C
and 20 °C isotherms. The white bars in (a) and (b) denote 9 October and 22 October, respectively

Masuda et al. Progress in Earth and Planetary Science(2023) 10:38

Page 11 of 12

buoy at 0° N, 95° W on 22 October (white dashed bars in
Fig. 12). Because this single example cannot be the basis
for any firm conclusions, it will be increasingly necessary
to acquire and publish data that can be used to accumulate such examples.

analyzed the data. GMRQ supported seashore observations, analyzed the data,and helped in their interpretation. All authors read and approved the final
manuscript.

5 Conclusions
We installed new loggers to monitor seawater temperatures at six coastal sites in Peru from 2017 to 2020. The
data are open and available in a well-organized, free dataset. This valuable dataset has resulted from high-resolution monitoring of Peruvian coastal waters.
Use of this dataset enabled us to confirm that coastal
surface water temperatures were closely linked tomonthly temperature anomalies and aspects of El Niño inthe open ocean. The temperature anomalies at the southern coastal sites lagged those at northern coastal sites by
about one month. This difference may be attributable tothe passage of coastal Kelvin waves.
The high temporal resolution of our monitoring system may enable detection of equatorial Kelvin waves andshorter-term oceanic phenomena. The demonstration by
our data that seashore water temperatures exhibited variability with periods of 80 and 120 days in the northern(equatorial) part of the study area suggests that equatorial waves may not penetrate far into higher latitudes on
sub-seasonal timescales. Nevertheless, it is not possible
to draw general conclusions from an analysis of a shortperiod of only three years, when the cold ENSO phase
was dominant.
There are many uncertainties in our knowledge ofthe area on the eastern boundary of the Pacific Ocean
because of insufficient observation coverage. The kindof organized network of observations along the Peruvian
coast described herein and publication of the reported
data may greatly enhance that knowledge.

Availability of data and materials
The logger data supporting the conclusions of this article are available
at https://​www.​jamst​ec.​go.​jp/​goorc/​data/​peru_​smasu​da. The buoy data
are available at https://​www.​pmel.​noaa.​gov/​gtmba/​pmel-​theme/​pacif​
ic-​ocean-​tao.

Abbreviations
ENSO	El Niño/La Niña Southern Oscillation
GDAC	Argo Global Data Assembly Centre
IMARPE	Peruvian Sea Institute
JAMSTEC	Japan Agency for Marine-Earth Science and Technology
NOAA	National Oceanic and Atmospheric Administration
WOCE	World Ocean Circulation Experiment
WOD09	World Ocean Database 2009
Acknowledgements
We are grateful to the National Fund for Fisheries Development (FONDEPES),
the Hydrographic Department of the Peruvian Navy (DIHIDRONAV), thePeruvian Sea Institute, and Industrial Pesquera Santa Monica for permission
to install data loggers inside their facilities. Our work benefited from support
by Dr. H. Uchida with respect to calibration of the sensors we used. Numericalcalculations were performed on the Data Analyzer of JAMSTEC.
Author contributions
SM conceived and designed the study. MK proposed the topic and carriedout seashore observations. LAIS supported seashore observations andFunding
This work was partly supported by a Japan Society for the Promotion of Science KAKENHI Grant 17H04579, JP22H05207 and by the Japan Science andTechnology Agency, AIPTrilateral AI Research Grant Number JPMJCR20G5.

Declarations
Competing interests
The authors declare that they have no competing interest.
Received: 25 July 2022 Accepted: 7 July 2023

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