###### Abstract

The Atacama desert stands as the most arid, non-polar, region on Earth and has
accommodated a considerable portion of the world’s ground-based astronomical
observatories for an extended period. The comprehension of factors important
for observational conditions in this region, and the potential alterations
induced by the escalating impact of climate change, are, therefore, of the
utmost significance. In this study, we conduct an analysis of the surface-
level air temperature, water vapour density, and astronomical seeing at the
European Southern Observatory (commonly known by its acronym, ESO) telescope
sites in northern Chile. Our findings reveal a discernible rise in temperature
across all sites during the last decade. Moreover, we establish a correlation
between the air temperature and water vapour density with the El Niño Southern
Oscillation (ENSO) phases, wherein, the warm anomaly known as El Niño (EN)
corresponds to drier observing conditions, coupled with higher maximum daily
temperatures favouring more challenging near-infrared observations. The
outcomes of this investigation have potential implications for the enhancement
of the long-term scheduling of observations at telescope sites in northern
Chile, thereby aiding in better planning and allocation of resources for the
astronomy community.

###### keywords:

climate change; Atacama; atmospheric effects; ENSO; methods:data analysis;
northern Chile; telescope sites

1 1 0 Academic Editor: Firstname Lastname 10 August 2023 13 September 2023 17
September 2023 https://doi.org/ On the Impact of ENSO Cycles and Climate
Change on Telescope Sites in Northern Chile On the Impact of ENSO Cycles and
Climate Change on Telescope Sites in Northern Chile Julia Victoria Seidel
1,*,†,, Angel Otarola 1 and Valentina Théron 1,2 Julia Victoria Seidel and
Angel Otarola and Valentina Théron Seidel, J.V.; Otarola, A.; Théron, V.
Academic Editor: Marc Sarazin Correspondence<EMAIL_ADDRESS>ESO Fellow.

##  1 Introduction

The Atacama climate is primarily influenced by large-scale subsidence at
subtropical latitudes. However, the combination of the Andean slope and the
adjacent cold Humboldt stream in the Pacific Ocean also makes a significant
contribution Rutllant et al. (2003). This interaction results in an
atmospheric marine boundary layer characterised by extremely dry and cloud-
free air above it (relative humidity below $15\%$), providing favourable
conditions for astronomical observations (Muñoz et al., 2011).

It is precisely this fortuitous geography, with extremely dry and cloud-free
air at high altitudes that makes the Atacama desert the primary host of world-
class astronomical facilities in the world. Not only does northern Chile
already host the vast majority of 8 m class visible and near-infrared
telescopes, but it is also the construction site of the world’s first 40 m
class telescope, the Extremely Large Telescope (ELT). Important factors for
the data quality in cutting-edge astronomy are the integrated absolute
humidity (precipitable water vapour, PWV), together with meteorological
conditions, such as the surface air temperature, wind speed, and relative
humidity (Schöck et al., 2009; Vernin et al., 2011). PWV and its proxy, the
surface water vapour density (WVD), are relevant in the near- to mid-infrared
range, as water vapour affects the astronomical observations via atmospheric
absorption and thermal emissions (Bustos et al., 2014; Otarola et al., 2015).
This means the absorption of light by the water in Earth’s atmosphere
significantly reduces the light received by telescopes, especially in the
near-infrared. Observing schedules at the European Southern Observatory, the
focal point of this work, are created up to half a year in advance when
weather conditions are still unclear. Therefore, if too many observational
programmes with strong constraints on precipitable water vapour are scheduled,
but then atmospheric conditions do not allow for them, the telescopes will end
up with fewer observable programmes in the scheduling period than were needed.
In other words, during observing nights with adequate but not excellent
observing conditions, where less restrictive observations could have been
observed, the telescopes are needlessly sitting idle. This lost time is aptly
called idle time and should be avoided as much as possible. To this date, the
astronomy community has no metric on which to base the average conditions
beyond the known annual seasonal variations, and the impact of climate change
on the future of infrared programmes is uncertain at best.

In addition to the scheduling issue, the overall quality of observations
during a specific night is an important metric to assess the suitability of an
observing site. In the visible and near-infrared, astronomers employ the
seeing term for this purpose. In simple terms, seeing is a measure of the
angular broadening of an astronomical point-like source on the image plane due
to Earth’s atmospheric turbulence. It is expressed in arcseconds, a unit of
small angles common in astronomy, where one arcsecond is equal to
$\frac{1}{3600}$ of a sexagesimal degree. The smaller the seeing magnitude the
better resolved the image of the observed object. Excellent seeing for
astronomical research is defined as seeing below $0.6"$ and indicates a near-
motionless image. Median level seeings range from $0.6"$ to $0.9"$. Acceptable
seeing ranges from $1.5"$ up to $2.0"$ at which levels the image starts to
become more distorted. Important factors affecting the turbulent mixing of the
atmospheric layers, and hence affecting the seeing, are the vertical gradients
of the horizontal wind speed and temperature gradients (Melnick et al., 2009,
and references therein). It remains a topic of much interest to know whether
astronomical seeing is affected by long-term climate oscillations or climate
change, a point to which we aim to contribute in this manuscript. In the
context of the long timescale and associated cost for the construction and the
subsequent operation of the ELT in the next half century, an assessment and
hopefully a predictive tool of overall atmospheric conditions and seeing is
crucial for the community.

In this work, we aim to fill this knowledge gap for the astronomy community by
assessing the suitability of ENSO as a predictive tool for interannual
condition changes. The prominent feature of the El Niño (EN) phase of ENSO is
the warming of the tropical Pacific, quantified by the Niño3.4 index, leading
to increased near-surface air temperatures down to 30∘ S—encompassing all
observatories in this study along the Pacific coast of South America. Its
counterpart, La Niña (LN), is marked by opposite conditions. While EN phases
have been correlated with higher precipitation in the 30∘–41∘ S latitudes band
(Montecinos and Aceituno, 2003), the subtropical region, where the Atacama
Desert lies, has been correlated with dryer conditions (Rojas-Murillo et al.,
2022).

Hence, we assess the impact of ENSO phases and global warming trends on WVD
and temperature for European Southern Observatory sites, providing essential
metrics for the community to evaluate long-term observational conditions in
northern Chile. We propose strong ENSO phases as a metric to better schedule
challenging observing programmes, but find no significant impact of global
warming on astronomical site quality. However, we confirm the globally seen
warming trend and explore local effects on high altitude sites and curious
local cooling effects impacting the Paranal Observatory specifically. With
regards to seeing, we show that the median seeing conditions remain stable
within the natural inter-annual variability, a crucial result in the context
of the ELT. The manuscript is structured as follows: In Section 2, we provide
an overview of the sites, the acquired data, and the analysis methods. In
Section 3, we link the data to climate indices and explore the connection to
climate change. We then provide important conclusions for the astronomy
community in Section 4 where we highlight the power of established climate
indices to prevent time loss due to sub-optimal planning and provide an
outlook for future studies to monitor seeing in a warming climate.

##  2 Materials and Methods

###  2.1 Data Collection and Accessibility

For this study, we collected data from three different observatories at four
different sites (see overview in Table 1). We provide instructions on how to
access all the data in this study in the Data Availability Statement at the
end of the manuscript. An overview of the coverage and further details are
presented in Table 2.

