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Remarkably heavy and devastating rainfalls affected large parts of Peru during the austral summer 2016–2017. These rainfalls favoured widespread land sliding and extensive flooding and generated one of the most severe disasters of Peru since the 1997–1998 El Niño event. The amount of rainfall recorded between January and March 2017 only compares to the biggest El Niño events of the last 40 years (i.e. 1982–1983 and 1997–1998) and exceeded the 90th percentile of available records (1981–2017) over much of the northern and central coasts of Peru, the Andean region and Amazonia. The occurrence of these heavy rainfalls was highly anomalous as it occurred during the first austral summer following the development and decay of a very strong El Niño in 2015–2016. Here, we propose that the likely cause of the anomalous rainfalls is linked to the combination of an especially intense wet spell over the Central Andes related to a deep, long-lasting anticyclone located adjacent to the Chilean coast, and to the unusual development of warm water off the coast of Peru in the nominal El Niño 1 + 2 region. This warming has been related to an anomalous weakening of the mid-upper level subtropical westerly flow, which in turn led to a weakening of the southeasterly trades off the coast, thus hindering the upwelling near the Peruvian coast and favoring the eastern Pacific warming. This development is counter to the usual evolution of sea surface temperature in the eastern equatorial Pacific following very strong El Niño events, such as those occurred in 1982–1983, 1997–1998, and 2015–2016. This paper explores the unusual nature of this event in the observational record and illustrates its consequences.
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1 3
Climate Dynamics
The anomalous 2017 coastal El Niño event inPeru
C.Rodríguez‑Morata1· H.F.Díaz2· J.A.Ballesteros‑Canovas1,3· M.Rohrer1,4· M.Stoel1,3,5
Received: 20 March 2018 / Accepted: 22 September 2018
© Springer-Verlag GmbH Germany, part of Springer Nature 2018
Remarkably heavy and devastating rainfalls affected large parts of Peru during the austral summer 2016–2017. These rainfalls
favoured widespread land sliding and extensive flooding and generated one of the most severe disasters of Peru since the
1997–1998 El Niño event. The amount of rainfall recorded between January and March 2017 only compares to the biggest
El Niño events of the last 40years (i.e. 1982–1983 and 1997–1998) and exceeded the 90th percentile of available records
(1981–2017) over much of the northern and central coasts of Peru, the Andean region and Amazonia. The occurrence of
these heavy rainfalls was highly anomalous as it occurred during the first austral summer following the development and
decay of a very strong El Niño in 2015–2016. Here, we propose that the likely cause of the anomalous rainfalls is linked to
the combination of an especially intense wet spell over the Central Andes related to a deep, long-lasting anticyclone located
adjacent to the Chilean coast, and to the unusual development of warm water off the coast of Peru in the nominal El Niño
1 + 2 region. This warming has been related to an anomalous weakening of the mid-upper level subtropical westerly flow,
which in turn led to a weakening of the southeasterly trades off the coast, thus hindering the upwelling near the Peruvian coast
and favoring the eastern Pacific warming. This development is counter to the usual evolution of sea surface temperature in
the eastern equatorial Pacific following very strong El Niño events, such as those occurred in 1982–1983, 1997–1998, and
2015–2016. This paper explores the unusual nature of this event in the observational record and illustrates its consequences.
Keywords Coastal El Niño· Sea surface temperature· Rainfalls· Flooding· Peru
1 Introduction
During the 2016–2017 austral summer, Peru experienced
widespread and unusually heavy rainfall, which triggered
landslides and severe flooding, thereby leading to cata-
strophic damage to housing and infrastructure, and affect-
ing more than 6,60,000 people, and leaving more than 100
deaths (INDECI 2017). The areas most affected by the dis-
aster were located in the northern and central coastal regions
of Peru (Fig.1), as well as southeast of the Loreto region in
the Peruvian Amazonas basin. In addition to the death tolls
and physical injuries, these heavy rainfalls also resulted in
increased exposure of local populations to disease pathogens
and mental health problems associated with loss, disrup-
tion, and displacement (Hales etal. 2003; Waring and Brown
2005; Few 2007). In the context of the 2016–2017 extreme
rainfall season 6270 dengue cases were confirmed in Peru
(PAHO 2017), which triggered the declaration of a sanitary
emergency by the Ministry of Health (MINSA) in seven
regions of Peru. The heavy rainfalls of the 2016–2017 aus-
tral summer were highly anomalous because they occurred
Electronic supplementary material The online version of this
article (https :// 2-018-4466-y) contains
supplementary material, which is available to authorized users.
* C. Rodríguez-Morata
1 Climate Change Impacts andRisks intheAnthropocene
(C-CIA), Institute forEnvironmental Sciences, University
ofGeneva, Boulevard Carl-Vogt 66, 1205Geneva,
2 Department ofGeography andEnvironment, University
ofHawai‘i atManoa, Honolulu, USA
3, Department ofEarth Sciences, University
ofGeneva, Rue des Maraîchers 13, 1205Geneva,
4 Meteodat GmbH, Technoparkstrasse 1, 8005Zurich,
5 Department F.A. Forel forAquatic andEnvironmental
Sciences, University ofGeneva, Boulevard Carl-Vogt 66,
1205Geneva, Switzerland
C.Rodríguez-Morata et al.
1 3
during the first austral summer following the development
and decay of a very strong El Niño in 2015–2016. The rain-
fall recorded between January and March 2017 exceeded
the 90th percentile of available measurements for the period
1981–2017 over much of the northern and central coasts
of Peru, the Andean region and Amazonia. This event is
only comparable to the rainfall measured during the larg-
est El Niño events of the last 40years (i.e. 1982–1983 and
1997–1998). Interestingly, in these areas, rainfall totals dur-
ing the 2016–2017 austral summer exceeded those recorded
during the 2015–2016 El Niño event. The 2015–2016 El
Niño has been classified as a strong event by the interna-
tional community, even if it has had much less dramatic
consequences for Peru than the strong El Niño events in
1972–1973, 1982–1983, and 1997–1998 (L’Heureux etal.
2017; Sanabria etal. 2018).
Rainfall during the 2016–2017 austral summer occurred
in conjunction with largely negative (positive) SST anoma-
lies in the central (eastern) equatorial Pacific off the coast
of South America. Based on the monthly “ENSO Diag-
nostic Discussion Archive” from the Climate Predic-
tion Center (CPC 2018a), below normal SST anomalies
were registered in the Niño 3.4 region for November and
December 2016, suggesting a developing La Niña event
(NOAA 2017). The Oceanic Niño Index (ONI) however
extends La Niña status from July–September 2016 to
November-January 2017 (CPC 2018b). However, during
January 2017 and extending through March 2017, a strong
Fig. 1 Geographical distribution
of regions and rain gauges used
in this study. A digital elevation
model shows the topographical
characteristics and altitudes of
The anomalous 2017 coastal El Niño event inPeru
1 3
positive SST anomaly was observed in the 1 + 2 region,
leading to an event which has previously been referred to a
“Coastal El Niño” (Takahashi and Martínez 2017; hereaf-
ter CEN-2017). Therefore, a watch status was put in place
at the end of January 2017, and an alert status was issued
between February and May 2017 (ENFEN 2017a, b).
The observed SST anomaly event in the equatorial
Pacific during the 2016–2017 austral summer was highly
atypical because it occurred during the austral summer
season, and at a time when the 2015–2016 El Niño event
was in its last phase of decay (L´Heureux etal. 2017). The
situation in early 2017 differed substantially from previous
summers following strong El Niños in terms of the spatio-
temporal SST pattern. Thus, the 2016–2017 rainfall event
cannot be classified with classical ENSO definitions (i.e.
Rasmusson and Carpenter 1982; Ashok etal. 2007; Kug
etal. 2009; Yeh etal. 2009; Kao and Yu 2009; Takahashi
etal. 2011; Yu etal. 2011). The reasons for this may be
related to the fact that (1) the 2016–2017 austral sum-
mer event was not a clearly coupled ocean–atmosphere
phenomenon in the equatorial Pacific (Garreaud 2018),
and that (2) the warming was not present throughout the
Equatorial Pacific Basin.
The ENSO cycle may present strong variability in terms
of its amplitude, temporal evolution, and spatial pattern
(Larkin and Harrison 2005; Ashok etal. 2007; Takahashi
etal. 2011; Dommenget etal. 2013; Capotondi etal. 2015).
For instance, Takahashi and Martinez (2017) describe a pre-
vious coastal event in 1925 as the result of cold conditions in
the western-central equatorial Pacific that would have helped
to destabilize the ITCZ and generate strong northerly winds
across the equator. More recently, Garreaud (2018) devel-
oped a first approximation to the origin of the CEN-2017
providing a reasonable description of the atmospheric forc-
ing involved in the origin of the coastal SST overwarming.
This author proposed that during the recent coastal El Niño,
an external forcing provided by vacillations in the free-trop-
ospheric subtropical westerlies would have led to a weaken-
ing of the SE trades at lower levels and consequently to the
increase of coastal SST.
