The purpose of this article is to show the extreme
temperature regime of heat waves across Africa over recent years (1981–2015).
Heat waves have been quantified using the Heat Wave Magnitude
Index daily (HWMId), which merges the duration and the intensity of extreme
temperature events into a single numerical index. The HWMId enables a
comparison between heat waves with different timing and location, and it has
been applied to maximum and minimum temperature records. The time series
used in this study have been derived from (1) observations from the Global
Summary of the Day (GSOD) and (2) reanalysis data from ERA-Interim. The
analysis shows an increasing number of heat waves of both maxima and minima
temperatures in the last decades. Results from heat wave analysis of maximum
temperature (HWMId
Africa is considered one of the most vulnerable regions to weather and climate variability (Solomon, 2007); extreme events such as heat waves have an impact to public health, water supplies, food security. According to Albrecht (2014), climate change will increase its pressure in northern Africa. All African capital cities are anticipated to face more exceptionally hot days in the future with respect to the rest of the world.
In fact, recent findings of the World Meteorological Organization (WMO) indicate that the years 2011–2015 have constituted the warmest 5-year period on record (WMO, 2015) and heat waves of maximum temperature have increased both in severity and number accordingly.
Despite its vulnerability, the distribution of African heat waves is still poorly understood due to the lack of accurate baseline data on current climate (UNECA, 2011). Specifically there are still uncertainties in the state of the art of our actual understanding of temperature extreme regime; only a few studies have considered the whole of Africa (Collins, 2011). Such information is paramount, since it is necessary to assess the impacts of climate change on human and natural systems and to develop suitable adaptation and mitigation strategies at country level.
Daily records are needed in order to analyse extreme temperature regimes. To this end, the Global Surface Summary of the Day (GSOD) meteorological dataset has been employed. GSOD is a compilation of daily meteorological data produced by the National Climatic Data Center, available from 1929 to present, which displays a reasonably dense spatial coverage across Africa. However, a general caveat with the GSOD dataset is the limit imposed by its sparse gauge network. There are many regions, especially across Central Africa where the absence of temperature records precludes a comprehensive and robust analysis. To circumvent this limitation, daily reanalysis data have also been used. Reanalysis is a combination of observations and climatological models through data assimilation systems to produce a single, uniform global dataset (Kalnay et al., 1996), thus enabling a homogeneous coverage of Africa.
The magnitude of heat waves for both observations and reanalysis is quantified on annual basis by means of the Heat Wave Magnitude Index daily (HWMId; Russo et al., 2015) for the period 1981–2015 across Africa. The HWMId has been applied to maximum and minimum temperature.
The objective of this paper is to analyse African heat wave regime and identify the most important of heat waves during 1981–2015. These analyses draw attention to the spatial distribution of temperature extremes and their temporal evolution in the past decades, still largely unknown. Considering both its wide geographic scope and spatial resolution, the study represents an important step towards the assessment of heat wave frequency in the last 3 decades using records of daily maximum and minimum temperatures acquired at regional level.
The availability of such information is paramount. The more reliable the assessment of heat waves is, the better African countries will be equipped to strengthen their coping capacities. This study also provides insight in human exposure to heat waves in Africa.
Some early exploratory research using similar methodology for South America showed some promising preliminary results (Ceccherini et al., 2016). In this paper, the heat wave classification scheme has been consolidated and improved (Russo et al., 2015) and both observations and reanalysis datasets have been employed.
The time series of temperature used in this study have been derived from
(1) observations and (2) reanalysis. GSOD is
the dataset of observations. GSOD records, produced by the National Climatic
Data Center, are mainly recorded at international airports and include
maximum and minimum values of temperature. GSOD records underwent extensive
automated quality controls to eliminate many of the random errors found in
the original data (further details on the GSOD data can be obtained from the
website
ERA-Interim (Berrisford et al., 2011; Dee et al., 2011) is the dataset of reanalysis providing hydrometeorological variables such as maximum and minimum temperature, evaporation, snowfall, runoff and precipitation across land at various temporal scales. Reanalysis has been increasingly used to address a variety of climate-change issues and has by now become an important method in climate-change research (Fan and van den Dool, 2004; Marshall and Harangozo, 2000; Uppala et al., 2005). ERA-Interim is a reanalysis product of the European Centre for Medium-Range Weather Forecasts (ECMWF) available from 1979 and continuously updated in real time. The data assimilation system used to produce ERA-Interim is based on a 4-D variational scheme (4D-Var) with a 12 h analysis window (for further information on 4D-VAR see Courtier et al., 1994).