Table 1: Location of all European Southern Observatory sites used for data collection in this study. Observatory | Latitude [∘S] | Longitude [∘W] | Elevation [MASL]
---|---|---|---
la Silla | 29.258 | 70.738 | 2400
Paranal | 24.627 | 70.404 | 2650
Llano del Chajnantor (CBI) | 23.0333 | 67.7667 | 5080
Llano del Chajnantor (APEX) | 23.0058 | 67.7592 | 5150
Table 2: Data provided from the respective meteo station (T and RH at 2m)

Site |  Obs Period |  Time Resolution |  $\\#$Days with Obs |  % of Coverage
---|---|---|---|---
la Silla |  10-May to 23-April |  1 min |  4740 |  $91.20\%$
Paranal |  98-July to 23-April |  1 min |  13,997 1 |  $91.86\%$ 1
|  84-November to 98-June |  20 min to h + |  - |  -
CBI |  99-April to 5-July |  1 h |  $2867$ |  $77.01\%$
APEX |  6-January to 23-April |  1 min |  $6050$ |  $98.61\%$

1 Combining historical and current data.

####  2.1.1 La Silla

The Astronomical Weather Station (AWS) started routine operation in September
1991 and full automation was reached in December 1993. From October 1988 to
February 1991, measurements were conducted from Cerro Vizcachas, 7 km to the
east of La Silla, which was once a candidate site for the Very Large Telescope
(VLT). In 2008, the AWS was struck by lightning and had to be replaced by a
new weather station and sensors. This replacement was completed in May 2010.
However, the data before this event show a marked offset in pressure, jumping
from previously approximately 774 hPa median pressure to 769 hPa median
pressure. This change is also visible in different temperatures and dew points
before and after the event. It is unclear whether this difference is due to a
change in the location of the AWS (no such change was recorded) or if the
values were previously recorded at 30 m and then at 2 m, but saved in the
database as a continuous series at 2 m. Due to this inconsistency, we focus
our study on the data after the lightning strike, with the new AWS station
(2010 and beyond).

####  2.1.2 Paranal Observatory

The temperature at different altitudes for the Paranal Observatory, as well as
the relative humidity and the wind direction, were obtained from the European
Southern Observatory ambient query form. However, the ambient query form only
contains data from the Vaisala Meteorological station at the Paranal
Observatory since August 1998 when the station was upgraded. We obtained the
historical values since its installation in October 1984 from European
Southern Observatory internal documents. Measurements were suspended from July
1991 to September 1992 for the 14-month duration of the levelling work of the
Paranal Observatory peak to make the platform needed to install the VLT. No
offsets were found in the data due to this intervention. Values are sparse in
the historical data until April 1994 (one or two daily measurements) and then
show a 20-minute cadence. The sparse data are not taken into account in our
analysis but are available upon request. No change points were found between
the historical data and the current data.

The optical turbulence at the Paranal Observatory is measured with the help of
two instruments that share the same optical tube assembly. The integrated
turbulence along the atmospheric column is measured with a differential image
motion monitor (DIMM) instrument (Sarazin and Roddier, 1990). Sharing the
field-of-view is the multi-aperture scintillation sensor (MASS) instrument
(Tokovinin and Kornilov, 2007), which provides a measure of the vertical
profile of the optical turbulence. The MASS helps to obtain the integrated
seeing in the troposphere, 250m above the surface level and beyond. Hence,
these instruments have proven important to monitor both the integrated
turbulence in the whole atmosphere above the DIMM level (currently 7 m above
the surface) and the seeing in the free-troposphere.

###  2.2 Paranal Observatory Precipitable Water Vapour and Water Vapour
Density Calculation

The PWV measurements from the Paranal Observatory were obtained via two
sources. Starting from April 2015, PWV measurements were performed with the
LHATPRO (Low Humidity And Temperature PROfiling, (Kerber et al., 2012))
instrument. This is a microwave radiometer covering the strong water vapour
emission line at 183 GHz, and providing a record of measurements with a time
resolution of a few seconds. Historical values are available at a lower time
sampling from the observation of the equivalent width telluric standard stars
under clear sky conditions with the XSHOOTER and UVES instruments. PWV
measurements from XSHOOTER, at an average sampling rate of every three hours,
are available from October-2009 to March-2020, while one daily measurement or
less from UVES is available from April-2000 to September-2020. The data, as
monthly median values for the Paranal Observatory, are shown in Figure 1; no
augmentation in minimum PWV with time is discernible. When data from more than
one measurement were available, we verified that the difference between the
data was less than $1\sigma$ and then provided the mean in the plot. No
conflicting data points were found and the measurements from XSHOOTER and UVES
confirmed the new LHATPRO measurements. The PWV of the Chajnantor site has
been studied in detail in Cortés et al. (2020). Unfortunately, for the la
Silla Observatory, no such observations exist. Given that we are mainly
interested in the impact on surface conditions at telescope sites, we use the
water vapour density (WVD) as a proxy for all three sites.

Figure 1: Monthly median PWV at the Paranal Observatory. No significant
incremental trends for minimum PWV are discernible. However, for the annual
minimum PWV, a marginal seasonal pattern on a decadal scale might be inferred.
This pattern remains statistically non-significant.

The water vapour density (WVD) is calculated from the relative humidity (RH)
and the temperature (T) at the altitude of the meteorological sensors station.
The water vapour pressure at saturation $e_{\mathrm{sat}}$ is derived from
these two parameters via the solution of the Clausius–Clapeyron equation from
Buck (1981):

$e_{\mathrm{sat}}=6.1121\cdot\exp{\frac{17.502T_{c}}{240.97+T_{c}}}\cdot
100\mathrm{[Pa]}$ (1)

where $T_{c}$ is the temperature in degrees Celsius. The nominal water vapour
pressure is then

$e_{0}=e_{\mathrm{sat}}\cdot\frac{\mathrm{RH}}{100}\mathrm{[Pa]}$ (2)

and, following the ideal gas law,

$\mathrm{WVD}=\frac{e_{0}}{R_{v}T_{K}}\cdot
1000[\frac{\text{g}}{\text{m}^{3}}]$ (3)

with $T_{K}$ the temperature in Kelvin, and $R_{v}$ as the specific gas
constant for water vapour ($461.5~{}\frac{J}{K\cdot\mathrm{kg}}$), the WVD can
be derived for all three observatory sites.

###  2.3 Paranal Observatory Turbulence Quantification

We use the computed surface level temperature and wind speed gradients to
produce an overall estimate of the Richardson number as a proxy for
turbulence. The Richardson number is the result of taking the ratio between
the buoyancy forces and the square of the wind shear, and is computed as
follows:

$\mathscr{R}i=\frac{g}{\theta}\frac{\frac{d\theta}{dh}}{(\frac{dU}{dh})^{2}}\mathrm{[adimentional]}$
(4) $\theta=T\mathrm{[K]}(\frac{P_{0}}{P})^{R/c_{p}}\mathrm{[K]}$ (5)

where $g$ is the acceleration of gravity (9.8
$\frac{\text{m}}{\text{s}^{2}}$), $\theta$ is the mean air potential
temperature, $d\theta/dh$ is the vertical gradient of air potential
temperature, and $dU/dh$ is the vertical gradient of the horizontal wind
speed. $P_{0}$ is the pressure at a reference altitude level (the sea level,
$\sim$1013 hPa), R is the gas constant of mixed air ($287.05$
$\frac{\text{J}}{\text{K}\cdot\mathrm{kg}}$), and $c_{p}$ is the specific heat
capacity at constant pressure ($1005.0$
$\frac{\text{J}}{\text{K}\cdot\mathrm{kg}}$).

###  2.4 Missing Values and Consistency

Some of the historical data show sections of missing data, some as large as
years, but mostly one value or two during the day. In the cases where only one
value was missing consecutively, we applied a linear interpolation before
building monthly sums or means. In cases where more than one value was missing
in a row, the missing value was treated like a propagating NaN. For
consistency, we checked that the maximum daily value was not below the minimum
daily value of the previous day (Hunziker et al., 2017). No inconsistent
values were found. In order to avoid an undue influence of outliers on our
analysis of the minimum and maximum values, we calculate extremes as the
$0.5\%$ and $99.5\%$ percentile values, respectively.