In this paper, we take advantage of the information con-
tained in these previous works to extend our understanding
about (1) the atmospheric and oceanic context during the
2016–2017 austral summer in Peru, and (2) the impacts
it has had on the magnitude, distribution, and timing of
extreme rainfalls that occurred over Peru. To this end, we
first describe the CEN-2017 event and related impacts in Peru
before we explore how different this event was compared to
others in the historical record. We use different reanalysis
data for SST and atmospheric variables as well as gridded
and observational rainfall data sources to contextualize the
2016–2017 austral summer precipitation and to describe the
spatio-temporal evolution of the event during this period.
2 Data andmethods
2.1 Precipitation analysis
We used rainfall information from 338 Peruvian gauge
stations, provided by the National Service of Meteorol-
ogy and Hydrology of Peru (SENAMHI), the MeteoDat
portal (Schwarb etal. 2011) as well as gridded data from
the GPM IMERG dataset (Huffman 2015) to characterize
the spatial distribution and intensity of accumulated rain-
fall during the 2016–2017 DJFM austral summer. Gauge
stations are distributed all along the country but major
density is founded in the most populated coastal regions
(Fig.1). Yet, rainfall records of these stations are not
always continuous and the number of gauge stations with a
full record for each month can vary substantially. Gridded
satellite data from GPM IMERG has a spatial resolution
of 0.1° × 0.1° but only covers the period between 2014 to
the present (extended information is available at https :// .php?q=GPM). In fact, it is possible
that observed values from gauge stations can be different
from GPM Imerg values for the same location. This is due
to (1) the inherent error associated with the precipitation
model (Huffman etal. 2017) and (2) the 0.1° × 0.1° spatial
resolution of the GPM data, meaning that rainfall values
are in fact average values of an area of 121km2 for which
differences in precipitation must be expected at more local
To provide further context for the magnitude of the pre-
cipitation event, we have used gridded data (1° × 1°) from
GPCC Monitoring Product (Schneider etal. 2015) as well
as observational data from 73 Peruvian gauge stations cov-
ering the entire period (1982–2017), (i.e. 17 gauge stations
for the month of December; 40 for January; 24 for Febru-
ary and 34 for March). With this data base, we have com-
puted rank percentile maps of precipitation to compare the
2016–2017 DJFM monthly precipitation with rainfall data
for the same period over the last 36years (1982–2017).
2.2 Large‑scale atmospheric andoceanic synoptic
analysis ofthe2016–2017 coastal event
The domain of the synoptic analyses covers the entire
South American continent and part of the East Pacific and
West Atlantic oceans and is large enough to capture the
main features of climate variability over South America
[i.e., the Bolivian High (BH), the South American mon-
soon (SAH), Intertropical Convergence Zone (ITCZ), and
the South Atlantic Convergence Zone (SACZ); Garreaud
etal. 2009]. At high levels (i.e., 200hPa), the Bolivian
High (BH) is the major synoptic feature occurring during
C.Rodríguez-Morata et al.
1 3
austral summers (Lenters and Cook 1997). It consists in
a closed anticyclone developing over the central Andes,
which forms in response to latent heat released during
deep convection over the Amazon basin (Sulca etal.
2016).To describe the CEN-2017, we have used daily
data of geopotential height, wind velocity components,
outgoing longwave radiation (OLR) and sea surface tem-
perature (SST) from the NCEP/NCAR reanalysis (Kalnay
etal. 1996) and provided by the NOAA/OAR/ESRL PSD
( This data is available on
a 2.5° × 2.5° grid at 17 pressure levels. Anomalies refer to
the 1981–2010 climatology. Outgoing long-wave radiation
(OLR) anomalies were used as a proxy for convective pro-
cesses (NCAR 2014). Negative (positive) OLR anomalies
are indicative of enhanced (suppressed) convection and
hence more (less) cloud coverage. More (less) convec-
tive activity in the central and eastern equatorial Pacific
implies higher (lower), colder (warmer) cloud tops, which
emit much less (more) infrared radiation into space.
Analysis of the dominance and changes of synoptic pat-
terns over time between December 2016 and March 2017 (at
the daily scale) was performed with Self Organizing Maps
(SOMs; Kohonen 2001; Hewitson and Crane 2002; Reusch
etal. 2005a, b, 2007; Cassano etal. 2006a). To this end, we
have used geopotential and zonal wind anomalies at 200,
500, and 850hPa as well as daily SST data, with a spatial
resolution of 2.5° × 2.5°, from the ERA-Interim reanalysis
climate data set (ECMWF 2016) with a time span from 1979
to present (Dee etal. 2011). ERA-Interim data was retrieved
from http://www.ecmwf .int/resea rch/era.
2.2.1 Self‑organizing maps
Self-organizing maps (SOM) represent a clustering tech-
nique that allows summarizing large, high-dimensional
records by treating data as a continuum. SOM identify pat-
terns using an iterative clustering algorithm (Hewitson and
Crane 2002), and produce a set of nodes (i.e., generic syn-
optic states directly interpretable as physical process states)
in a two-dimensional lattice with similar states close to each
other and the most extreme states at the opposite corners.
Analyses of these nodes allow the characterization of the
frequency of each synoptic state, the spatio-temporal transi-
tions between states as well as their dominance in a given
temporal horizon (Kohonen 2001; Hewitson and Crane
This technique has been used successfully in many
meteorological, climatological, and oceanic research
applications worldwide, either to characterize extreme
weather and rainfall events (Hong etal. 2005; Cassano
etal. 2006a, b; Morata etal. 2006; Zhang etal. 2006;
Uotila etal. 2007; Schuenemann etal. 2009), including in
the Peruvian Amazon and Andes regions (Espinoza etal.
2012, 2013; Paccini etal. 2017; Rodriguez-Morata etal.
2018), to visualize synoptic weather patterns over a region
(Hewitson and Crane 2002; Reusch etal. 2005a, b, 2007;
Johnson etal. 2008; Seefeldt and Cassano 2008; Wiseand
Dannenberg 2014), or to evaluate Global Climate Model
(GCM) results (Lynch etal. 2006; Cassano etal. 2007;
Skific etal. 2009a, b).
Initially, SOMs are formed by an arbitrary number of
clusters or nodes. Each cluster is associated with two vec-
tors. The first vector describes the position of the cluster
on the lattice, whereas the second (also referred to as ref-
erence vector) represents the position of the cluster cen-
troid in the data space. By using an iterative process, an
unsupervised algorithm is applied to adjust the reference
vectors representing the nodes based on the differences
between the reference vectors and each input value. In
each iteration, the Euclidean distance between input data
and reference vectors is calculated and the best match-
ing reference vector is identified for each input record.
Neighboring reference vectors of each best match are then
updated to result in adjacent nodes having the strongest
similarity. Iterations are ended when stable values of the
reference vectors are reached. The choice of the number
of nodes depends on the specific research context and
amount of data. Generally, smaller (larger) number of
nodes implies less (more) possibilities to characterize the
high-dimensional data space and therefore more (less)
generalization of the input data.
To analyze the 2016–2017 austral summer event, we
defined a 5 × 5 nodes lattice to best discriminate the main
synoptic South American summertime features including
those patterns associated with ENSO (positive and nega-
tive phases). The SOM analysis was carried out with the
MeteoLab toolbox for Matlab (http://grupo s.unica
meteo /Meteo Lab.html) using a linear decay to zero for
both the learning rate and neighborhood amplitude after
5000 cycles. Input records (i.e. days) with common syn-
optic patterns were then linked to the same SOM node
or cluster. The resulting set of clusters (SOM) represents
meaningful subgroups (i.e. generic climatic patterns)
within the larger dataset. To identify these groups (i.e.
nodes) we used a coordinate system naming the rows with
numbers (i.e. 1–5) and the columns with letters (i.e. A–E).
Note that while several variables can be jointly analyzed
only one SOM grid is produced and each SOM node con-
tains the four variables. However, for clarity, SOM maps
for each variable are shown separately. The daily scale of
our study has allowed us to construct frequency maps of
the SOM grid for each austral summer (DJFM) since 1979,
thereby allowing us to track transitions between synoptic
states and the dominance (i.e. the number of days that a
synoptic pattern is present during the austral summer) of
each state.
The anomalous 2017 coastal El Niño event inPeru
1 3
2.3 Interannual comparison ofaustral summers
We have used the OLR anomaly of the NOAA/Monthly
Mean upward longwave flux at top of the atmosphere
dataset since 1979 (http://clime t.cgi?field
=umd_olr) as well as SSTs from the HadISST1 data set, and
derived El Niño 1 + 2 and 3.4 indices (Rayner etal. 2003)
to assess similarities/differences between the 2016–2017
rainfall event and other austral summers since 1870. Had-
ISST1 has a monthly resolution, from 1870 to date, and is
available on a 1° × 1° grid. The use of the 3.4 and 1 + 2
SST indices served to separate the different behavior in
the central (3.4; 5°N–5°S, 170°W–120°W) and eastern
(1 + 2; 0°–10°S, 90°W–80°W) Pacific, since SST in these
two regions modulate rainfall over Peru in different ways
(Lavado-Casimiro and Espinoza 2014; Sulca etal. 2017).
