Uses and limits of thermal indices: the case of Sahel
Résumé
Our main goal here is to analyse extreme heat waves (HWs) in the Sahel (13°N-
18°N; 16°W-30°E), using different thermal indices. In the ACASIS project funded by
the French “Agence Nationale de la Recherche”, HWs analyses are characterized for
the first time during the hottest season in the Sahel, using the Global Summary of the
Day (GSOD) synoptic observations during April-May-June 1973-2013. Such extreme
high temperatures are usually defined by 3 criteria: 1/ Their low probability of
occurrence: less than the 10% of the days. The use of absolute thresholds,
associated with heat budget and physiological impacts, could be an alternative
(Seneviratne et al., 2012). 2/ Their intensity, i.e. their amplitude in terms of standard
deviations from the local climatology (Goubanova, 2007), or otherwise excesses over
absolute intensity thresholds. 3/ Their severity: extreme events causing socioeconomic
or human losses, the notion of impact is thus of major importance for their
definition (Beniston et al., 2007).
According to prior IPCC assessments (TAR, AR4 and SREX), it is very likely that
increased maximum temperatures and enhanced probabilities of hot days occurrences
will occur at the global scale. There is also medium confidence that warm spells/heat
weave frequencies, lengths or intensities will increase in many regions (Cubasch et al.,
2013). Because of its latitude, Sahel is especially concerned, with diurnal
temperatures often exceeding 40°C in boreal spring and, to a lesser extent, autumn.
AR4 concluded that global land-surface air temperature (LSAT) had increased over the
instrumental period of record, with the warming rate approximately double that
reported over the oceans since 1979. AR5 confirms previous estimates: Global LSAT
increased: 0.1005°C ± 0.01925°C per decade over 1901-2012 period while the rise
reaches 0.262°C ± 0.05°C per decade over 1979-2012 time period, according to the
observations of CRUTEM4.1.1.0, GHCNv3.2.0, GISS and Berkeley dataset average
(Hartman et al., 2013). Changes in the length of the spells are observed and they are
likely to increase but this needs to be investigated further over Sahel where severe
heat can have damaging consequences for societies.
For instance, in Niamey (Niger) in April 2010 whereas Tmax reaching 47°C (Ringard et
al., 2015), increased morbidity and mortality rates were observed, especially among
the elderly and young children. Moreover, in some places, HWs can be associated with
increased rates of atmospheric pollution.
Terminology is large: a “ hot spell” implies the hottest temperature during an extreme
of temperature with a small duration (a few hours), small extension (around the
station) and a high frequency; a “warm spell” should be less frequent and covers a
larger area; a “heat wave” (HW) exceeds thresholds of temperature most frequently
and covers at least a whole region (~ 500-1000 km²). These thresholds can be
absolute (T° fixed locally) or relative, exceeding 5 °C as local standards for example
for Expert Team on Climate Change Detection and Indices (ETCCDI, 2013). ACMAD
(African Centre of Meteorological Applications for Development) classifies the extreme
temperatures following five high-impact weather events; they broadcasts a mail alert
when Tmax> 40°C. In ACASIS, Sahelian HW is defined (Rome et al. 2015) as a
period of at least 3 consecutive days of extremely high (above the local 90th
percentile) daily heat index. The Heat Index (HI) (Steadman 1979, 1994),
combining temperature and relative humidity, appears as the most appropriate index,
suitable for tropical climate, which takes into account the human-perceived equivalent
temperature. The role of humidity is rather small in boreal spring across the Sahel,
when high incoming solar radiation is combined with extremely dry soils, preventing
cooling effect associated with latent heat flux. As for global scales, results show a
clear warming trend over the last three decades, with a clear trend for HW events to
become more frequent, last longer, cover larger areas and reach higher intensities
(see also Oueslati et al. 2015). We then observe a decrease in cases of “Caution” HI
and an increase in cases of “Danger” and “Extreme Danger” HI values.
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