###  2.5 Trends and Correlation Calculation

The seasonal trends were calculated for the austral seasons: summer (December
to February, DJF), autumn (March to May, MAM), winter (June to August, JJA),
and spring (September to November, SON). The summer season thus commences in
the December of the previous calendar year. As we are principally interested
in the impact on astronomical sites, the data were separated into day and
night by calculating the time when the sun zenith angle was $90^{\circ}$ at
the telescope site (i.e., above the horizon). Considering that temperatures
usually follow non-normal distributions, we applied the non-parametric
Mann–Kendall test to estimate trends (J., 1949) with the removal of potential
auto-correlation in time series (Yue and Wang, 2004), as implemented in
(Hussain and Mahmud, 2019). In this implementation, trends are established
with a Kendall’s $\tau$-based slope estimator (Sen, 1968), with statistical
significance set to the $5\%$ level (_p_ -value). We estimated the correlation
between our observed parameters and modes of climate variability with a
Student’s _t_ -test. The resulting Pearson’s correlation coefficient (PCC) is
shown, together with the _p_ -value. The significance level of the correlation
is set at $5\%$ (_p_ -value).

###  2.6 Climate Indices

The general atmospheric circulation can be disturbed by internal modes—large-
scale disruptions due to the unstable coupling of the atmosphere–ocean system.
This cyclical climate variability plays an important role in South America’s
climate on both sides of the Andean Cordillera, as recently highlighted in
Gómez-Fontealba et al. (2023), where changes in the surface wind of western
Patagonia were linked to ENSO and PDO modes. The three most important large-
scale modes of climate variability in South America are described as the
Antarctic circulation (AAO, (Thompson and Wallace, 2000)), and the
aforementioned long-term Pacific decadal oscillation (PDO, (Mantua et al.,
1997)) and El Niño-Southern Oscillation (ENSO, (Smith and Sardeshmukh, 2000)).
In this work, we will mostly focus on ENSO and provide only a surface
definition of the other two phenomena and their impact.

The AAO is the main component of the 850 hPa geopotential height anomalies
south of $20^{\circ}$S but also influences temperature and precipitation
anomalies in South America, e.g., the AAO is associated with precipitation
anomalies in southern Chile and the subtropical east coast of the continent
(Garreaud et al., 2009). The PDO is the main reason for the monthly sea
surface temperature anomalies in the Pacific Ocean (north of 20∘ N) with a
decadal oscillation between warm and cold phases. The ENSO operates on shorter
cycles ranging between 2 and 7 years and is typically separated into two
anomaly cycles: El Niño (EN, warm phase) and La Niña (LN, cold phase), where
occurrences in the EN phases are usually roughly inverse to the LN phases. The
ENSO is monitored via various indices with the Niño3.4 index the most
appropriate choice for northern Chile (Böhm et al., 2020). Niño3.4 quantifies
the SST anomaly between 5∘ N–5∘ S and between 170–120∘ W and was obtained from
the National Oceanic and Atmospheric Administration of the U.S. Department of
Commerce as a monthly mean time series. An EN or LN phase is declared when the
temperature anomaly deviates $>|0.5\,^{\circ}\text{C}|$ from the historical
mean. We define strong LN or EN phases by a deviation of
$>|0.75\,^{\circ}\text{C}|$ from the historical mean.

##  3 Results

###  3.1 European Southern Observatory Sites Temperature Trends

All sites in our study are found at geographical elevations 2400 m above sea
level (MASL). However, both the la Silla and the Paranal Observatory are close
to the coastal line and at the lower boundary of high-elevation sites. On the
other hand, the Chajnantor site lies at over 5000 MASL on the western slope of
the Andes mountains. This allows us to provide some insights into how
temperature trends change with altitude. The significance of temperature
trends, as a function of site (altitude), was assessed using the Mann–Kendall
(MK) test. See Appendix A for the tables for all sites together with a more
in-depth analysis of the temporal warming variations for the Paranal
Observatory (Appendix A-Tables 7 and 9).

Our results show a significant increase in temperatures at the Chajnantor
site, with rising trends for temperatures during the austral summer. These
trends confirm the projected altitude-dependent warming acceleration, both
globally (Pepin et al., 2016) and for future observatories Haslebacher et al.
(2022). In the austral autumn, the minimum temperatures at the Chajnantor site
exhibit an increase of $~{}1\,^{\circ}\text{C}/$decade, twice as fast as any
other parameter. A consistent rise in the minimum temperature during the
austral autumn (and the maximum temperature in summer) extends across all
sites. Previous studies on climate trends in Chile have demonstrated
substantial warming during the austral autumn and summer while suggesting
cooling during spring and winter (Burger et al., 2018) linked to climate
change (Rangwala and Miller, 2012). Our findings corroborate a strong warming
trend, specifically evident in the minimum temperature during autumn, which
implies reduced cool periods leading up to winter. This indicates a
lengthening of the ablation season in the high Andes with an impact on the
glacier mass balance (Burger et al., 2018). Nonetheless, the climate forcing
responsible for glacial shrinkage in Chile is a complex subject (Masiokas et
al., 2016) and beyond the scope of this work.

While a definitive warming trend attributed to global warming is evident
across European Southern Observatory sites, annual-level variations cannot be
solely ascribed to climate change. We investigated the link between extreme
warm events when the daily maximum ambient temperature exceeded the 90th
percentile of the long-term temperature record and ENSO (Figure 2). Our
findings reveal a distinct correlation between the occurrence of warm surface
temperature anomalies and the Niño3.4 index across sites. Further details on
the correlations for all sites can be found in Table 3 and in Appendix A.

Figure 2: Days in a month with the ambient temperature at 2m above surface above the long-term 90th percentile for all sites studied. Values below the thresholds are shaded in blue, and values above the threshold are shaded in red. The lowest two panels show the Niño3.4 index and the PDO (Pacific Decadal Oscillation) index.  Table 3: Student’s _t_ -test for correlation between El Niño3.4 anomaly and sunset temperature outliers (see Figure 2).  Observatory |  PCC |  _p_ -Value |
---|---|---|---
Chajnantor (CBI+APEX) |  $0.192$ |  $0.004$ |  correlation
Paranal |  $0.453$ |  $0.000$ |  correlation
la Silla |  $0.228$ |  $0.012$ |  correlation

###  3.2 European Southern Observatory Sites Surface Water Vapour Density
Trends and Precipitable Water Vapour

The surface water vapour density (WVD) in the Paranal and the la Silla
Observatories is anti-correlated with ENSO (see Table 4). Years during strong
EN phases show no surface-level wet periods, while there are a larger number
of days with temperatures above the long-term 90th quantile, e.g., in 1998, in
2010, and in 2016 (see Figure 3). While a decrease in WVD is also observed for
the Chajnantor site during strong EN phases, the overall WVD is extremely low,
and no correlation could be determined in our short data series. The most
important site to quantify is the Paranal Observatory due to its close
proximity to the construction site (Cerro Armazones) of the future Extremely
Large Telescope (ELT) Gilmozzi and Spyromilio (2007).

The monthly median values of precipitable water vapour (PWV) for the Paranal
Observatory are shown in Figure 1 and no augmentation in minimum PWV with time
is discernible; however, a marginal decadal oscillation can be inferred. If
anything, what is clearly visible is the variability in the maximum PWV events
during the austral summers. This variability is most likely influenced by the
predominant position of the Bolivian high-pressure centre during summer months
Garreaud (2011); Krishnamurti et al. (2013); Segura et al. (2020), which leads
to dry anomalies during the normally wet summer for the combination of
EN/PDO(+) Rojas-Murillo et al. (2022). The PWV long-term record for the
Chajnantor site was analysed in Cortés et al. (2020), and no evidence of PWV
trends over the 20 years of data was found, in agreement with our results for
the Paranal Observatory.