Indices were obtained from https ://
gcos_wgsp/Times eries /. We employed a Superposed Epoch
Analysis (SEA) to compare 1 + 2 and 3.4 El Niño indices
based on SST standardized monthly anomalies correspond-
ing to all summers after El Niño events since 1950 (this is
the temporal limitation of the ONI index). The selection of
El Niño events was based on the classification of the Oce-
anic Niño Index (ONI) ( cts/
analy sis_monit oring /ensos tuff/ensoy ears.shtml ). This index
is most commonly used to define El Niño and La Niña events
and is based on SST anomalies in the Niño 3.4 region, which
represents the average equatorial SSTs across the Pacific
from about the dateline to the South American coast (Tren-
berth 2016). The ONI uses a 3-month running mean, and
to be classified as a full-fledged El Niño or La Niña, the
anomalies must exceed + 0.5°C or − 0.5°C for at least five
consecutive months. Furthermore, we wanted to compare the
SST distribution in the equatorial Pacific during the CEN-
2017 event with its counterpart post-strong El Niño austral
summers in 1878–1879, 1983–1984 and 1998–1999. Statis-
tical analyses have been carried out using the t-test for one-
sided sample (Haynes 2013) at significance level of 0.05.
Fig. 2 Maps representing the DJFM 2016–2017 accumulated rainfall
using data from (1) 300 rain gauges distributed all along Peru and (2)
GPM IMERG gridded data of 0.1° × 0.1° spatial resolution. Black
star is indicating the location of Lima. Note that the scale range is
different for the accumulated rainfall for the entire period from DJFM
(a) and at monthly scale (be)
C.Rodríguez-Morata et al.
1 3
3 Results
3.1 December–March precipitation analysis
From December 2016 to February 2017, accumulated pre-
cipitation over Peru (Fig.2) varied from extremely low
values (Obs.: 0mm; GPM: 31.76mm; Fig.2a) to unusu-
ally high totals (Obs.: 3291mm; GPM: 2142mm; Fig.2a).
By month, data from stations indicate that the highest
values of rainfall were recorded in March (1062mm;
Fig.2e) in the station of Pasaje Sur, in the north of Lam-
bayeque region. December 2016 (Fig.2b) exhibits the
second highest value (856.2mm) in El Boquerón station,
in the border between the Huánuco and Ucayali regions.
January 2017 (Fig.2c) recorded its maximum at the same
station (812.1mm) and February 2017 rainfalls (Fig.2d)
were highest at Quincemil station (831.1mm) located at
the border between Cuzco and Madre de Dios.
Rank percentile maps (Fig.3) show that many areas
of Peru received cumulative precipitation totals between
Fig. 3 Rank percentile maps for DJFM 2016–2017. The number of stations available for each month varies substantially. We indicate the number
of stations having a full record for the period 1982–2017 for each month
The anomalous 2017 coastal El Niño event inPeru
1 3
December 2016 and March 2017 that exceed all values
recorded since 1982. Regarding GPCC data by month,
cumulative precipitation exceeded the 80th percentile in
only some parts (7.9%) of the country in December 2016
(Fig.3a). In west Loreto and in the Ancash region precipi-
tation totals were over the 90th percentile or even unprec-
edented (i.e. 100th) in the case of the Ancash coast. By
contrast, in the other parts of Peru, rainfall totals remained
under the 50th percentile. During January 2017 (Fig.3b),
the 80th percentile was exceeded in 35.8% of the country,
whereas the 90th percentile was exceeded along the North
(i.e. Tumbes and Piura) and Central coast (i.e. Ancash and
Lima). Unprecedented values were seen along the South
coast (i.e. Arequipa), Central Andes (i.e. San Martin, Huá-
nuco, Pasto and Junín) as well as in the Amazonian lowlands
(i.e. south-west Loreto, Amazonas and Ucayali). In Febru-
ary 2017 (Fig.3c), cumulative precipitation exceeded the
80th percentile over 17% of the country but values above
the 90th percentile were still observed along the North coast
(i.e. Piura and Lambayeque) as well as west of the Loreto
and Huánuco regions. During March 2017 (Fig.3d), more
than one-fourth of the country (28.5%) shows percentile val-
ues above the 80th percentile and two spots – i.e. along the
North coast (Tumbes and Piura) and in the Central Andes
(Huánuco, Pasto, Junin and Ancash) exhibit 100th percentile
From the 17 gauge station records available for Decem-
ber 2016, only two show rank percentiles above the 60th
percentile: Bambamarc (66th) and Querocotillo (97th). In
line with the GPCC data, the other stations present values
under the 50th percentile. In January 2017, out of 40 sta-
tions, 25 exhibit values above the 60th, 15 above the 90th
and 5 reached the 100th percentile, most of the latter being
located in the western part of the Peruvian Central Andes.
From the 24 stations available for February 2017, 17 exhib-
ited values above the 60th, 7 above the 90th and 3 reached
the 100th percentile, again on the western Andean slopes but
also along the north coast of Peru. In March 2017 (for which
34 stations are available), 27 stations show values above the
60th, 16 above the 90th and 8 reaching the 100th percentile,
all of the latter are located along the west Andean slope.
3.2 Large‑scale atmospheric andoceanic synoptic
analysis ofthe2016–2017 coastal event
3.2.1 Event description
From December 2016 to March 2017, consecutive geopoten-
tial anomalies at 200hPa occur adjacent to the Chilean coast
centered at 33°S (Fig.4a). The biggest anticyclonic anomaly
developed on 20th January 2017 and lasted about 2weeks
with values above 112 gpm reaching positive anomalies of
Fig. 4 Time-longitude plots for the period 1/12/2016 to 30/4/2017
averaged at latitude 33°S. a Geopotential height anomaly at 200hPa.
b Zonal wind anomaly at 500hPa. Several periods of positive geo-
potential anomalies are observed and concurrent with strong negative
anomalies in the zonal winds at the free troposphere (red colors in
both plots)
C.Rodríguez-Morata et al.
1 3
200 gpm in some periods. Furthermore, two other, simi-
lar events occur in February and March 2017 but were of
shorter duration and less intense with anomaly values
ranging between 90 and 158 gpm. Coincident with these
height anomalies, strong negative zonal wind anomalies
are observed at 500hPa with net velocity values ranging
between 14.4 and 24m/s (Fig.4b). The negative character
of these wind anomalies indicates a weakening of the west-
erly wind component and therefore a strengthening of the
easterly component at 33°S. The average of the 1000hPa
vector wind anomaly along the Peruvian coast between Janu-
ary 20 and March 25, 2017, displays a common west-east
vector wind anomaly pattern just off the north coast of Peru
(Fig.5a) with a maximum speed wind anomaly of 4.8m/s.
Figure5b–d represent the situation corresponding with the
strongest 5-day anomalies of specific events for January,
February, and March, with highest speed anomalies occur-
ring in February and March with 5.1 and 5.2m/s, respec-
tively, just off the north coast (Fig.5c,d).
Large-scale convection development during the CEN-
2017 event is represented by OLR anomalies in Fig.6. In
December, the main convection center is located in the
Amazon basin, while a secondary area starts to develop
over the south of Brazil (Fig.6a). Later in January, in this
second area an enhancement of the negative anomaly of the
OLR can be seen with a net maximum value of − 30W/m2,
indicating an increase of the convective activity (Fig.6b).
Additionally, the shape of this feature extends in a NW–SE
diagonal forming a band of convection typical of the South
Atlantic Convergence Zone (SACZ). Also during January,
negative OLR anomalies increase along the Peruvian and
north Chilean coast. In February, the convective band asso-
ciated with the SACZ is less intense (higher values of OLR)
and moves southward. At the same time an intense con-
vective band progress in the central-east equatorial Pacific
(Fig.6c). During March, the anomaly associated with the
SACZ practically disappears and the most extensive con-
vective center is located in the eastern equatorial Pacific
adjacent to the northern Peruvian coast with negative OLR
anomalies of 30W/m2 (Fig.6d).
The spatio-temporal propagation of the absolute SST in
the equatorial Pacific as well as along the Peruvian coast
from November 2016 to November 2017 is represented in
Fig.7. Thus, for 0°–10°S (El Niño region 1 + 2; Fig.7a)
SST values raised abruptly by 2°C (from 25 to 27°C) along
the Peruvian north coast around mid-January until they pro-
gressively reach SST values > 30°C between mid-February
and mid-March (Fig.7a). On the other hand, for 10°S–17°S
(Fig.7b), SST were more moderate along the south-central
coast of Peru (Fig.7b) with maxima of 28°C in mid-March.
