Introduction
In the coming decades, climate change will expose hundreds of millions of
people to its impacts (Pachauri et al., 2014; Solomon et al., 2007; WHO, 2015).
Many areas of the world – Africa and Latin America, among others
(Niang et al., 2014) – will have to deal with increases in temperature and changes in extreme
weather conditions such as heat waves, altering the probability of
experiencing major heat waves in the very near future (Field et al., 2012).
This, in turn, may lead to serious implications, mainly health and health-service related
(Barnett et al., 2012; Conti et al., 2005; Ostro et al., 2009). The 2003 European
heat wave (Beniston, 2004) illustrated how infrastructures, even in highly
developed countries, can fail to deal with such environmental challenges.
From this perspective, variability and changes in extreme temperature
regimes present a considerable challenge for South America (Magrin et al., 2014).
Different aspects of the occurrence of temperature extremes – both spatially
and temporally – are still lacking for the continent (Rusticucci, 2012). A complete picture,
along with a robust assessment, of temperature extreme regimes might provide
essential information on the climate-related risks that society now face,
and how these risks are changing.
Amongst the areas of South America most vulnerable to heat and cold waves
are the so-called “megacities”, i.e. metropolitan areas with total
populations in excess of 10 million people such as Bogota, Sao Paulo, Rio de
Janeiro, and Buenos Aires. Climate change issues are thus coupled with
anthropic pressure issues.
In order to study extreme temperature regimes, daily records are needed.
This requirement is particularly hard to meet in South America, which has a
sparse sensing network. To overcome this problem, the Global Surface Summary
of the Day (GSOD) meteorological data set has been employed. GSOD is a
compilation of daily meteorological data produced by the National Climatic
Data Center (NCDC), available from 1929 to present, which displays a reasonably
dense coverage across South America. GSOD has been recently employed to show
an increase in the number of heat waves in urban areas at the global scale
(Mishra et al., 2015).
The aim of this paper is twofold. Firstly, we calculate annual magnitudes of
heat waves and cold waves during 1980–2014 using maximum and minimum daily
temperature from GSOD meteorological records. Secondly, we estimate trends
of maximum, minimum temperature, and their relative range across South
America. These analyses put in evidence of different aspects of temperature
extremes, still largely unknown across South America.
The analysis presented below follows a three-step procedure that is divided
as follows: (1) selection of temperature records with at least 30 years of
data (see Sect. 2.2.1); (2) calculation of the Heat Wave Magnitude Index (HWMI)
and Cold Wave Magnitude Index (CWMI) for the period 1980–2014;
(3) estimation of the trend for annual mean of daily maximum temperature, annual
mean of daily minimum temperature, and the mean temperature range
(i.e. MTR = annual mean of daily maximum - annual mean of daily minimum
temperature; see Sect. 2.2 for further details).
Results
Heat wave
Figure 2 shows the maximum value in 5-year periods of the HWMItx from 1980
to 2014. Four magnitude classes are shown. Since the magnitude class 16–32
occurs only three times, it has been merged with the class 8–16 for
the sake of clarity. Considering that maximum and minimum temperature are
highly correlated, results with HWMItn do not differ significantly from
those detected with HWMItx (see Supplement).
There is evidence that an increase of heat wave intensity is ongoing.
Specifically, from 1995 onwards it is possible to observe heat waves spread
across South America, with the maximum presence during 2010–2014. Between
1980 and 1984 there have been heat waves with magnitude equal to 16
across the central part of the continent, mainly corresponding to Peru,
whereas other regions do not show such great heat waves (for further details
see the Supplement). In particular, in the austral summer of 1982–1983,
during one of the strongest El Niño events (Cane, 1983) of
the 20th century, the highest HWMI values in Peru were comparable with
the HWMI peak of the 2003 European heat wave that killed more than 70 000 people
(Robine et al., 2008; Russo et al., 2014).
The central part of the continent also displays severe heat waves during
1995–1999 in correspondence with the 1997–98 El Niño event
(McPhaden, 1999), whereas the rest of the region does not
present patterns related to El Niño. Generally, HWMI's frequency is low
until 1994 and then increases rapidly.
Histogram of heat waves for 5-year periods during 1980–2014 for
maximum temperature (Tx, upper panel) and minimum temperature (Tn, bottom panel).
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Histograms in Fig. 3 show the temporal distribution of heat wave for each
class of magnitude for both maximum temperature (Tx, upper panel) and
minimum temperature (Tn, bottom panel). The 5-year time window allows us
to better visualize the evolution of heat waves and to filter out the
influence of El Niño on the occurrence of extreme events. Results
complement and confirm previous findings shown in Fig. 2. The occurrence of heat
waves generally increased in the last 10 years. This is noticeable from the
analysis of the histograms, where the occurrence of heat waves rises from
20 (20) to 150 (50) heat wave events per 5-year period for maximum (minimum) temperature
from 1980–1984 to 2010–2014. Also, our results show that 35 % of the
available meteorological stations present intense heat waves with HWMItx ≥ 2.