The reanalysis dataset used in this study has a spatial resolution of
0.75
For both observations and reanalysis dataset the timespan considered in this study refers to the period 1 January 1981–30 June 2015.
In this paper the HWMId, recently defined by Russo et al. (2015), has been employed to detect African heat waves for the period 1981–2015. The HWMId is a simple numerical indicator that takes both the duration and the intensity of the heat wave into account. Basically, the magnitude index sums excess temperatures beyond a certain normalized threshold and merges durations and temperature anomalies of intense heat wave events into a single indicator, according to the methodology described in Russo et al. (2014, 2015). The HWMId is an improvement on the previous Heat Wave Magnitude Index (i.e. HWMI by Russo et al., 2014) and able to overcome its limitations. More precisely, HWMI has some problems in assigning magnitude to very high temperatures in a changing climate, thus resulting in an underestimation of extreme events.
Heat Wave Magnitude Index daily of maximum temperature (HWMId
The HWMId is defined as the maximum magnitude of the heat waves in a year.
Specifically, a heat wave is defined as a period
The interquartile range (IQR, i.e. the difference between the 25th and
75th percentiles of the daily maxima temperature) is used as the
heat wave magnitude unit, since it represents a non-parametric measure of the
variability. If a day of a heat wave has a temperature value equal to IQR,
its corresponding magnitude value will be equal to 1. According to this
definition, if the magnitude on the day
The HWMId computations requires at least a 30-year time series of daily temperature records. GSOD stations with less than 30-year records and with more than 30 % of gaps have been excluded from our analysis (for further details see Ceccherini et al., 2016). As a result, 260 GSOD stations out of 958 satisfy these conditions.
Heat waves are computed using (1) maximum (hereafter HWMId for each GSOD station and for the entire spatial domain of the daily ERA-Interim maxima and minima
temperatures dataset across Africa. Specifically, the index has been computed
separately for each cell at
Heat waves generally occur between December and January in the Southern
Hemisphere and between June and July in the Northern. In order to avoid
splitting event occurrences that happen within a regular calendar year,
starting and ending dates have been redefined accordingly. Therefore, the
HWMId computation starts on 1 January 1981 and ends on 31 December 2014 in
the Northern Hemisphere. Similarly, the HWMId computation starts on 1 July 1981
and ends on 30 June 2015 in the Southern Hemisphere (for further
information see HWMId function in Gilleland and Katz, 2011). Note that
this scheme leaves the tropics out of consideration.
Also, the southern hemispheric 6-month time shift causes temporal
inconsistency in the dataset: the time span is 34 years in the Southern
Hemisphere and 35 in the Northern. However, starting our analysis from 1980
would have further reduced the number of available GSOD stations from 260
to
As Fig. 1 but applied to minimum temperature (HWMId
Figures 1 and 2 display the maximum value in 5-year periods of the HWMId of GSOD observations from 1981 to 2015 for maximum and minimum temperature, respectively. The bottom-right panel of Fig. 1 shows the spatial distribution of the 260 GSOD temperature stations employed in this study.
There is a clear indication that both intensity and spatial distribution of
heat waves of maximum temperature are increasing. Specifically, from 1996
onwards it is possible to observe a positive increase in heat waves'
magnitude and spread across Africa, with the maximum presence during 2011–2015.
HWMId
Despite the generally high correlation between maximum and minimum
temperature, HWMId results with minimum temperature differ significantly
from those with maximum temperature. Specifically, the number of stations
affected by heat wave events of minimum temperature is low , i.e.
The interannual evolution of heat waves is fully detailed in Fig. 3, which
shows the occurrence of HWMId greater than a given magnitude level
(i.e. HWMId
Annual distribution of events exceeding four different thresholds
(i.e. HWMId
Heat Wave Magnitude Index daily of maximum temperature (HWMId
As Fig. 4 but applied to minimum temperature (HWMId
As for GSOD, Fig. 4 shows the HWMId
Spatial patterns indicate an increase of HWMId
Unlike our finds for GSOD, the temporal evolution of HWMId
Also, HWMId
Just as for GSOD, Fig. 6 shows the occurrence of HWMId greater than a given magnitude level for maximum and minimum temperature, respectively. Maximum temperature displays a positive and statistically significant trend for the first three classes. The slopes of the linear regression are comparable with those of GSOD observations.
Unlike GSOD, the percentage of area affected by heat waves increase also for minimum temperature, which exhibits slopes comparable with those of maximum temperature even if they are slightly lower.
Annual distribution of events exceeding four different thresholds
(i.e. HWMId
A visual comparison of heat wave detection from observations (GSOD) and
reanalysis (ERA-Interim) is given in the Fig. S3. The maps show the
HWMId
Although the qualitative character of the comparison due to the low GSOD station number, ERA-Interim shows good agreement with observations.