Considering SST anomalies (Fig.8), December 2016 was
characterized by negative SST anomalies in the central-east-
ern Pacific (3 and 3.4 Niño regions) and close to neutral SST
prevailed in region 1 + 2 (Fig.8a). During January 2017,
Fig. 5 Near surface vector wind anomalies between January and
March 2017 indicating a trend from the west to the east in the equato-
rial band and along the South American coast thus indicating easter-
lies weakening. a Average anomaly from January 20th to March 25th.
bd the situation for different moments is concurrent with the U wind
negative anomalies represented in the Fig.4b
The anomalous 2017 coastal El Niño event inPeru
1 3
Fig. 6 Monthly Outgoing Longwave Radiation (OLR) anomaly for December 2016 to March 2017
Fig. 7 Sea surface temperature (SST) time-longitude plot showing the
overwarming in the South Pacific averaged to a El Niño 1 + 2 region
(averaged between 0° and 10°S); and b along the center and south
Peruvian coast (averaged between 10°S and 17°S). Data provided by
the NOAA/ESRL Physical Sciences Division, Boulder Colorado from
their Web site at
C.Rodríguez-Morata et al.
1 3
negative SST anomalies started to become more intense in
the equatorial Pacific, but remained restricted to region 3.4.
At the same time, SST anomalies in the 1 + 2 region start
to rise (Fig.8b). Between February and March 2017, the
SST anomaly was at its maximum with values 2.8°C above
the average in region 1 + 2 (Fig.8c, d). Central Pacific SST
anomalies were close to neutral values.
3.2.2 SOM analysis
The SOM maps corresponding to the anomalies of the geo-
potential (Z) and zonal wind anomaly (U) at 200, 500 and
850hPa, as well as SST show 25 possibilities of synoptic
settings during the austral summer (an overview of all SOM
maps is provided in FiguresS1–S7, Supporting information).
The distribution of these 25 possibilities place contrasting
situations in opposite sides of the SOM map (i.e., El Niño
like patterns in the right side and La Niña in the left side)
and a range of intermediate possibilities in between. Thus we
find that strong El Niño events (i.e. 1982–1983, 1997–1998,
2009–2010, and 2015–2016) are mostly related with node
1E (Fig.9) representing a pattern dominated by (1) a full-
basin overwarming in the equatorial Pacific (Fig. S7) and
(2) a strong positive 850hPa zonal wind anomaly along
the equatorial Pacific, characteristic of El Niño events (Fig.
S6). In opposite, strong La Niña events (i.e. 1988–1989,
1998–1999, 1999–2000, 2007–2008 and 2010–2011) are
related with nodes of column A, linked to different exten-
sions of cooling in the equatorial Pacific (Fig. S7). Synoptic
states related with neutral conditions are scattered in the
center of the SOM grid (Fig.9) and are related with varia-
tions in the strength and position of pressure centers over the
continent, the south Pacific, and the south Atlantic (Fig S1,
2, 3). Weak-to-moderate ENSO extremes are also related to
scattered synoptic states but exhibit a clear trend toward El
Niño or La Niña sides of the SOM grid.
The temporal evolution and dominance (i.e. the number
of days of pattern activity) of each generalized pattern dur-
ing each austral summer since 1979 (considering 121days
between December to March) are represented in Fig.9. We
identify different pattern tracks depending on the ENSO
phase (i.e. El Niño, La Niña, or neutral conditions). For
strong El Niño and La Niña years, the path is restricted to
few a patterns with very high dominance in each setting. For
example, in the case of the strong El Niño in 1982–1983,
1997–1998 and 2015–2016, the dominance of the node 1E
was of 61, 86, and 88 days, respectively. However, in the
case of neutral and weak-moderate El Niño and La Niña
years, the path is more diverse with less time at each node.
For the 2016–2017 austral summer event, however, the path
was very much restricted to the nodes in the central columns
of the SOM map, and moves from nodes in the lower part of
Fig. 8 Plots ad show the evolution of the SST monthly anomaly in the equatorial Pacific between December 2016 and March 2017. We use
HadISST1 data provided by the NOAA/OAR/ESRL PSD (
The anomalous 2017 coastal El Niño event inPeru
1 3
the grid (associated with cold SST in the equatorial Pacific),
to nodes in the upper part (related with warmer SST in the
1 + 2 El Niño region than in the central Pacific). Upper-level
pressure anomalies were negative to neutral at the beginning
of the event and then evolve to very positive anomalies.
3.3 The 2017 coastal event inalarger context
Figure10 represents the JFM averaged anomaly of El Niño
3.4 and 1 + 2 index values since 1870 to the present. The
value corresponding to the 1 + 2 El Niño region (shaded in
red Fig.10 and indicated with a black arrow) during the
2016–2017 was only exceeded by years classified as El
Niño events (i.e. 1878*, 1889, 1897, 1900, 1926, 1983*,
1987, 1997*, and 2016*; asterisks indicate strong El Niño
events). The values corresponding to the summers following
the decay of an El Niño event (Table1) show that the 2017
JFM anomaly in the 1 + 2 El Niño region was significantly
higher (0.94) than average (− 0.15). The situation in the 3.4
region is different as it presents a value (− 0.15) slightly
above the average for 2017 but still negative and not signifi-
cant. Regarding the OLR anomalies in El Niño 1 + 2 region
since 1979 (Fig.11), we observe that the negative anomaly
during 2016–2017 austral summer only has been exceeded
during the El Niño years in 1982–1983, 1997–1998, and
Results of the SEA (Fig.12) comparing the Niño SST
indices (i.e. Niño 1 + 2 and 3.4) of several austral summers
after El Niño events indicate that for the central Pacific (Niño
3.4 index; Fig.11a) 2016–2017 SST anomaly values were
not significantly far from the average for D (0) (i.e. index
value − 0.47, p-value 0.098), J + 1 (i.e. index value − 0.44,
p-value 0.177) and F + 1 (i.e. index value − 0.07, p-value
0.105). Since M + 1 (Mar 2017) the 3.4 SST anomaly was
statistically significantly high but below of 1992–1993 aus-
tral summer. In the 1 + 2 SST index case, (Fig.11b) results
indicate statistically significant high values during the
2016–2017 austral summer for all the months of the period.
Figure13 shows differences of the spatial distribution
of the SST anomaly along the equatorial Pacific between
the 2016–2017 austral summer and its counterparts in
1878–1879, 1983–1984 and 1998–1999. During the CEN-
2017, the west-central Pacific (Niño 4 and 3.4 regions) in
fact exhibited the lowest SST anomaly during December-
March with the only negative values of the period. This cool-
ing is coherent as such but still far from the negative values
observed in 1878–1879, 1983–1984 and 1998–1999. The
Pacific Niño 3 and 3.4 regions showed neutral to positive
anomalies, which were again significantly higher in 2017
than in 1878–1879, 1983–1984 and 1998–1999 when the
anomalies were clearly negatives. The east Pacific (Niño
1 + 2 region) displays by far the largest anomalies with maxi-
mum value of 2.1°C during the CEN-2017, very different
from the very negative values, which remained in this region
for the other years.
4 Discussion andconclusions
During the 2016–2017 austral summer, intense rainfalls
were recorded over large parts of Peru. The accumulated
measured precipitation exceeded all summer values recorded
since 1982, and led to catastrophic damage to housing and
infrastructure, affecting more than 660,000 people, and leav-
ing more than 100 deaths (INDECI 2017). Furthermore, the
spatial and temporal synchronicity of these extraordinary
rainfall values observed during the DJFM 2016–2017 in
various and very diverse geographic settings across Peru
can be described as unusual.
Our observations of the climatic background regarding
the large-scale oceanic and atmospheric setting of this cos-
tal event agree with Garreaud (2018), who pointed to the
role of extratropical climatic forcing. While it is true that
between January and March 2017, positive anomalies of the
sea surface height were detected along the Peruvian coast
(these have been related to warming Kelvin waves activity
impacting the north Peruvian coast), these waves contributed
only partially to the SST warming in this region (ENFEN
2017a, b). In fact, time series (not shown) of subsurface sea
temperatures and dynamic height of the sea surface obtained
from the Tropical Atmosphere Ocean (TAO) array of buoys
moored in the tropical Pacific do not provide clear evidence
of Kelvin activity, suggesting they were not the primary
mechanism associated with the strong coastal warming in
At the beginning of the 2016–2017 austral summer, the
strongest rainfall totals were found over some areas of the
Central Andes, in the Huánuco region. Even though seasonal
rainfalls over the Peruvian Central Andes (and Peru in gen-
eral) occur during the summer (IGP 2005; Sulca etal. 2016),
the accumulated rainfall in January was the highest since
1982 over many of the Central Andes regions (i.e. San Mar-
tin, Huánuco, Pasto and Junín) as well as along the South
coast (i.e. Arequipa) and over the Amazonian lowlands (i.e.
south-west Loreto, Amazonas and Ucayali). According to
our synoptic analysis, the long-lasting, upper-level anticy-
clone observed adjacent to the Chilean coast during January
(Fig.4a) led to an intensification of mid-upper level sub-
tropical easterly winds (Fig.4b) favoring a moisture flux
from the Amazon basin and thus intense precipitation in
the Central Andes. These observations agree with Garreaud
(2000) and Sulca etal. (2016) who describe wet events over
the Central Andes in relation with upper-level geopoten-
tial anomalies linked to the equatorward propagation of
mid-latitude wave trains. This is also in line with Garreaud
(1999, 2018), who found that the propagation of mid-latitude
C.Rodríguez-Morata et al.