Maximum temperature shows the highest number of heat waves, while minimum
temperature shows the lowest. For maximum temperature, the increase in heat
wave events occurred for all the magnitude classes. For minimum temperature
it is possible to observe the upward trend only for heat waves with
magnitude greater than 2, whereas the other classes are relatively
stationary. Between 2005 and 2014 the frequency of extreme heat waves has
increased to 40 observations per year, as compared to ∼ 8.5 per year
in the period from 1980 to 2004.
Maximum temperature has the highest number of heat waves, with
1805 occurrences, while minimum temperature has the lowest, with 1025 occurrences. Note
that this tally includes heat waves with HWMI greater than 1
(i.e. “normal” heat waves) that are not shown in Figs. 2 and 3.
Heat Wave Magnitude Index of maximum temperature (HWMItx) for 2013.
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Annual maps of HWMItx and CWMItn are provided in the Supplement. By
way of example, Fig. 4 shows the HWMItx for 2013 when Argentina,
Uruguay, and Brazil experienced intense heat waves. Specifically, Argentina
and Uruguay experienced extremely warm temperatures at the end of 2013
(Blunden and Arndt, 2014), whereas the cities of Sao
Paulo and Rio de Janeiro (south-eastern Brazil) experienced their warmest
January and February at the beginning of 2014 (Blunden
and Arndt, 2015). Note that 2013 refers to the period July 2013–June 2014,
as explained in the method section. GSOD-derived observations are able
to capture and quantify extreme weather events that occurred between 2013
and 2014. The spatial distribution of the HWMI also shows a hot spot of
increase in HWMI frequency and magnitude from 2009 onwards across Paraguay
and south-eastern Brazil, as shown in Fig. S3 of the Supplement. However, spatial
patterns of heat waves are heavily influenced by the distribution of GSOD
stations throughout the continent.
Cold wave
As for heat waves, Fig. 5 shows the CWMItn for 5-year periods from 1980
to 2014 (5-year maps for CWMItx are shown in the Supplement). By means of
the CWMI we were able to detect the strongest cold waves occurred in Latin
America since 1980. It is possible to see that the vast majority of cold
waves have a moderate magnitude with CWMI not below -3. Contrary to HWMI, it
is not possible to distinguish an increase of cold wave intensity in the last decade.
Very extreme cold waves (i.e. CWMI ≤ -8) are detected in
correspondence to 1980–1984 and 1995–1999 periods across Ecuador and
Venezuela. However, there is no temporal coherence with El Niño events
(i.e. 1982–1983 and 1997–1998), since the major cold wave events took place
during 1980 and 1996 respectively (annual maps of CWMItn are shown
in the Supplement).
Just as for heat waves, histograms in Fig. 6 show the temporal distribution
of cold waves for each class of magnitude.
The occurrence of cold waves is essentially stationary across the weather
stations. Only CWMItn ≤ -2 and ≤ -3 exhibit a positive
trend in the 1980–1999 period, with a peak during 1995–1999 and a negative
trend afterwards. Specifically, GSOD stations with CWMItn ≤ -2 are
nearly double the number of the other periods.
Cold Wave Magnitude Index of minimum temperature (CWMItn)
for 5-year periods from 1980 to 2014. The magnitude scale follows the
classification of cold waves provided in Table 1.
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Histogram of cold waves for 5-year periods during 1980–2014 for
maximum temperature (Tx, upper panel) and minimum temperature (Tn, bottom panel).
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Trend in the mean annual maximum temperature (Tt, left panel),
mean annual minimum temperature (Tn, central panel), and mean temperature
range (MTR, right panel). Units are expressed in Celsius degrees per decade
[∘C decade-1]. Tx and Tt have the same colour bar.
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Differently from heat waves, the CWMI applied to minimum and
maximum temperatures has the same order of occurrence, with 1150 and
1187 events respectively. Cold waves with CWMI ≤ -2 are observed for 32 %
of the available GSOD stations (i.e. about 83 stations).
Trend analysis
Figure 7 shows the slope of statistically significant trends, if any, of
(1) annual mean of daily maximum temperature, (2) annual mean of daily minimum
temperature, and (3) MTR. Note that only stations
with a statistically significant trend (i.e. p value smaller than 0.05, or
5 %) are shown, and the number of points changes accordingly.
Interestingly, MTR displays trends in stations where there are no trends for
either minimum or maximum temperature.
