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Uses and limits of thermal indices: the case of Sahel
Sandra Rome, Vincent Moron, Boutheina Oueslati, Benjamin Pohl, Bernard Fontaine, Arona Diedhiou
To cite this version:
Sandra Rome, Vincent Moron, Boutheina Oueslati, Benjamin Pohl, Bernard Fontaine, et al.. Uses and limits of thermal indices: the case of Sahel. CFCC15 Scientific Committee, chaired by Chris Field.
Our Common Future Under Climate Change (CFCC15) International Scientific Conference, Jul 2015, Paris, France. UNESCO, Future Earth, and ICSU. �halshs-01175704�
Uses and limits of thermal indices: the case of Sahel
(1) Grenoble Alpes University, LTHE UMR 5564 UJF-CNRS-IRD, Grenoble, France;
(2) Aix-Marseille Université, CEREGE UM 34 C NRS, France, Geography, Aix-en-Provence, France;
(3) CNRS, Centre de Recherches de Climatologie-BIOGEOSCIENCES, Université de Bourgogne, Dijon, France;
(4) Institute of Research for Development (IRD), LTHE - Grenoble Alpes University, Grenoble, France.
Objectives & key points Data and methods Results
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Minimal temperature (Tmin), maximal temperature (Tmax) and mean temperature (Tmean) from Global Summary of the Day (GSOD) synoptic observations during Januany- December 1973-2014.
Network of 145 stations (Fig. 1).
Daily temperature observational data
Tropical regions: warm environment where temperature extremes generate health im- pacts. Mean basic state is already warm and rather close to some physiological thresholds (example : 200-250 days with Tx > 35°C).
The study is threefold:
1. Select the heat index (HI) the most re- levant to the tropical dry area of West Africa.
2. Test the modified HI of Steadman (1979) using 2 declinations of it's defi- nition.
3. Characterize and analyse heat waves (HWs) in West Africa, particularly in the Sahel (13°N- 18°N; 16°W-30°E).
Calculation of the Sahel Heat Wave index (HW iSahel )
Hierarchical clustering (CHA) of mean annual temperature
HW detection and definition for the Sahel
Where:
HI= heat index (in degrees Fahrenheit); T= ambient (in de- grees Fahrenheit); R= relative humidity (percentage value between 0 and 100);
c1 = -42.379; c2 = 2.04901523;
c3 = 10.14333127; c4= -0.22475541;
c5 = -6.83783 *10-3; c6 = -5.481717 *10-2;
c7 = 1.22874 *10-3; c8 = 8.5282 *10-4;
c9 = -1.99 *10-6.
0 10 20 30 40 50 60
0 50 100 150 200 250 300
Temp (Celsius)
Heat Index (Celsius)
Heat index, temperature and relative humidity (in %)
0 10 20 30 40 50 60 70 80 90 100
25 30 35 40 45 50 55
20 25 30 35 40 45 50 55 60
Comfort Warning
Extreme warning Danger
Extreme Danger
A classification of stations according to their annual thermal regime (Fig. 3)
Spatial and temporal extent of HW in West Africa
Mean extent (%)
Mean intensity (degC)
Mean duration (days)
0 2 4 8 10 15 20 25 3
5 7 9 11 13 15 20
Mean extent (%)
Mean intensity (degC)
0 2 4 8 10 15 20 25
48 50 52 54 56 58 60
Mean extent (%)
Mean duration (days)
Percentage of hot days (%)
0 2 4 8 10 15 20 25 3
5 7 9 11 13 15 20
Mean extent (%) Percentage of hot days (%)
0 2 4 8 10 15 20 25
0 0.5 1 1.5 2
HWs are more severe during the dry season (Fig. 5).
Figure 5. Mean intensity of HWs as a function of mean extent and duration (left) ; Percentage of hot days as a function of mean extent and duration (right).
Setting:
HW iSahel (modified HI of Steadman, 1979), using 2 T90p thresholds (Fig. 4).
2) Over all days of the year. This calcula- tion can identify the strongest heat stress potentially impacting human health,
one of the ACASIS program goal.
1) On 5-day moving window average. This calculation re- moves seasonality and detects HW whatever the time of year.
Contact author:
sandra.rome@ujf-grenoble.fr
Acknowledgment:
ACASIS program;
NOAA GSOD data
The calculation of HW is derived from Steadman’s Heat Index (HI) formula (1979, Equation 1, Fig. 2) amended (see § HW detection).
HI= c1 + c2 T + c3 R + c4 T R + c5 T2 + c6 R2 + c7 T2R + c8 TR2 + c9 T2R2 [Eq. 1]
Figure 2. Approximation of the Heat Index converted to Celsius (modified from Steadman, 1979). Colors indi- cate the level of HI, from comfort to extreme danger.
Figure 3. Spatial and temporal variation of temperature (in Celsius) in Tropi- cal Africa north of Equator (Tn in blue, Tx in red and dew point in black) ob- tained by hierarchical clustering (CHA) of mean annual cycle computed on 1973-2014 period.The curves are the spatial average (except for the first clus- ter which includes a single station – Nouakchott-) of the stations assigned to a given cluster.
Figure 4. Spatial extension (percentage of stations belonging to the cluster) of HW in West Africa, according to the two declinations of percentile calcu- lation (top: 5-day moving window average; bottom: all days - see § definition).
Green, blue and black squares correspond to the clusters shown in figure 3.
T90p: 5-day moving window average
T90p: all days of the year
The Sahel HWs damaging to health occur mostly between April and July (Fig. 4 bottom).
They are more widespread, more and more frequent since 1998.
In dry tropical zone like the Sahel, a heat wave is detected when the heat index of the day (HI max ) and of the night
(HI min ) exceeds the 90th percentile (T90) for at least 3.5 consecutive days (7 overruns).
The T90 is calculated with 2 declinations:
Figure 1. Meteorological data network available in West Africa: location of the 145 stations available in GSOD having at least 3650 daily entries in the 1973-2013 period.
Elevation (m)
Short (3-days) and weekly extended (2-8 %) HW s occur frequently but have weak intensities (less than 54 ° C ).
HW s w ith higher intensity (larger than 54 ° C ) last longer (m ore than 9 days) and/or cover large areas (larger than
10 %), depending on the dom ain.
S. Rome (1), V. Moron (2), B. Oueslati (3), B. Pohl (3), B. Fontaine (3) & A. Diedhiou (4)
©Rome et al., 2015 CFCC
©Rome et al., 2015 CFCC
©Rome et al., 2015 CFCC
©Rome et al., 2015 CFCC
©Rome et al., 2015 CFCC