1 3
The anomalous 2017 coastal El Niño event inPeru
1 3
Rossby wave trains drive rainfall variability on the Altiplano
on intra-seasonal time scales.
During February and March 2017, the extreme rainfall
values moved to the Peruvian north and central coast areas
as well as to the Central Andes with cumulative March pre-
cipitation exceeding any other value for the same period
since 1982 in many regions (Figs.2, 3). For this period,
OLR anomalies in the 1 + 2 El Niño region (Figs.6, 11) were
consistent with the exceptionally high values of SST in the
region (Figs.7, 8, 10, 13d), reaching levels that are normally
indicative of the development of an El Niño event (Rasmus-
son and Carpenter 1982; Trenberth and Stepaniak 2001).
Furthermore, Fig.4b shows that the upper-level subtropical
easterlies were reinforced, which, according to Garreaud
(2018), could have modified the subtropical circulation in
the lower troposphere and in turn may have directly affected
SST in the eastern equatorial Pacific (Fig.5). In fact, the
continued strength of the easterly trades in the equatorial
band is supported by the low-level equatorward flow along
the Pacific coast of South America, generated by descent
when the subtropical westerlies meet the Andes (Rodwell
and Hoskins 2001). If the subtropical westerlies are weak
or reversed, this equatorward flow does not provide the
mass continuity to the SE trade winds, thus hampering the
upwelling along the coast and leading to surface warming
in the eastern Pacific.
We also point to the potential role of SST in the South
Atlantic and the SACZ during the CEN-2017 event. As
observed in Fig.8, an intense positive anomaly is observed
in the South Atlantic centered at 40°S, 40°W, coincident
with a SST anomaly in the equatorial Pacific, OLR nega-
tive anomaly over the SACZ (Fig.6), and heavy rainfalls
over the Peruvian central Andes (Figs.2, 3). This situation
is coherent with findings of Rodrigues-Chaves and Nobre
(2004) and Carvalho etal. (2004), who stated that positive
correlation exists between SST over the South Atlantic (from
the 40°S–0°) and the SACZ. Furthermore, our observations
are in line with Lavado-Casimiro etal. (2013) who found
that higher values in the southern tropical Atlantic (0–20°S,
30°W–10°E) than in the north tropical Atlantic (5–20°N,
60–30°W) favor precipitation in the Peruvian Amazon-
Andes. On the other hand, the intensification of the con-
vective activity in the SACZ in January (Fig.6b) could be
related to the rainfall in the Central Andes by controlling
the position of the Bolivian High (Lenters and Cook 1999).
Fig. 9 Graphs showing the dominance of the synoptic patterns during
the austral summers since 1979. The ONI classification of each aus-
tral summer is represented by the colored squares and the abbrevia-
tions correspond to: VSEN Very strong El Niño; SEN Strong El Niño;
MEN Moderate El Niño; WEN Weak El Niño; SLN Strong La Niña;
MLN Moderate La Niña; WLN Weak La Niña
Fig. 10 Niño 3.4 and 1 + 2 indices since 1870. The black arrow indicates the year 2017
C.Rodríguez-Morata et al.
1 3
Finally, the southward shift of the SACZ observed in Febru-
ary (Fig.6c) could be related with the enhanced tropical con-
vection over the central and eastern Pacific (Nogues-Paegle
and Mo 1997).
Striking rainfall totals and high rank percentiles in the
northern interior lowlands of Piura and Lambayeque as well
as in southern region up to La Libertad during March 2017
are consistent with studies stating that inter-annual rainfall
variability in these regions cannot be explained by El Niño
activity alone (Lagos etal. 2008; Lavado-Casimiro and
Espinoza 2014; Rau etal. 2017). For instance, Rau etal.
(2017) state that inter-annual variations of rainfall do not
necessarily correspond to strong El Niño years, and that an
important part of rainfall variability in the region may cor-
respond to local convective events associated with coastal
warm oceanic conditions related mainly to Kelvin waves
and the Madden and Julian Oscillation (MJO) (Bourrel etal.
2015). By contrast, inter-annual rainfall variability in the
northern highlands of Cajamarca was not clearly related with
SST in the east Pacific, but negatively correlated with SST in
the central Pacific (Bourrel etal. 2015; Rau etal. 2017). This
is consistent with the negative anomalies that we observe in
the west-central Pacific (Niño 4 and 3.4 regions in Fig.8).
Regarding rainfall activity in the Peruvian Amazonian
lowlands, rainfall totals reached their long-term maxima
mostly during January–February 2017 as well. We hypoth-
esize that this anomaly can be related with the positive SST
Table 1 JFM SST anomalies in the 1 + 2 and 3.4 El Niño regions cor-
responding to years following the decay of an El Niño event since
1870 and indicating either neutral or weak La Niña conditions
The 2017 year was significantly above the average in the 1 + 2 El
Niño region, indicating a clear El Niño situation. The situation in the
3.4 region reflects temperatures slightly under the average but they
are not significant. Note that the considered cases are those where the
SST values in the Niño region decay toward zero during the remain-
ing months of year + 1 of the El Niño event. Some Niño’s do not fol-
low that decay mode pattern, and so are not included here. Statistical
significance computed using a t-test for one-sided samples at a sig-
nificance level of 0.05 (below)
* Significant values
JFM El Niño 1 + 2 SST
JFM El Niño
3.4 SST
1879 − 0.36*− 0.32*
1890 − 0.42*− 1.92*
1898 − 0.51*− 0.6*
1904 − 0.44*− 0.8*
1927 − 0.013*0.05
1967 − 0.63*− 0.53*
1984 − 0.49*− 0.6*
1999 0.04*− 1.29*
2017 0.94*− 0.15
1870–2017 average − 0.15 − 0.073
Fig. 11 Time series of OLR anomaly in El Niño 1 + 2 region since 1979
The anomalous 2017 coastal El Niño event inPeru
1 3
anomalies in the South Tropical Atlantic observed in Fig.8c.
This hypothesis is consistent with Espinoza etal. (2009) or
Lavado-Casimiro etal. (2013) who concluded that intense
rainfalls over Peru are likely related with the positive SST
anomalies observed in the South Tropical Atlantic. Yet, fur-
ther research is needed to understand the role of the tropical
Atlantic during the CEN-2017 event.
The warming in the equatorial Pacific was not a full
basin event and the SOI index, with values close to zero
(not shown) indicates that the CEN-2017 event was not a
coupled ocean–atmosphere phenomenon. As such, it dif-
fers from a typical El Niño and defines it like an ocean-
coastal event (Takahashi and Martinez 2017). Consider-
ing the SST values in the eastern Pacific, this 2016–2017
summer also differs from its counterparts after El Niño
events since 1870 (Fig.10, 11, 12 and 13). Differences
with previous years are also noticed in the transition
between synoptic states throughout the summer (Fig.9).
Even if the synoptic states related with the 2016–2017
austral summer are commonly linked to the large-scale
summer dynamic over South America and are not exclu-
sive of the 2016–2017 summer, what is different this time
is the low dispersion and relatively low number of synoptic
states compare with previous years (except for the strong
El Niño and La Niña years). Thus, dominance of these
synoptic states was higher, favoring the climatic processes
that trigger the overwarming in the equatorial Pacific.
Consequently, the usual precipitation pattern after a strong
El Niño event—with slightly above-normal rainfalls in
the south of Peru and nearly normal to dry conditions in
the north—was altered after the end of the 2015–2016 El
Niño, as it did not result in the usual cooling of the central
equatorial Pacific (Lavado-Casimiro and Espinoza 2014).
We conclude that the coexistent SST anomalies in the
equatorial Pacific (and presumably also in the Tropical
Atlantic) have clearly favoured the development of the
extreme “Coastal El Niño” event and concomitant high
magnitude of precipitation over Peru in DJFM 2016–2017.
The approach shown in this paper, together with its inter-
pretation within a climatic context, demonstrates that the
DJFM 2016–2017 rainfall pattern over Peru was highly
anomalous, both in terms of its magnitude and timing after
a strong El Niño event. The rather severe consequences
and important death tolls cannot be explained by the
anomalous weather phenomenon only, but are also due to
the absence of El Niño early warnings, which in turn were
largely the result of the abrupt and unexpected warming
above the average in the Niño region 1 + 2. This suggests
that disaster management strategies in Peru should main-
tain the same level of vigilance across time, regardless of
the ENSO phase, taking into account the whole variability
of South American summers.
Fig. 12 Superposed epoch analysis for the anomalies of the 3.4 (a)
and 1 + 2 (b) El Niño indices during the year after strong El Niño
events since 1951. Following the nomenclature system of Rasmusson
and Carpenter (1982), analysis covers the months of December after
an El Niño event (D0) to June of the following year (J + 1) and they
are centered in March 2017 (M + 1). Shades years are statically sig-
nificant at 0.05 level
C.Rodríguez-Morata et al.