Annual mean of daily maximum temperature is generally increasing – up to
1 ∘C per decade – across the continent; only a few stations display
a negative trend. Annual mean of daily minimum temperature is increasing (up
to 1 ∘C per decade) in the tropics (i.e. above -23∘ N)
and decreasing (up to 0.5 ∘C per decade) in the extratropics.
MTR displays a widening behaviour in the extratropics, where the temperature
range displays a broadening trend of up to 1 ∘C per decade.
Conversely, spatial patterns of MTR trends in the tropics indicate that
negative trends generally prevail, with a few stations erratically showing
positive trends. However, both spatial density and magnitude of negative
trend across the tropics are less prominent than the positive ones in the
south. The maximum reduction found in MTR is equal to 0.5 ∘C per decade.
The number of GSOD stations that display statistical significant trends for
maximum temperature, minimum temperature, or MTR (i.e. all the stations
shown in Fig. 7) is equal to 75.
Extreme temperatures over a megacity: Bogota
In order to confirm our previous findings, the daily meteorological data
pertaining to the Bogota have been analysed. Located in the tropics, Bogota
and its metropolitan area is one of the four megacities of South America
and represents one of the areas more at risk of extreme events
(Instituto de Hidrología, Meteorología y Estudios
Ambientales et al., 2014). The meteorological station is located in the El
Dorado International Airport, 15 km north-west of central Bogota.
Figures 8 and 9 show the 10-day moving average of daily maximum and minimum
temperatures respectively. The average values (i.e. mean ±2 SD – standard
deviation) of the 1980–2014 time series are in dark brown and the
highest and lowest values in light brown.
The black lines in Figs. 8 and 9 represent the 10-day moving average of
daily average for 1980–2014 of maximum and minimum temperature respectively.
Similarly, the orange lines represent the 10-day moving average of daily
average in the most recent 5-year period (i.e. 2010–2014).
In the last 5 years our data show a shift towards warmer temperatures: in
2010–2014 temperature values are nearly always above the 1980–2014 average.
This is particularly noticeable for minimum temperatures, upholding what we
previously found across the tropics: Tn is increasing faster than Tx.
The two-sample Kolmogorov–Smirnov test has been employed to test whether
daily average for 2010–2014 and daily average for 1980–2014 come from the
same distribution, for both maximum and minimum temperature.
The p value has been calculated, getting the result of 1.14 × 10-9 and
1.82 × 10-5 for maximum and minimum temperature respectively. With a so low p value, it
is reasonable to assume that they do not come from the same distribution.
The null hypothesis is rejected and we can infer that different statistical
distributions are indeed present, thereby confirming our graphical inference.
Discussion
Our work is as an attempt towards temperature regime characterization of
South America and presents a number of caveats thereof.
Firstly, the spatial distribution of GSOD station is a critical aspect. GSOD
stations are unevenly distributed across the region. In addition, as
heat/cold waves do not occur homogeneously throughout all of South America,
the location of the observing stations surely influences the results.
However, spatial distribution of temperature, especially at daily level, is
a major issue for this region with such a sparse network. Definitively, the GSOD
data set represents the state of the art of daily temperature records across
South America where large gaps exist (e.g. records were interrupted or
stations were removed). This is of course an issue that cannot be rectified,
and only reanalysis products might help in producing consistent pictures of
the region.
Secondly, it is likely that land use changes such as urbanization or large
agricultural expansion induce abrupt changes or jumps in the time series of
temperature (Zhou et al., 2014), and these aspects has not been taken into
account in our study. However, trends and abrupt changes cannot be easily
distinguished in statistical tests (Yevjevich, 1987) since they are very closely intertwined.
Thirdly, trend analysis presented in this study cannot predict future
climate patterns completely accurately. Caution should be exercised:
evidence from longer records, instrumental or proxy, suggests that local
trends are omnipresent but not monotonic; rather, at some time upward
trends turn to downward ones and vice versa (Montanari and Koutsoyiannis, 2014).
Fourthly, the location of the meteorological stations can certainly affect
the temperatures recorded. GSOD records, which often pertain to urban areas,
are subject to the urban island effect that influences the recorded surface
air temperatures, as shown across the USA by Fall et al. (2011) and at global
scale by Kalnay and Cai (2003).
That being said, our analysis provides a unique research opportunity to
explore observed extreme temperature regimes across South America, reducing
spatial and temporal gaps on heat and cold waves and their trends
(Perkins, 2015). This analysis takes advantage of the
state-of-the-art technique developed in heat wave (Hoag,
2014) and cold wave (Forzieri et al., 2015) assessment. This
analysis gives a perspective on cold waves, which have been rarely studied.
Bogota 10-day moving average of maximum daily temperature (2010–2014
versus 1980–2014 values).