Figure 3: The El Niño3.4 anomaly is shown in yellow with the number of days
with sunset temperature above the 90th quantile (red line) and the WVD (blue
dot) overlaid on the same scale as EN3.4 for the Paranal Observatory. The WVD
is displayed as the wettest yearly period, the month each year with the
highest mean WVD. A clear correlation is visible between positive ENSO
anomalies and extremely warm and dry periods.  Table 4: Student’s _t_ -test
for correlation between el Niño3.4 anomaly annual maximum value of WVD monthly
medians.

Observatory |  PCC |  _p_ -Value |
---|---|---|---
Chajnantor (APEX) |  - |  $0.385$ |  no correlation
Paranal |  $0.134$ |  $0.022$ |  anti-correlation
la Silla |  $0.380$ |  $0.000$ |  anti-correlation

###  3.3 The Impact on Astronomical Seeing

In observational astronomy, and as a more technical addition to the definition
in Section 1, seeing is a term that refers to the broadening in the point-
spread-function (PSF) of a point-like source (a star) attributed to
atmospheric optical turbulence. The broadening occurs due to the scattering of
light away from the core of the PSF because of rapid fluctuations in the air
index of refraction along the path the starlight follows through the
atmosphere to an imaging detector.

In optical wavelengths, the index of refraction fluctuation is dominated by
the turbulent mixing of air of different temperatures Ciddor (1996). The
effects of turbulent fluctuations of humidity in the air index of refraction
are more relevant for light in the infrared and radio wavelengths Thayer
(1974). In this paper, we refer to seeing, in the optical band, more
specifically as measured by the differential image motion monitor (DIMM)
instrument at a wavelength of 500 nm. In simple terms, seeing is a measure of
the angular size, in arcsecs, of the PSF at the half-intensity point and at
the first order driven by changes in wind direction and temperature gradients.
While it is understood in detail how seeing is influenced by environmental
parameters, predictive tools for seeing on longer time scales are not
available to the community.

In the context of the ELT, the impact of climate variations on atmospheric
turbulence and, thus, seeing at the Paranal Observatory is of the utmost
importance. As stated above, the temperature gradient has an important impact
on seeing. This gradient is naturally largest at sunset when the surface and
telescope domes are heated from the strong solar irradiation in the Atacama
desert. The authors of (Cantalloube et al., 2020) studied the amount of time
each year that the sunset temperature was above 16 ∘C for the period
2008–2020. The value of 16 ∘C is the maximum temperature at which the
telescope dome environments can be maintained while still providing safe
cooling to the electronics equipment. We have expanded the analysis to include
the full available time series 1994 to 2022 and confirm their trends (see
Figure 4). The yearly fraction of sunset surface air temperatures exceeding 16
∘C has increased at a rate of over $2\%$ per decade, reaching up to a 20–25%
yearly fraction during the last EN phase in 2018–2019. This trend is in line
with global warming, as confirmed via the MK statistical test. Because of the
necessary cooling within the telescope dome environments, on warmer days
(temperatures at sunset above 16 ∘C), the domes are cooler than the external
ambient temperature at the time of dome opening. In (Cantalloube et al.,
2020), it is inferred that this particular temperature gradient leads to
stronger turbulence. However, we may argue that the internal and cooler
atmosphere inside the dome is stratified and convection is, thus, inhibited.
That said, this may require a specific study on the science image quality as a
function of the difference in temperature between the internal dome
environment and the external air. At least, in terms of atmospheric seeing,
our study of evening and morning twilight seeing compared to total seeing for
the most affected years (2016–2021) shows that the fraction of high-seeing
events ($>$1.6 arcseconds) for the evening twilight is equal to the total
seeing statistics (see Figure 5 and Table 11).

Figure 4: The fraction of the year where the sunset air temperature at 2 m is
above 16 ∘C as black dots, expanding Figure 1c from Cantalloube et al. (2020),
which spanned the years 2008–2020. The magenta line marks a linear fit to the
data to indicate the long-term trend.

Furthermore, and motivated by the work of Cantalloube et al. (2020), we
analysed the near surface level temperature record to check for possible
trends in temperature gradients between the 30 m and 2 m height from the
ground level. Similarly, we also analysed the vertical gradient in the
horizontal wind speed from wind measurements performed at 30 m and 10 m above
ground level. The long-term record for the monthly means of the vertical
differences in temperature and horizontal wind speed are shown in Figure 6.
Our results show seasonal variability, but, overall, the temperature and
horizontal wind speed gradient have remained stable, within their natural
variability. For completeness, the gradients are shown together with the ENSO
index. This result on the wind speed and temperature gradient expands the
exhaustive work on the relation of seeing and wind speed and temperature
gradients in the context of climate change from 2009 at the Paranal
Observatory (Melnick et al., 2009). In that work, the authors analysed the
data from 1990 to 1998 and from 1998 to 2008 and concluded that climate change
had no significant impact on seeing in this time period. Additionally, and
most importantly, they concluded that the Armazones site, now host of the ELT
construction, was unaffected both by seeing changes due to climate change or
due to EN or LN phases. We expand their findings with complementary data from
the decade following their study.

Figure 5: Mean seeing probability density function (PDF) from Table 11
comparing morning and evening seeing on the left and the same normalised
function for strong EN and LN events from Tables 12 and 13. The PDF, on the
right-hand side, is normalised such that all curves match the long-term median
of seeing at the Paranal Observatory. The figure helps to compare the relative
shapes of the probability density function curves. Figure 6: (top) Monthly
means of the air temperature difference between measurements at 30 m and 2 m
above ground level. (middle) Monthly means of the differences in the
horizontal wind speed measured at 30 m and 10 m above ground level. The bottom
plots, for completeness, show the time series of the El Niño index. Note: The
trends of wind differences are shown starting in 2002. The wind record prior
to 2002 shows some inconsistencies that produced a very small magnitude wind
gradient. It is possible that the measurements for the period prior to 2002
were not performed exactly at the heights above ground as reported in the
database, and, therefore, we opted to exclude this data.

To better understand the potential impact of ENSO on seeing, we studied the
seeing during the night, separating out especially strong EN/LN phases (see
Appendix B-Tables 12 and 13) and found no significant difference in seeing,
although dry EN phases showed less variance in seeing. Figure 7 shows the
seeing at the Paranal Observatory in the period 1998-2023. This time series is
affected by biases induced by construction and the proximity of the telescope
buildings to the former seeing monitor tower (1998-April 2016) (Cantalloube et
al., 2020), a position prone to the influence of surface-layer turbulence re-
circulation. The new seeing monitor instrument (DIMM) was then moved away from
the telescope building and raised up, from 5 m above ground level to 7 m. Due
to this improvement, the seeing came back to the best long-term values before
construction, showing that no gradual increase with time was taking place. In
the latest record, we may see evidence of an increase in the lowest seeing
values, but the time series is still too short to attempt a correlation with
ENSO or climate change.

Moreover, we explored a possible trend between the surface-level turbulence
differences between the EN and LN phases of ENSO via the Richardson number.
Our results, derived from the data in Figure 6 and taking $\theta$ = 314 K in
Equation (4), show a nearly constant long-term Richardson number of about
0.2–0.3, consistent with the earlier work of Melnick et al. (2009), and no
differences between the EN and LN periods.

Figure 7: Mean monthly seeing at the Paranal Observatory with various
important changes highlighted that impacted the mean seeing trend. No net
increase in seeing is discernible at this stage.