1 3
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... Coastal El Niño events are instances of longlasting marine heatwaves in the Easternmost Tropical Pacific not associated with typical basinwide El Niño events. This type of event came into prominence with the large event of early 2017 [1][2][3][4][5][6]. This event was largely unexpected and unpredicted [2,5], as it did not fit any of the known basin-wide ENSO types [7][8][9][10][11][12]. ...
... In parts of Northern Peru "rainfall exceeded 700mm, about 15 times the long-term mean, and landslides and flooding were widespread, with a large cost in lives and infrastructure" [2]. Other impacts include increases in exposure to pathogens and a dramatic increase in dengue cases in Peru [6]. Comparing this pattern with the extreme 1997-1998 basin-wide event during the same season (Figs. ...
... There are two main triggers, both with an important role for evaporative cooling, that have been proposed to explain the 2017 event. In the first case, a weakening of the southeast trade winds, triggered either by an extratropical Rossby wave train in the southern Pacific [2,6] or through a teleconnection from the Amazon region [3], weakened coastal upwelling and also reduced evaporative cooling. The second mechanism underscores the influence of remote forcing from the western/central Pacific [14]. ...
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Coastal El Niño events —instances of anomalous ocean warming in the far eastern Tropical Pacific during basin-scale neutral or cold conditions— can have severe societal impacts for countries along the west coast of South America, as exemplified by the 2017 and 2023 Peru-Ecuador floods. Due to brevity of the observational record, it is not well understood whether these events are driven by local or large-scale processes. Here, to overcome this limitation, we use a data-driven modeling approach to address their return period and forcing mechanisms. It is shown that extreme coastal El Niño events result from the constructive interactions of the Pacific Meridional Modes (PMM). Specifically, the North PMM yields a dipole-like anomaly SST pattern along the equator that favors its development, while the positive phase of the South PMM reinforces it. A smaller group of more moderate coastal events are remotely driven by zonal wind anomalies in the western Tropical pacific without the PMMs’ influence. The role of PMMs in the development of extreme coastal El Niño suggests that they may be more predictable than previously thought.
... La cause probable des précipitations anormales est liée à la combinaison d'une période humide particulièrement intense sur les Andes centrales liée à un anticyclone profond et durable situé au large des côtes chiliennes, et au développement inhabituel de précipitations chaudes. Eau au large des côtes du Pérou dans la région nominale d'El Niño 1 + 2. Ce réchauffement a été lié à un affaiblissement anormal du flux d'ouest subtropical moyenélevé, qui à son tour a conduit à un affaiblissement des alizés du sud-est au large de la côte, ce qui a rendu la remontée d'eau difficile près de la côte péruvienne et favorisé le réchauffement du Pacifique oriental [2] . ...
Conference Paper
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Au Pérou, au cours des mois de janvier à mars 2019, les hauts plateaux péruviens ont subi les conséquences du phénomène El Niño, un événement naturel caractérisé par la fluctuation des températures océaniques dans la partie centrale et orientale du Pacifique équatorial, associée à des changements dans l’atmosphère. Les principaux secteurs touchés correspondent aux hautes zones andines et à la jungle du Pérou où les précipitations ont dépassé 25,7 mm / jour. Cet article traite des dommages instantanés, des séquelles et des leçons apprises lors de la construction de l’autoroute Oyón-Ambo lors du phénomène El Niño de 2019. Cette route à chaussée rigide, située dans la région des hautes Andes de la région de Pasco au Pérou, est située entre 3500 mètres d’altitude et 4800 mètres d’altitude et est l’une des routes aux conditions géographiques les plus difficiles au monde. Cette route est adjacente à la rivière Chaupihuaranga, un affluent de la rivière Huallaga qui s’étend des sommets des chaînes de montagnes Raura et Huayhuash dans une direction nord-est jusqu’à son embouchure dans la Huallaga dans la ville d’Ambo in Huánuco, qui, en raison des fortes précipitations dues à cet événement naturel extrême, a augmenté son débit et causé de graves dommages et modifications de l’ingénierie du projet dans les travaux routiers. Cet événement a entraîné la modification des plans d’ingénierie de la route en construction pour permettre l’adaptation des travaux à la nouvelle topographie causée par ledit événement. Trois ans après ce phénomène, il a été possible de quantifier les dommages et les modifications du dossier technique du projet en raison de la survenance de ce phénomène naturel extrême et les leçons apprises ont été écrites pour être appliquées dans les futurs travaux routiers à construire . La gestion des risques est un outil essentiel pour minimiser les impacts de ces événements, lorsque la probabilité d’occurrence du phénomène extraordinaire est faible.
... For each study forest, we selected 15 samples (MS > 0.2) to acquire wood core digital images for the three welldefined Peruvian historical strong SAMS years (1983, 1998and 2017Rodríguez-Morata et al. 2019;Shimizu et al. 2020;Son et al. 2020), as well as for the two consecutive years before (1981,1982,1996,1997,2015,2016), and after strong monsoon events (1984,1985,1999,2000,2018,2019). Andean Walnut shows wood dark enough to increase the contrast in the wood core digital images (Inga & del Valle 2017), which allowed for the measurement of the vessel architecture parameters (Fig. A1D). ...
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Andean Walnut (Juglans neotropica Diels-Juglandaceae) is a long-lived, deciduous broadleaf Tropical Montane Cloud Forest (TMCF) tree species native to the Andes Cordillera; nevertheless, it has received limited attention for dendro-quantitative wood anatomical studies. Based on 70 increment cores from 50 Andean Walnut trees at two Peruvian TMCFs, two chronologies (from 1969 to 2020 and from 1964 to 2020) were developed. The xylem vessel parameters assessment allowed us to detect South American Monsoon System (SAMS) precipitation signals in the Andean Walnuts’ wood. Dendro-wood anatomical features can be assessed within an annual growth ring, which allows for assessing intra-annual past and present wood anatomy-function relationships and its climate vulnerability.
... The Ocean Nino Index (ONI) is a measure of the sea surface temperature (SST) anomaly in the central and eastern tropical Pacific Ocean. It is used to monitor and classify El Niño and La Niña events, which are large-scale climate patterns that can have significant impacts on weather patterns, agriculture, and other aspects of the Earth's climate system (Rodríguez-Morata et al., 2019). El Niño events are characterized by warmer-than-normal SSTs in the tropical Pacific, while La Niña events are characterized by cooler-than-normal SSTs. ...
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El Nino Southern Oscillation (ENSO) is an irregular climate oscillation induced by sea surface temperature anomalies (SSTA) in the Equatorial Pacific Ocean. An anomalous warming in this area is known as El Nino, while an anomalous cooling bears the name of La Nina. The objectives of this study are to: reproduce Oceanic Nino Index (ONI) based on MW OISST; produce Nino Region 3.4 wind and Sea Surface High (SSH); analyze the correlation between SST and Wind & SSH; discuss Typhoon Soudelor based on SST, Wind, and SSH; and analyze the correlation of El Nino and Precipitation in specific area. MWOI-SST was used to produce monthly mean SST over Nino 3.4 region from January 1998 – May 2020. Monthly wind data was obtained from QuickScat. Daily Sea Surface High (SSH) was obtained from Copernicus Marine Environment Monitoring Service (CMEMS). Daily precipitation from TRMM 3B42 over Bandung City (Indonesia) was used to assess the correlation between El Nino and precipitation in specific area. The results show that in Nino 3.4 region, 2015 is the hottest year during 1998-2020 period with average SST of 28.5oC, and 1999 is the coldest year with average SST of 25.7oC. The result shows that the MWOI-SST Ocean Nino Index has very strong correlation with ERSST.v5 with coefficient of correlation is 0.92 and RMSE is 0.36oC. the wind speed of Nino 3.4 region is range from 5.23 m/s to 7.97 m/s. Unlike Sea Surface Temperature (SST), annual average wind speed is more stable with monthly variation. The wind speed is observed high in the beginning and the end of years. Sea Surface Height (SSH) over Nino 3.4 region varied from 65.8 cm to 106.8 cm. 2015 is the highest SSH with annual average of 96 cm, whereas 1999 is the lowest SSH with annual average of 71.4 cm. It is observed that Sea Surface Temperature (SST) has negative correlation with wind speed with coefficient of correlation of 0.28. Conversely, Sea Surface Temperature (SST) over Nino 3.4 region has positive correlation with Sea Surface High (SSH) with coefficient of correlation of 0.30. which mean the higher temperature, the higher Sea Surface Height. During the passage of Typhoon Soudelor, there is evident cool trail along its track with rightward bias. We can assume that the decreasing precipitation in Bandung City might be affected by strong El Nino occurrence in 2015.