Although quantitative comparison between observation and reanalysis is
crucial, it is as fraught with difficulties as it is necessary. Even when GSOD
observations are available, they are limited to a few points in space and
time that may not represent a
A quantitative comparison has been carried out by computing the confusion
matrix of heat wave detection from observation and reanalysis. Tables 1 and 2
show the confusion matrices for the entire period 1981–2015 for maximum and
minimum temperature, respectively. Heat waves have been classified into four
classes, i.e. HWMId
The vast majority of the elements of the matrices are not on the top-left to bottom-right diagonal, i.e. the correct classification. Besides, we can observe a “decay” of the number of events correctly classified when the magnitude level increases. This is also due to the lower number of intense heat waves compared to the moderate ones.
Confusion matrix of heat wave detection from observation (GSOD) and reanalysis (ERA-Interim) for maximum temperature for the period 1981–2015.
Confusion matrix of heat wave detection from observation (GSOD) and reanalysis (ERA-Interim) for minimum temperature for the period 1981–2015.
The off-diagonal elements represent classification errors, i.e. the number of heat waves that ended up in another class during GSOD and ERA-Interim classification. For both maximum and minimum temperature we can observe that ERA-Interim often underestimates GSOD-based heat waves.
Overall, the values of accuracy of classification for maximum and minimum
temperature are 0.58 and 0.64, respectively. Note that these values are
highly influenced by the correct detection of HWMId
Figure 7 shows the density plot of population affected by heat waves of
maximum temperature (HWMId
Density plot of population affected by heat waves of maximum temperature
detected by reanalysis in 2011 using the LandScan population count database.
The
2011 results indicate that heat waves generally occur on populated areas. Similar results, shown in Fig. S5, using another population count dataset, namely the Joint Research Centre Urban Settlement Layer (Freire and Pesaresi, 2016), are obtained for 2014. The Joint Research Centre Urban Settlement Layer has been used to test another population dataset and further highlight this specific risk. The fact that two different years present similar patterns highlights the vulnerability of the African region and the importance of heat wave assessment and prediction.
In this work we present the results of the application of the HWMId in the assessment of climate change across Africa. Observation from GSOD and reanalysis from ERA-Interim datasets are used to identify heat waves, their temporal and spatial variability and their impacts on African population. GSOD observations are able to capture heat wave events at fine spatial scales, but they show a sparse coverage across Africa. Conversely, the reanalysis dataset, despite the coarse spatial resolution and the uncertainties, displays a homogeneous coverage.
Results from maximum temperature (HWMId
Specifically, from 1996 onwards it is possible to observe HWMId
Between 2006 and 2015 the frequency (spatial coverage) of extreme
heat waves had increased to 24.5 observations per year (
Minimum temperature exhibits incoherence between the results of GSOD and
ERA-Interim. GSOD-based HWMId
The pertinence of ERA-Interim HWMId is evaluated through comparisons with
GSOD records, recognizing that point measurements on the ground may not
adequately represent the hydrometeorological variability generally present
in the reanalysis pixel. Our results also show coherence between
observation-based and reanalysis-based heat waves using a visual comparison.
Instead, the quantitative analysis indicates that heat waves with a high
(i.e. HWMId
Many events are well documented in the news, indicating that HWMId is able to capture events that are perceived as heat waves by a broader public. Finally, the analysis of population hit by heat waves shows that the highest events affect the most populated regions rather than the uninhabited ones.
Our work has direct relevance for both scientists and policy makers. Increasing numbers of heat waves may pose challenges on health care and on electric supply for residential cooling demands, among others. These implications argue for the importance of enhancing the density of hydrometeorological stations to provide the baseline data that will be essential (1) for climate-change adaptation and (2) to reduce the uncertainties of reanalysis products. Further applications include (1) the employment of HWMId scheme to climatological models to quantify the increase in heat wave for the next decades; (2) the wider and quantitative implications of African heat waves on health, crops and finance; and (3) the analysis of teleconnections between the 2015/2016 El Niño event (Cesare, 2015) and heat waves in eastern Africa.
Dataset can be accessible from (1) Global Summary of the Day (GSOD) version 8,
National Climatic Data Center (
The authors declare that they have no conflict of interest.
Authors would like to thank the valuable support from JRC. This work has
received funding from European Commission EuropeAid Co-operation Office under
the grant agreement RALCEA. The data used in this paper can be obtained
from (1) Global Summary of the Day (GSOD) version 8, National Climatic Data
Center (