As well as the integrated (whole-atmosphere) seeing in Figure 7, we have
included the free-atmosphere seeing from 2016 onwards, which does not show any
particular trend either. Free-atmosphere seeing results from optical
turbulence from 250 m onwards, away from the surface layer, up to the top of
the troposphere. The MASS instrument allows measuring the free-atmosphere
seeing Tokovinin and Kornilov (2007), as well as other parameters important
for observational astronomy.

The period after 2020 was associated with LN conditions, and our results, as
shown in Appendix B, indicate a higher seeing variability during LN periods.
As we are entering (2023) into the next EN period, the future record of seeing
will show if the lowest seeing conditions decrease compared to the 2016-2020
period characterised by mainly neutral ENSO conditions. Nonetheless, it is
important to state that the seeing conditions in the Paranal and Cerro
Armazones region (ELT site) remain exceptional for seeing-limited astronomical
observations and, at large, are unaffected by ENSO and climate change,
confirming the results from a decade prior (Melnick et al., 2009).

##  4 Conclusions

From the analysis of meteorological data gathered at the European Southern
Observatory sites, we found a positive correlation of extremely surface-level
warm periods with ENSO across all sites, as well as an anti-correlation
between ENSO and the surface-level absolute humidity. In other words, during
EN phases, there was a high likelihood of warmer and dryer surface conditions.
Similarly, during LN, the surface meteorological conditions call for lower
maximum daily temperatures but higher absolute humidity. As a consequence, we
now, for the first time, have predicting capabilities to better schedule more
demanding infrared observation programs that cannot be executed in elevated
humidity. Conversely, during LN phases, when periods of wet and cloudy
conditions are more probable, we can plan for less restrictive programs.

Unrelated to astronomy, we confirm the overall trend of increasing
temperatures for all sites with rates of about or more than
$0.2^{\circ}$C/decade which are consistent with anthropogenic climate change
since 1980. Our results show a significant increase in temperatures at the
Chajnantor site in line with the acceleration of warming with altitude. These
long-term, high-altitude stations built for astronomy could potentially become
an important resource for the climate community as they fill an important gap
in station coverage Mutz et al. (2021). Regarding precipitable water vapour,
our data for the Paranal Observatory do not show an increase in any metrics
with time, confirming studies that have been conducted at the Chajnantor site.

While the trend in the absolute surface air temperature is evident, our
results do not show a long-term trend in the vertical gradient of air
temperature close to the surface, nor in the vertical gradient of the
horizontal component of wind speed. Their ratios, following the formalism of
the Richardson number, do not show a long trend or significant differences
between strong phases of ENSO either, implying that the ground-level
turbulence has remained within its natural seasonal variability, in line with
previously conducted studies on seeing.

Regarding a possible trend in astronomical seeing since then, unfortunately,
our long-term dataset for the Paranal Observatory is affected by biases that
are hard to correct without introducing systematic errors. Nonetheless, we
find no evidence for an increase in seeing or turbulence with time, contrary
to popular beliefs on astronomical data quality over time. However, it seems
that the spread in seeing is larger during strong LN phases, making conditions
during LN even more unpredictable. The record of seeing in the following years
will help elucidate a more direct impact of strong EN and LN events on seeing
and we will discuss any visible trends in follow-up work. Additionally, the
impact of the dome seeing on astronomical observations is not currently well
studied and should be measured systematically in the future.

In conclusion, our work serves to confirm the increasing warming trend with
altitude due to global warming, underscoring the unique exposure of
observatory sites to the challenges that lie ahead. Despite not finding any
direct impact on seeing yet, our research contributes valuable insights for
observatories in their preparation for the challenging future climatic
conditions, as well as a predictive tool via the ENSO phases for the
scheduling of challenging near-infrared programmes.

J.V.S. has led the data collection, analysis, and interpretation of the
temperature and WVD data, including the specific Paranal analysis of the 16
Celsius Degrees threshold, as well as the overall interpretation of the
results. J.V.S. has written the manuscript and is responsible for its
contents. A.O. proposed the project, led the discussion for data analysis and
interpretation, and produced the WVD data record, contributing also to the
writing and proofreading. V.T. led the analysis of the astronomical seeing
data at Paranal.

This research has received funding from the European Organisation for
Astronomical Research in the Southern Hemisphere (ESO).

Not applicable.

Not applicable.

Climate indices are available at www.psl.noaa.gov/data/climateindices/, recent
APEX, Paranal, and la Silla ambient conditions data from:
http://archive.eso.org/cms/eso-data/ambient-conditions.html, CBI data from
www.sites.astro.caltech.edu/~tjp/CBI/weather/index.html. The PWV Paranal
LHATPRO data can be queried at
www.archive.eso.org/wdb/wdb/asm/lhatpro_paranal/form. The historic PWV Paranal
UVES and XSHOOTER data can be retrieved at
www.eso.org/observing/dfo/quality/GENERAL/PWV/HEALTH/trend_report_ambient_PWV_closeup_HC.html.
All other historical data are available upon request either from the
corresponding author or from the European Southern Observatory climate
officer, A. Otarola. All websites were accessed in August 2023.

###### Acknowledgements.

The authors thank Alain Smette for retrieving the historic Paranal PVW data
and Aldo Pizarro for his knowledge of la Silla’s historic events. The
historical PVW data was calculated by Reinhard Hanuschick, the retired head of
ESO’s Quality Control, whom we thank for making this data publicly available.
This work relied on observations collected at the European Southern
Observatory (ESO). We thank the three referees and the editor for their
insightful comments which have improved the manuscript. The authors declare no
conflict of interest. Abbreviations The following abbreviations are used in
this manuscript:
AAO | Antarctic circulation
---|---
AMBL | atmospheric marine boundary layer
APEX | Atacama Pathfinder Experiment
CBI | Cosmic Background Imager
ESO | European Southern Observatory
---|---
ENSO | El Niño Southern Oscillation
FT | free troposphere
DJF | season December, January, February (austral summer)
JJA | season June, July, August (austral winter)
MAM | season March, April, May (austral autumn)
MASL | metres above sea level
NEO | near-Earth object
PCC | Pearson’s correlation coefficient
PDO | Pacific decadal oscillation
PWV | precipitable water vapour
SON | season September, October, November (austral spring)
SST | sea surface temperature
TI | temperature inversion
WVD | water vapour density
yes

##  Appendix A MK-Test of Temperature Trends across European Southern
Observatory Sites

Given our limitation in la Silla Observatory data to 2010 onwards (see Table
2), we have split the Paranal Observatory data into pre- and post-2010. The
pre-2010 Paranal Observatory temperature data, spanning approximately 15 years
from 1994 to 2009, exhibit decreasing temperature trends (compare Tables 7 and
9). The hiatus in warming during the 1990s and the first decade of the 21st
century for Chilean coastal and valley sites below 800 MASL is a well-studied
phenomenon (Falvey and Garreaud, 2009; Burger et al., 2018), most notably for
the maximum temperature as observed during autumn in the Paranal Observatory
with a strong negative slope. This cooling is related to a shift to the
negative phase of the Interdecadal Pacific Oscillation (IPO), caused by global
warming (Falvey and Garreaud, 2009), which has ended since then. This trend
has only been observed at altitudes below the Paranal Observatory; however,
the Paranal Observatory is less than 20 km direct distance from the coast. The
inversion layer that separates the coastal climate from the higher altitudes
of the Paranal Observatory is strongest in JJA, where we see no decreasing
trends for the pre-2010 Paranal Observatory data. We hypothesize that the
coastal cooling trend has only influenced the Paranal Observatory during
periods with a weaker atmospheric stratification. We have highlighted the most
prominent temperature trends with boldface in the tables.

Table 5: Temperature trends Llano del Chajnantor site, 2006–2023.