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Archaeological sites may be exposed to different types of risks related to wars, natural phenomena, and illicit human activities. Quantitative data on the type and extent of the damages and destructions suffered by these sites are of primary importance for their reparation and the planning of conservation and defence actions. The Apurlec Monumental Archaeological Complex (about seventh–fourteenth century AD, Peru, “Intangible and Essential Heritage” of the Peruvian Ministry of Culture) includes platforms, canals, and rectangular ceremonial/administrative enclosures. Between June and August 2021, Apurlec has been affected by a partial destruction of its southern sector. Here we present the results of two UAV photogrammetric surveys conducted before (23 January 2021) and after (30 August 2021) the destructive event. The comparison of the orthoimages and the Digital Surface Models obtained form the two surveys allow us to detect illicit activities as earth removal to collect construction material, creation of cultivable areas, and steal manufacts from archeological excavations. We calculate that the area covered by the destruction is 121,665 m² (perimeter of about 2 km²) the removed material amount to 401,513.5 m³, a value corresponding to a mass of about 702,648.63 ton. The post-destruction topography is lower of about 3.3 m with respect to the original one. Our anytical and metholodological approach could be extended to other archeological sites potentially exposed to anthropic and natural hazards.
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Purpose Societies go through complex challenges in the face of the vertiginous increase in disasters, mostly produced by the effects of extreme events. The lack of capacity to deal with disasters is evident, especially in developing countries, as in the case of Peru. Under such a premise, this paper contributes to strengthening the country’s capacities, through an evaluation of national disaster resilience to the El Niño-Southern Oscillation-driven hazards caused by the El Niño disaster event between 2016 and 2017 on the Peruvian coast. Design/methodology/approach By reviewing the literature, various hazards were identified, such as heavy rainfalls and cascading hazards, such as floods and landslides. Even though risk assessments were carried out, 169 people died and essential infrastructure was severely impacted and lost. Through a 12-criteria resilience assessment framework sub-divided into sustainable development and disaster risk reduction, a diagnosis of national disaster resilience was carried out, along with a disaster risk management evaluation. Under such assessments, strategic recommendations were proposed to enhance the resilience of the country. Findings The lack of resilience of the country is reflected in the evaluated criteria, the most negative being the built environment due to infrastructure system’s vulnerability to hazards, and the lack of social development, despite national economic growth in Peru. Originality/value The research is extremely valuable because it bridges the knowledge gap on disaster resilience in Peru. In addition, the methodology, as well as the multi-topic assessment framework, can be used for other analyses, which are key to building greater capacity in nations around the globe.
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During the last decades, marine heat waves (MHWs) have increased in frequency and duration, with important impacts on marine ecosystems. This trend has been related to rising global sea surface temperatures, which are expected to continue in the future. Here, we analyze the main characteristics and possible drivers of MHWs in the eastern South Pacific off Chile. Our results show that MHWs usually exhibit spatial extensions on the order of 103-104 km2, temperature anomalies in the mixing layer between 1 and 1.3°C, and durations of 10 to 40 days, with exceptional events lasting several months. In this region, MHW are closely related to the ENSO cycles, in such a way that El Niño and, to a lesser extent, La Niña events increase the probability of high intensity and extreme duration MHWs. To analyze the MHW drivers, we use the global ocean reanalysis GLORYS2 to perform a heat budget in the surface mixed layer. We find that most events are dominated by diminished heat loss –associated with reduced evaporation– and enhanced insolation; thus, this group is called ASHF (for air-sea heat fluxes). The second type of MHWs is driven by heat advection, predominantly forced by anomalous eastward surface currents superimposed on a mean westward temperature gradient. The third type of MHWs results from a combination of positive (seaward) anomalies of air-sea heat fluxes and heat advection; this group exhibits the greatest values of spatial extension, intensity, and duration.
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Crop wild relatives (CWRs) are important sources of novel genes, due to their high variability of response to biotic and abiotic stresses, which can be invaluable for crop genetic improvement programs. Recent studies have shown that CWRs are threatened by several factors, including changes in land-use and climate change. A large proportion of CWRs are underrepresented in genebanks, making it necessary to take action to ensure their long-term ex situ conservation. With this aim, 18 targeted collecting trips were conducted during 2017/2018 in the center of origin of potato (Solanum tuberosum L.), targeting 17 diverse ecological regions of Peru. This was the first comprehensive wild potato collection in Peru in at least 20 years and encompassed most of the unique habitats of potato CWRs in the country. A total of 322 wild potato accessions were collected as seed, tubers, and whole plants for ex situ storage and conservation. They belonged to 36 wild potato species including one accession of S. ayacuchense that was not conserved previously in any genebank. Most accessions required regeneration in the greenhouse prior to long-term conservation as seed. The collected accessions help reduce genetic gaps in ex situ conserved germplasm and will allow further research questions on potato genetic improvement and conservation strategies to be addressed. These potato CWRs are available by request for research, training, and breeding purposes under the terms of the International Treaty for Plant Genetic Resources for Food and Agriculture (ITPGRFA) from the Instituto Nacional de Innovacion Agraria (INIA) and the International Potato Center (CIP) in Lima-Peru.
Wildfire occurrence has increased sharply in the last two decades in the Peruvian Andes. There is, however, little research on wildfires and their impacts. This study explores the conditions conducive to wildfire during 2020. MODIS images were collected to estimate the development of vegetation. In addition, ground-based monthly and satellite-based daily precipitation data were collected. Daily precipitation regularity was evaluated using a concentration index (CI), while monthly precipitation was used to estimate the Standard Precipitation Index (SPI). We used also the Global Vegetation Moisture Index (GVMI), which is a useful indicator of vegetation dynamics based on vegetation moisture. Our results do not indicate a direct link between rainfall regularity (lowest CI values) and development of vegetation. Although the SPI drought analysis using seasonal rainfall indicated nearly normal conditions during 2019–2020, analysis of dry-day frequency (DDF) suggests that the dry period played an important role between September and November 2020, producing conditions similar to the droughts of 2005, 2010 and 2016. GVMI also showed below-average values from April to November. We corroborate the usefulness of DDF for monitoring the potential increase in wildfire conditions. A controlled burn policy could offer a more useful way to reduce the impacts of wildfire.
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Large-scale oceanic oscillations and their teleconnections with meteorological events are of great importance in macro-scale climatic studies. In this regard, this study investigates the spatiotemporal teleconnections between four oceanic oscillations, namely North Atlantic Oscillation (NAO), El Niño/Southern Oscillation (ENSO), Atlantic Multi-Decadal Oscillation (AMO), and Pacific Decadal Oscillation (PDO), against Peruvian precipitation patterns during the past 25 years (i.e., 1990-2015). For this purpose, variation in the precipitation pattern at monthly and annual scales as well as the Standardized Precipitation Index (SPI) time series at 1-, 3-, 12-, and 48-month time scales were evaluated at 10 meteorology stations across Peru. Pearson's correlation coefficient and mutual information between the oceanic oscillations and precipitation-born signals were calculated and spatially interpolated using the Kriging method. The results indicated the presence of three major climatic regions in the country. The NAO has the largest correlation with the monthly precipitation. However, the ENSO was found as the main climate driver of extremely wet and extremely dry conditions in the country. The results also demonstrated that the PDO has a higher impact on the annual precipitation pattern, particularly in the southern and eastern parts of the country.
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The occurrence of El Niño has been generally considered the main driver of hydro‐geomorphic processes in Peru. However, the climatic characterization of hydro‐geomorphic events (HGE) occurring in the absence of El Niño remains scarce. Information contained in the DesInventar disaster database suggests a widespread occurrence of HGE associated to cold‐neutral sea surface temperature (SST) in the central Pacific and south tropical Atlantic. Here, we aim at characterizing synoptic patterns associated with HGE that have occurred over last 35 years related to the different El Niño types and focusing as well on the non‐Niño phases. We use the ERA‐Interim reanalysis climate data and implement self‐organizing maps to assess the link between HGE in Peru and specific synoptic patterns. Results suggest that synoptic patterns associated with La Niña and neutral conditions play an important role in the occurrence of hydro‐geomorphic disasters in Peru during the austral summer. A total of 21% of the events are associated only with the 1972–1973, 1982–1983 and 1997–1998 El Niño events and are mainly focused in the northern Pacific coast of the country (i.e., Tumbes, Piura and Lambayeque) while more than 36% of the recorded events in the database were associated with La Niña and neutral conditions between 1970 and 2013. La Niña‐related events were more relevant in the Andean–Amazonian regions, whereas neutral conditions were related to more frequent HGEs in the southern regions (south of the 13.25°S) along the Peruvian Pacific coast. These outcomes imply an enhanced understanding of the synoptic mechanisms leading to the occurrence of HGE and contribute to a better understanding of the triggers of HGE causing disaster no exclusively related to El Niño‐like years in Peru.
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The far eastern tropical Pacific experienced a rapid, marked warming in early 2017, causing torrential rains along the west coast of South America with a significant societal toll in Peru and Ecuador. This strong coastal El Niño was largely unpredicted, even a few weeks before its onset, and it developed differently from either central or eastern events. Here we provide an overview of the event, its impacts and concomitant atmospheric circulation. It is proposed that a remotely forced, sustained weakening of the free tropospheric westerly flow impinging the subtropical Andes leads to a relaxation of the southeasterly (SE) trades off the coast, which in turn may have warmed the eastern Pacific throughout the weakening of upwelling in a near-coastal band and the lessening of the evaporative cooling farther offshore.