![]()
There is evidence of an ongoing increase in intensity and frequency of heat
wave events. Specifically, from 1995 onwards it is possible to observe heat
wave spread with the maximum presence during 2010–2014. In the last 10
years (i.e. 2005–2014), the number of GSOD stations that experienced heat waves
is greater than in the previous 25-year period (i.e. 1980–2004).
Interestingly, the spatial patterns of heat waves across Peru display a
correspondence with El Niño events (Carmona-Moreno et al., 2005), whereas
the rest of the continent does not show such great similarities.
Maximum temperature displays the highest number of heat waves and minimum
temperature the lowest. Interestingly, the HWMI applied to minimum
temperature displays lower magnitude than the HWMI values calculated with
daily maximum; there are no evident trends in the temporal distribution
of heat waves with magnitude index greater than 3. This might be due to
the rapid night-time cooling, especially along the ocean coastline.
The occurrence of cold wave is essentially stationary in the sense of both
intensity and frequency, with the exception of minimum temperature. In this
regard, it is possible to observe an upward trend, with the peak between 1995
and 1999, turning downwards afterwards. Specifically, the number of
GSOD stations with CWMI less or equal to -2 during 1995–2000 is twice the
number of events during the other periods. Cold waves do not display
temporal coherence with El Niño events. Apparently, there are no
similarities between La Niña events, volcanic eruptions, such as the
Pinatubo eruption of 1991, and cold waves across South America.
Bogota 10-day moving average of minimum daily temperature (2010–2014
versus 1980–2014 values).
![]()
Annual mean of daily maximum temperature is generally increasing, up to
1 ∘C per decade, whereas annual mean of daily minimum temperature
is increasing (up to 1 ∘C per decade) in the tropics and
decreasing (up to 0.5 ∘C per decade) in the extratropics. Also,
MTR displays an opposed latitude-dependent behaviour. MTR shows a broadening
trend up to 1 ∘C per decade in the extratropics, while negative
trends generally prevail in the tropics, with a reduction up to
0.5 ∘C per decade. Generally, minimum temperature shows interannual
variability higher than maximum temperature, as shown in Figs. 8 and 9.
This effect might hinder the trend detection of minimum temperature,
especially when the relative trend is included in the range of variability.
Results support the findings of Mishra et al. (2015): the number of heat waves
has increased. The occurrence of heat waves with a magnitude index greater than 2 has
been most prominent in the most recent period. Results also support what was
reported in Kenyon and Hegerl (2008): temperature extremes are substantially
affected by El Niño.
Generally, there is agreement with Rusticucci (2012) on the positive trends of warm
night (i.e. heat waves of minimum temperature), even if the trend of heat
waves related to maximum temperature is more pronounced.
Finally, coherence with IPCC 2007 findings (Solomon et al., 2007) has been
examined. The IPCC theoretical prediction for increasing in maximum temperature
are coherent with our results. Conversely, in our study the dramatic rise of
daily minimum temperatures, projected to increase faster than daily maximum
temperatures, has been found only over the tropics.
Conclusions
This study assesses the specific behaviour of extreme temperature regimes
from the GSOD database recorded across South America
in the period 1980–2014. Analysis of the extreme values and trends of
interannual ranges of temperatures are crucial since it gives insight into
“outside-of-the-box” scenarios (Lagadec, 2004) which very few studies consider.
Applications of these analyses are manifold in essential sectors such as local
health and social care systems (Carmichael et al., 2012; Gupta and Gregg, 2012).
Heat and cold waves are calculated using the Heat Wave Magnitude Index
(Russo et al., 2014) and the Cold Wave Magnitude Index obtained
by adapting the HWMI as an equivalent indicator, for maximum and minimum daily
temperatures. Finally, trend detection has been performed to assess the
significance of temporal changes in the annual mean of daily maximum temperature,
annual mean of daily minimum temperature, and the MTR.
Results from heat wave analysis indicates the presence of an increase in
intensity and in frequency of extreme events in the last 10 years. Results
from cold wave analysis show an erratic behaviour and no conclusion can be
drawn. Heat wave shows temporal coherence with El Niño events
(i.e. 1982–1983 and 1997–1998), whereas no connection between cold waves and El
Niño can be inferred from our analysis.
MTR trends indicate that the maximum temperature is generally increasing
faster than the minimum temperature over the extratropics. Across the
tropics, the interannual ranges of mean temperature are generally narrowing,
even if the corresponding spatial patterns are not outstandingly noticeable.
The outcomes described in this paper open the possibility to extend the
presented scheme over very long periods worldwide. Further applications
include the study of the impact of land use changes on heat waves and
differential increases in temperature. Other applications of the paper
include the employment of GSOD records as an independent check that
temperature reconstructions produced using reanalysis data such as
ERA-INTERIM (Dee et al., 2011) are in line with raw data from observational stations.