Season |  |  Trend |  _p_ -Value |  $\tau$ |  Slope
---|---|---|---|---|---
DJF (day) |  mean |  increasing |  $0.001$ |  $0.221$ |  $0.036$
|  max |  increasing |  $0.000$ |  $0.257$ |  $0.055$
|  min |  increasing |  $0.003$ |  $0.235$ |  $0.045$
DJF (night) |  mean |  increasing |  $0.000$ |  $0.250$ |  $0.040$
|  max |  increasing |  $0.000$ |  $0.353$ |  $0.069$
|  min |  increasing |  $0.025$ |  $0.176$ |  $0.043$
MAM (day) |  mean |  no trend |  $0.278$ |  - |  -
|  max |  no trend |  $0.888$ |  - |  -
|  min |  increasing |  $0.000$ |  $0.250$ |  $0.094$
MAM (night) |  mean |  no trend |  $0.412$ |  - |  -
|  max |  no trend |  $1.000$ |  - |  -
|  min |  increasing |  $0.000$ |  $0.221$ |  $0.112$

Table 6: Cont.

Season |  |  Trend |  _p_ -Value |  $\tau$ |  Slope
---|---|---|---|---|---
JJA (day) |  mean |  no trend |  $0.321$ |  - |  -
|  max |  no trend |  $0.778$ |  - |  -
|  min |  increasing |  $0.006$ |  $0.213$ |  $0.027$
JJA (night) |  mean |  increasing |  $0.027$ |  $0.176$ |  $0.029$
|  max |  no trend |  $0.092$ |  - |  -
|  min |  no trend |  $0.067$ |  - |  -
SON (day) |  mean |  increasing |  $0.045$ |  $0.150$ |  $0.026$
|  max |  no trend |  $0.292$ |  - |  -
|  min |  no trend |  $0.079$ |  - |  -
SON (night) |  mean |  increasing |  $0.001$ |  $0.267$ |  $0.049$
|  max |  increasing |  $0.009$ |  $0.200$ |  $0.025$
|  min |  increasing |  $0.012$ |  $0.200$ |  $0.050$

Table 7: Temperature trends Paranal Observatory, 1994–2009.

Season |  |  Trend |  _p_ -Value |  $\tau$ |  Slope
---|---|---|---|---|---
DJF (day) |  mean |  no trend |  $0.111$ |  - |  -
|  max |  increasing |  $0.004$ |  $0.361$ |  $0.110$
|  min |  decreasing |  $0.013$ |  $-0.190$ |  $-0.030$
DJF (night) |  mean |  no trend |  $0.515$ |  - |  -
|  max |  increasing |  $0.012$ |  $0.257$ |  $0.091$
|  min |  decreasing |  $0.000$ |  $-0.324$ |  $-0.083$
MAM (day) |  mean |  no trend |  $0.135$ |  - |  -
|  max |  decreasing |  $0.000$ |  $-0.253$ |  $-0.095$
|  min |  increasing |  $0.003$ |  $0.198$ |  $0.078$
MAM (night) |  mean |  no trend |  $0.676$ |  - |  -
|  max |  decreasing |  $0.029$ |  $-0.187$ |  $-0.070$
|  min |  no trend |  $0.222$ |  - |  -
JJA (day) |  mean |  no trend |  $0.496$ |  - |  -
|  max |  no trend |  $0.162$ |  - |  -
|  min |  no trend |  $0.205$ |  - |  -
JJA (night) |  mean |  no trend |  $0.062$ |  - |  -
|  max |  no trend |  $0.122$ |  - |  -
|  min |  increasing |  $0.010$ |  $0.282$ |  $0.196$
SON (day) |  mean |  no trend |  $0.205$ |  - |  -
|  max |  no trend |  $0.565$ |  - |  -
|  min |  no trend |  $0.403$ |  - |  -
SON (night) |  mean |  no trend |  $0.614$ |  - |  -
|  max |  no trend |  $0.428$ |  - |  -
|  min |  no trend |  $0.168$ |  - |  -

Table 8: Temperature trends Paranal Observatory, 2010 - 2023.

Season |  |  Trend |  _p_ -Value |  $\tau$ |  Slope
---|---|---|---|---|---
DJF (day) |  mean |  no trend |  $0.111$ |  - |  -
|  max |  increasing |  $0.004$ |  $0.362$ |  $0.110$
|  min |  no trend |  $0.261$ |  - |  -
DJF (night) |  mean |  no trend |  $0.515$ |  - |  -
|  max |  increasing |  $0.012$ |  $0.257$ |  $0.091$
|  min |  no trend |  $0.102$ |  - |  -
MAM (day) |  mean |  no trend |  $0.135$ |  - |  -
|  max |  decreasing |  $0.001$ |  $-0.187$ |  $-0.095$
|  min |  increasing |  $0.003$ |  $0.231$ |  $0.078$

Table 9: Cont.

Season |  |  Trend |  _p_ -Value |  $\tau$ |  Slope
---|---|---|---|---|---
MAM (night) |  mean |  no trend |  $0.676$ |  - |  -
|  max |  decreasing |  $0.029$ |  $-0.187$ |  $-0.054$
|  min |  increasing |  $0.002$ |  $0.253$ |  $0.230$
JJA (day) |  mean |  no trend |  $0.917$ |  - |  -
|  max |  no trend |  $0.162$ |  - |  -
|  min |  no trend |  $0.205$ |  - |  -
JJA (night) |  mean |  no trend |  $0.918$ |  - |  -
|  max |  no trend |  $0.702$ |  - |  -
|  min |  increasing |  $0.010$ |  $0.282$ |  $0.196$
SON (day) |  mean |  no trend |  $0.609$ |  - |  -
|  max |  no trend |  $0.410$ |  - |  -
|  min |  no trend |  $0.089$ |  - |  -
SON (night) |  mean |  no trend |  $0.714$ |  - |  -
|  max |  no trend |  $0.168$ |  - |  -
|  min |  no trend |  $0.212$ |  - |  -

Table 10: Temperature trends la Silla Observatory, 2010–2023.

Season |  |  Trend |  _p_ -Value |  $\tau$ |  Slope
---|---|---|---|---|---
DJF (day) |  mean |  no trend |  $0.293$ |  - |  -
|  max |  no trend |  $0.084$ |  - |  -
|  min |  no trend |  $0.421$ |  - |  -
DJF (night) |  mean |  no trend |  $0.064$ |  - |  -
|  max |  no trend |  $0.078$ |  - |  -
|  min |  no trend |  $0.764$ |  - |  -
MAM (day) |  mean |  increasing |  $0.000$ |  $0.275$ |  $0.168$
|  max |  no trend |  $0.131$ |  - |  -
|  min |  increasing |  $0.006$ |  $0.275$ |  $0.267$
MAM (night) |  mean |  increasing |  $0.003$ |  $0.231$ |  $0.153$
|  max |  no trend |  $0.438$ |  - |  -
|  min |  increasing |  $0.040$ |  $0.231$ |  $0.290$
JJA (day) |  mean |  no trend |  $0.285$ |  - |  -
|  max |  no trend |  $0.746$ |  - |  -
|  min |  no trend |  $0.380$ |  - |  -
JJA (night) |  mean |  no trend |  $0.138$ |  - |  -
|  max |  no trend |  $0.162$ |  - |  -
|  min |  no trend |  $0.119$ |  - |  -
SON (day) |  mean |  increasing |  $0.039$ |  $0.205$ |  $0.061$
|  max |  no trend |  $0.464$ |  - |  -
|  min |  increasing |  $0.000$ |  $0.397$ |  $0.382$
SON (night) |  mean |  no trend |  $0.325$ |  - |  -
|  max |  no trend |  $0.488$ |  - |  -
|  min |  increasing |  $0.000$ |  $0.449$ |  $0.256$

##  Appendix B Paranal Seeing Analysis

Table 11: Probability density function of mean seeing during the period 2016
to 2021.