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El Nino events, characterized by anomalous warming in the eastern equatorial Pacific Ocean, have global climatic teleconnections and are the most dominant feature of cyclic climate variability on subdecadal timescales. Understanding changes in the frequency or characteristics of El Nino events in a changing climate is therefore of broad scientific and socioeconomic interest. Recent studies(1-5) show that the canonical El Nino has become less frequent and that a different kind of El Nino has become more common during the late twentieth century, in which warm sea surface temperatures (SSTs) in the central Pacific are flanked on the east and west by cooler SSTs. This type of El Nino, termed the central Pacific El Nino (CP-El Nino; also termed the dateline El Nino(2), El Nino Modoki(3) or warm pool El Nino(5)), differs from the canonical eastern Pacific El Nino (EP-El Nino) in both the location of maximum SST anomalies and tropical-midlatitude teleconnections. Here we show changes in the ratio of CP-El Nino to EP-El Nino under projected global warming scenarios from the Coupled Model Intercomparison Project phase 3 multi-model data set(6). Using calculations based on historical El Nino indices, we find that projections of anthropogenic climate change are associated with an increased frequency of the CP-El Nino compared to the EP-El Nino. When restricted to the six climate models with the best representation of the twentieth-century ratio of CP-El Nino to EP-El Nino, the occurrence ratio of CP-El Nino/EP-El Nino is projected to increase as much as five times under global warming. The change is related to a flattening of the thermocline in the equatorial Pacific.
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The 1925 El Niño (EN) event was the third strongest in the twentieth century according to its impacts in the far-eastern Pacific (FEP) associated with severe rainfall and flooding in coastal northern Peru and Ecuador in February–April 1925. In this study we gathered and synthesised a large diversity of in situ observations to provide a new assessment of this event from a modern perspective. In contrast to the extreme 1982–1983 and 1997–1998 events, this very strong “coastal El Niño” in early 1925 was characterised by warm conditions in the FEP, but cool conditions elsewhere in the central Pacific. Hydrographic and tide-gauge data indicate that downwelling equatorial Kelvin waves had little role in its initiation. Instead, ship data indicate an abrupt onset of strong northerly winds across the equator and the strengthening/weakening of the intertropical convergence zones (ITCZ) south/north of the equator. Observations indicate lack of external atmospheric forcing by the Panama gap jet and the south Pacific anticyclone and suggest that the coupled ocean–atmosphere feedback dynamics associated with the ITCZs, northerly winds, and the north–south SST asymmetry in the FEP lead to the enhancement of the seasonal cycle that produced this EN event. We propose that the cold conditions in the western-central equatorial Pacific, through its teleconnection effects on the FEP, helped destabilize the ITCZ and enhanced the meridional ocean–atmosphere feedback, as well as helping produce the very strong coastal rainfall. This is indicated by the nonlinear relation between the Piura river record at 5°S and the SST difference between the FEP and the western-central equatorial Pacific, a stability proxy. In summary, there are two types of EN events with very strong impacts in the FEP, both apparently associated with nonlinear convective feedbacks but with very different dynamics: the very strong warm ENSO events like 1982–1983 and 1997–1998, and the very strong “coastal” EN events like 1925.
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The El Niño of 2015-16 was among the strongest El Niño events observed since 1950, and took place almost two decades after the previous major event in 1997-98. Here, perspectives of the event are shared by scientists from three national meteorological or climate services that issue regular operational updates on the status and prediction of the El Niño-Southern Oscillation (ENSO). Public advisories on the unfolding El Niño were issued in the first half of 2015. This was followed by significant growth in sea surface temperature (SST) anomalies, a peak during November 2015 - January 2016, subsequent decay, and its demise during May 2016. The lifecycle and magnitude of the 2015-16 El Niño was well predicted by most models used by national meteorological services, in contrast to the generally over-exuberant model predictions made the previous year. The evolution of multiple atmospheric and oceanic measures demonstrates the rich complexity of ENSO, as a coupled ocean-atmosphere phenomenon with pronounced global impacts. While some aspects of the 2015-16 El Niño rivaled the events of 1982-83 and 1997-98, we show that it also differed in unique and important ways, with implications for the study and evaluation of past and future ENSO events. Unlike previous major El Niños, remarkably above-average SST anomalies occurred in the western and central equatorial Pacific, but were milder near the coast of South America. While operational ENSO systems have progressed markedly over the past several decades, the 2015-16 El Niño highlights several challenges that will continue to test both the research and operational forecast communities.
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Documenting the heterogeneity of rainfall regimes is a prerequisite for water resources management, mitigation of risks associated to extremes weather events and for impact studies. In this paper, we present a method for regionalization of rainfall over the Peruvian Pacific slope and coast, which is the main economic zone of the country and concentrates almost 50% of the population. Our approach is based on a two-step process based on k-means clustering followed by the regional vector method (RVM) applied to a network of 145 rainfall stations covering the period 1964–2011. The advantage of combining cluster analysis and RVM is demonstrated compared with just applying each of these methods. Nine homogeneous regions are identified that depict the salient features of the rainfall variability over the study area. A detailed characterization of the rainfall regime in each of the identified regions is presented in response to climate variability at seasonal and interannual timescale. They are shown to grasp the main modes of influence of the El Niño Southern Oscillation (ENSO), that is, increased rainfall over downstream regions in northern Peru during extreme El Niño events and decreased rainfall over upstream regions along the Pacific slope during central Pacific El Niño events. Overall our study points to the value of our two-step regionalization procedure for climate impact studies.
This study aims to relate the intra-seasonal rainfall variability over the Amazon basin to atmospheric circulation patterns (CPs), with particular attention to extreme rainfall events in the Amazon–Andes region. The CPs summarize the intra-seasonal variability of atmospheric circulation and are defined using daily low-level winds from the ERA-Interim (1.5° × 1.5°) reanalysis for the 1979–2014 period. Furthermore, observational data of precipitation and high-resolution TRMM 3B42 (∼25 km), 2A25 PR (∼5 km) and CHIRPS (∼5 km) data products are related to the CPs throughout the Amazon basin. Nine CPs are determined using a hybrid method that combines a neural network technique (self-organizing maps, SOM) and hierarchical ascendant classification. The CPs are characterized by a specific cycle with alternative transitions and a duration of 14 days on average. This configuration initially results in northerly winds to southerly winds towards the northern or eastern Amazon basin. The related rainfall suggests that it is driven mainly by CP dynamics. In addition, we demonstrate a good agreement amongst the four rainfall data sets: observed precipitation, TRMM 3B42, TRMM 2A25 PR and CHIRPS. Furthermore, special attention is given to the Amazon–Andes transition region. Over this region, two particular CPs (CP4 and CP5) are identified as the key contributors of maximum and minimum daily rainfall, respectively. Thus, during the dry season, 40.8% (11.4%) of the CP5 (CP4) days demonstrate rainfall of less than 1 mm day−1, while during the wet season, 6.2% (14.6%) of the CP5 (CP4) days show rainfall amounts higher than the seasonal 90th percentile (10.4 mm day−1). This study provides additional information concerning the intra-seasonal circulation variability in Amazonia and demonstrates the value of using remote sensing precipitation data in this region as a tool for forecast in areas lacking observable information.
El Niño in the eastern and central Pacific has different impacts on the rainfall of South America, and the atmospheric pathways through the South Pacific Convergence Zone (SPCZ) and Inter-Tropical Convergence Zone (ITCZ) are poorly understood. To address this, we performed linear regression analysis of E (eastern Pacific) and C (central Pacific) indices of sea surface temperature (SST), as well as precipitation indices for the SPCZ and ITCZ, with gridded precipitation and reanalysis data sets during the austral summer (December–February) for the 1980–2016 period. Positive C induces dry anomalies along the tropical Andes and northern South America (NSA), while wet anomalies prevail over southeastern South America (SESA). Moreover, it produces wet conditions in the northwestern Peruvian Amazon. In contrast, positive E enhances wet conditions along the coasts of Ecuador and northern Peru associated with the southward displacement of the eastern Pacific ITCZ and induces dry conditions in Altiplano, Amazon basin, and northeastern Brazil (NEB). Both El Niño Southern Oscillation (ENSO) indices are associated with weakened upper-level easterly flow over Peru, but it is more restricted to the central and southern Peruvian Andes with positive E. Both SPCZ indices, the zonal position of the SPCZ and its latitudinal displacement, suppress rainfall along western Peruvian Andes when are positive, but the latter also inhibits rainfall over the Bolivian Altiplano. They are also linked to upper-level westerly wind anomalies overall of Peru, but these anomalies do not extend as far south in the first. The southward displacement of the eastern Pacific ITCZ also induces wet anomalies in SESA while dry anomalies prevail over NEB, the western Amazon basin, and Bolivia. Oppositely, the southward displacement of the central Pacific ITCZ induces dry anomalies in NEB and along the northern coast of Peru; while wet anomalies occur mainly in eastern Brazil, Paraguay, and Bolivia through an enhancement of the low level jet.