Fraction |  Total Nights |  Morning Twilight |  Evening Twilight
---|---|---|---
$5\%$ |  $0.42$ |  $0.38$ |  $0.47$
$10\%$ |  $0.47$ |  $0.44$ |  $0.51$
$15\%$ |  $0.51$ |  $0.48$ |  $0.55$
$20\%$ |  $0.54$ |  $0.51$ |  $0.58$
$25\%$ |  $0.57$ |  $0.55$ |  $0.61$
$50\%$ |  $0.71$ |  $0.72$ |  $0.76$
$75\%$ |  $0.93$ |  $1.04$ |  $0.98$
$90\%$ |  $1.28$ |  $1.48$ |  $1.27$
$95\%$ |  $1.57$ |  $1.77$ |  $1.51$
$97.5\%$ |  $1.84$ |  $2.04$ |  $1.75$
$99\%$ |  $2.22$ |  $2.43$ |  $2.06$
$99.5\%$ |  $2.48$ |  $2.73$ |  $2.3$

Table 12: Spread of seeing for strong EN phases ($\delta
T>0.75\,^{\circ}\text{C}$).

Period |  Variance |  75–25% |  95–5%
---|---|---|---
2-July to December |  $0.13$ |  $0.42$ |  $1.14$
6-October to November |  $0.28$ |  $0.55$ |  $1.64$
9-October to 10-February |  $0.20$ |  $0.47$ |  $1.37$
15-May to 16-March |  $0.19$ |  $0.50$ |  $1.36$

Table 13: Spread of seeing for strong LN phases ($\delta
T<-0.75\,^{\circ}\text{C}$).

Period |  Variance |  75–25% |  95–5%
---|---|---|---
98-July to 00-March |  $0.19$ |  $0.45$ |  $1.39$
5-December to 6-January |  $0.13$ |  $0.40$ |  $1.12$
7-August to 8-April |  $0.24$ |  $0.59$ |  $1.46$
8-December to 9-January |  $0.24$ |  $0.55$ |  $1.46$
10-July to 11-February |  $0.34$ |  $0.54$ |  $1.66$
11-September to 11-December |  $0.18$ |  $0.48$ |  $1.27$
17-November to 18-January |  $0.12$ |  $0.33$ |  $1.10$
20-September to 21-February |  $0.13$ |  $0.38$ |  $1.12$

References

## References

  * Rutllant et al. (2003) Rutllant, J.A.; Fuenzalida, H.; Aceituno, P.  Climate dynamics along the arid northern coast of Chile: The 1997–1998 Dinámica del Clima de la Región de Antofagasta (DICLIMA) experiment.  J. Geophys. Res. Atmos. 2003, 108, 4538.  https://doi.org/10.1029/2002JD003357.
  * Muñoz et al. (2011) Muñoz, R.C.; Zamora, R.A.; Rutllant, J.A.  The Coastal Boundary Layer at the Eastern Margin of the Southeast Pacific (23.4°S, 70.4°W): Cloudiness-Conditioned Climatology.  J. Clim. 2011, 24, 1013–1033.  https://doi.org/10.1175/2010JCLI3714.1.
  * Schöck et al. (2009) Schöck, M.; Els, S.; Riddle, R.; Skidmore, W.; Travouillon, T.; Blum, R.; Bustos, E.; Chanan, G.; Djorgovski, S.G.; Gillett, P.; et al.  Thirty Meter Telescope Site Testing I: Overview.  Publ. Astron. Soc. Pac. 2009, 121, 384.  https://doi.org/10.1086/599287.
  * Vernin et al. (2011) Vernin, J.; Muñoz-Tuñón, C.; Sarazin, M.; Vazquez Ramió, H.; Varela, A.M.; Trinquet, H.; Delgado, J.M.; Jiménez Fuensalida, J.; Reyes, M.; Benhida, A.; et al.  European Extremely Large Telescope Site Characterization I: Overview.  Publ. Astron. Soc. Pac. 2011, 123, 1334.  https://doi.org/10.1086/662995.
  * Bustos et al. (2014) Bustos, R.; Rubio, M.; Otarola, A.; Nagar, N.  Parque Astronómico de Atacama: An Ideal Site for Millimeter, Submillimeter, and Mid-Infrared Astronomy.  Publ. Astron. Soc. Pac. 2014, 126, 1126–1132.  https://doi.org/10.1086/679330.
  * Otarola et al. (2015) Otarola, A.; Richter, M.; Packham, C.; Chun, M.  Atmospheric transmission and thermal background emission in the mid-infrared at Maunakea.  J. Phys. Conf. Ser. 2015, 595, 012023.  https://doi.org/10.1088/1742-6596/595/1/012023.
  * Melnick et al. (2009) Melnick, J.; Sarazin, M.; Lombardi, G.L.; Navarrete, J.  The Paranal surface layer.  In Proceedings of the Optical Turbulence Meets Meteorology, held on 15-18 September 2008 at Nymphes Bay, Alghero, Sardinia, Italy, 2009; pp. 136–143.  https://doi.org/10.1142/9781848164864_0017.
  * Montecinos and Aceituno (2003) Montecinos, A.; Aceituno, P.  Seasonality of the ENSO-Related Rainfall Variability in Central Chile and Associated Circulation Anomalies.  J. Clim. 2003, 16, 281–296.  https://doi.org/10.1175/1520-0442(2003)016<0281:SOTERR>2.0.CO;2.
  * Rojas-Murillo et al. (2022) Rojas-Murillo, K.; Lupo, A.R.; Garcia, M.; Gilles, J.; Korner, A.; Rivera, M.A.  ENSO and PDO related interannual variability in the north and east-central part of the Bolivian Altiplano in South America.  Int. J. Climatol. 2022, 42, 2413–2439.  https://doi.org/10.1002/joc.7374.
  * Sarazin and Roddier (1990) Sarazin, M.; Roddier, F.  The ESO differential image motion monitor.  Astron. Astrophys. 1990, 227, 294–300.
  * Tokovinin and Kornilov (2007) Tokovinin, A.; Kornilov, V.  Accurate seeing measurements with MASS and DIMM.  Mon. Not. R. Astron. Soc. 2007, 381, 1179–1189.  https://doi.org/10.1111/j.1365-2966.2007.12307.x.
  * Kerber et al. (2012) Kerber, F.; Rose, T.; Chacón, A.; Cuevas, O.; Czekala, H.; Hanuschik, R.; Momany, Y.; Navarrete, J.; Querel, R.R.; Smette, A.; et al.  A water vapour monitor at Paranal Observatory.  In Proceedings of the Ground-Based and Airborne Instrumentation for Astronomy IV, Amsterdam, The Netherlands, 1–6 July 2012; Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series; McLean, I.S., Ramsay, S.K., Takami, H., Eds., 2012; Volume 8446, p. 84463N.  https://doi.org/10.1117/12.924340.
  * Cortés et al. (2020) Cortés, F.; Cortés, K.; Reeves, R.; Bustos, R.; Radford, S.  Twenty years of precipitable water vapor measurements in the Chajnantor area.  Astron. Astrophys. 2020, 640, A126.  https://doi.org/10.1051/0004-6361/202037784.
  * Buck (1981) Buck, A.L.  New Equations for Computing Vapor Pressure and Enhancement Factor.  J. Appl. Meteorol. 1981, 20, 1527–1532.  https://doi.org/10.1175/1520-0450(1981)020<1527:NEFCVP>2.0.CO;2.
  * Hunziker et al. (2017) Hunziker, S.; Gubler, S.; Calle, J.; Moreno, I.; Andrade, M.; Velarde, F.; Ticona, L.; Carrasco, G.; Castellón, Y.; Oria, C.; et al.  Identifying, attributing, and overcoming common data quality issues of manned station observations.  Int. J. Climatol. 2017, 37, 4131–4145.  https://doi.org/10.1002/joc.5037.
  * J. (1949) Kendall, M. G.  Rank Correlation Methods. [Pp. vii + 160. London: Charles Griffin and Co. Ltd., 42 Drury Lane, 1948. 18s.].  J. Inst. Actuar. 1949, 75, 140–141.  https://doi.org/10.1017/S0020268100013019.
  * Yue and Wang (2004) Yue, S.; Wang, C.  The Mann-Kendall Test Modified by Effective Sample Size to Detect Trend in Serially Correlated Hydrological Series.  Water Resour. Manag. 2004, 3, 201–218.
  * Hussain and Mahmud (2019) Hussain, M.; Mahmud, I.  pyMannKendall: A python package for non parametric Mann Kendall family of trend tests.  J. Open Source Softw. 2019, 4, 1556.  https://doi.org/10.21105/joss.01556.
  * Sen (1968) Sen, P.K.  Estimates of the Regression Coefficient Based on Kendall’s Tau.  J. Am. Stat. Assoc. 1968, 63, 1379–1389.  https://doi.org/10.1080/01621459.1968.10480934.
  * Gómez-Fontealba et al. (2023) Gómez-Fontealba, C.; Flores-Aqueveque, V.; Alfaro, S.C.  Teleconnection between the Surface Wind of Western Patagonia and the SAM, ENSO, and PDO Modes of Variability.  Atmosphere 2023, 14, 608.  https://doi.org/10.3390/atmos14040608.
  * Thompson and Wallace (2000) Thompson, D.W.J.; Wallace, J.M.  Annular Modes in the Extratropical Circulation. Part I: Month-to-Month Variability.  J. Clim. 2000, 13, 1000–1016.  https://doi.org/10.1175/1520-0442(2000)013<1000:AMITEC>2.0.CO;2.
  * Mantua et al. (1997) Mantua, N.J.; Hare, S.R.; Zhang, Y.; Wallace, J.M.; Francis, R.C.  A Pacific Interdecadal Climate Oscillation with Impacts on Salmon Production.  Bull. Am. Meteorol. Soc. 1997, 78, 1069–1079.  https://doi.org/10.1175/1520-0477(1997)078<1069:APICOW>2.0.CO;2.
  * Smith and Sardeshmukh (2000) Smith, C.A.; Sardeshmukh, P.D.  The effect of ENSO on the intraseasonal variance of surface temperatures in winter.  Int. J. Climatol. 2000, 20, 1543–1557.  https://doi.org/10.1002/1097-0088(20001115)20:13<1543::AID-JOC579>3.0.CO;2-A.
  * Garreaud et al. (2009) Garreaud, R.D.; Vuille, M.; Compagnucci, R.; Marengo, J.  Present-day South American climate.  Palaeogeogr. Palaeoclimatol. Palaeoecol. 2009, 281, 180–195.  https://doi.org/10.1016/j.palaeo.2007.10.032.
  * Böhm et al. (2020) Böhm, C.; Reyers, M.; Schween, J.H.; Crewell, S.  Water vapor variability in the Atacama Desert during the 20th century.  Glob. Planet. Chang. 2020, 190, 103192.  https://doi.org/10.1016/j.gloplacha.2020.103192.
  * Pepin et al. (2016) Pepin, N.C.; Maeda, E.E.; Williams, R.  Use of remotely sensed land surface temperature as a proxy for air temperatures at high elevations: Findings from a 5000 m elevational transect across Kilimanjaro.  J. Geophys. Res. Atmos. 2016, 121, 9998-10.  https://doi.org/10.1002/2016JD025497.
  * Haslebacher et al. (2022) Haslebacher, C.; Demory, M.E.; Demory, B.O.; Sarazin, M.; Vidale, P.L.  Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050. Projected increase in temperature and humidity leads to poorer astronomical observing conditions.  Astron. Astrophys. 2022, 665, A149.  https://doi.org/10.1051/0004-6361/202142493.
  * Burger et al. (2018) Burger, F.; Brock, B.; Montecinos, A.  Seasonal and elevational contrasts in temperature trends in Central Chile between 1979 and 2015.  Glob. Planet. Chang. 2018, 162, 136–147.  https://doi.org/10.1016/j.gloplacha.2018.01.005.
  * Rangwala and Miller (2012) Rangwala, I.; Miller, J.R.  Climate change in mountains: A review of elevation-dependent warming and its possible causes.  Clim. Chang. 2012, 114, 527–547.  https://doi.org/10.1007/s10584-012-0419-3.
  * Masiokas et al. (2016) Masiokas, M.H.; Christie, D.A.; Le Quesne, C.; Pitte, P.; Ruiz, L.; Villalba, R.; Luckman, B.H.; Berthier, E.; Nussbaumer, S.U.; González-Reyes, Á.; et al.  Reconstructing the annual mass balance of the Echaurren Norte glacier (Central Andes, 33.5° S) using local and regional hydroclimatic data.  Cryosphere 2016, 10, 927–940.  https://doi.org/10.5194/tc-10-927-2016.
  * Gilmozzi and Spyromilio (2007) Gilmozzi, R.; Spyromilio, J.  The European Extremely Large Telescope (E-ELT).  Messenger 2007, 127, 11.
  * Garreaud (2011) Garreaud, R.D.  The Climate of northern Chile: Mean state, variability and trends.  In Proceedings of the Revista Mexicana de Astronomia y Astrofisica Conference Series, Astronomical site testing data in Chile Valparaiso, Chile, Dec 1 - 3, 2010; Revista Mexicana de Astronomia y Astrofisica Conference Series; Volume 41, pp. 5–11.
  * Krishnamurti et al. (2013) Krishnamurti, T.N.; Martin, A.; Krishnamurti, R.; Simon, A.; Thomas, A.; Kumar, V.  Impacts of enhanced CCN on the organization of convection and recent reduced counts of monsoon depressions.  Clim. Dyn. 2013, 41, 117–134.  https://doi.org/10.1007/s00382-012-1638-z.
  * Segura et al. (2020) Segura, H.; Espinoza, J.C.; Junquas, C.; Lebel, T.; Vuille, M.; Garreaud, R.  Recent changes in the precipitation-driving processes over the southern tropical Andes/western Amazon.  Clim. Dyn. 2020, 54, 2613–2631.  https://doi.org/10.1007/s00382-020-05132-6.
  * Ciddor (1996) Ciddor, P.E.  Refractive index of air: New equations for the visible and near infrared.  Appl. Opt. 1996, 35, 1566.  https://doi.org/10.1364/AO.35.001566.
  * Thayer (1974) Thayer, G.D.  An improved equation for the radio refractive index of air.  Radio Sci. 1974, 9, 803–807.  https://doi.org/10.1029/RS009i010p00803.
  * Cantalloube et al. (2020) Cantalloube, F.; Milli, J.; Böhm, C.; Crewell, S.; Navarrete, J.; Rehfeld, K.; Sarazin, M.; Sommani, A.  The impact of climate change on astronomical observations.  Nat. Astron. 2020, 4, 826–829.  https://doi.org/10.1038/s41550-020-1203-3.
  * Falvey and Garreaud (2009) Falvey, M.; Garreaud, R.D.  Regional cooling in a warming world: Recent temperature trends in the southeast Pacific and along the west coast of subtropical South America (1979–2006).  J. Geophys. Res. Atmos. 2009, 114, D04102.  https://doi.org/10.1029/2008JD010519.
  * Mutz et al. (2021) Mutz, S. G., Scherrer, S., Muceniece, I., et al.  Climate Dynamics, 2021, 56, 2881.  https://doi:10.1007/s00382-020-05620-9.