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Climate change can increase the risk of conditions that exceed human thermoregulatory capacity. Although numerous studies report increased mortality associated with extreme heat events, quantifying the global risk of heat-related mortality remains challenging due to a lack of comparable data on heat-related deaths. Here we conducted a global analysis of documented lethal heat events to identify the climatic conditions associated with human death and then quantified the current and projected occurrence of such deadly climatic conditions worldwide. We reviewed papers published between 1980 and 2014, and found 783 cases of excess human mortality associated with heat from 164 cities in 36 countries. Based on the climatic conditions of those lethal heat events, we identified a global threshold beyond which daily mean surface air temperature and relative humidity become deadly. Around 30% of the world's population is currently exposed to climatic conditions exceeding this deadly threshold for at least 20 days a year. By 2100, this percentage is projected to increase to -1/448% under a scenario with drastic reductions of greenhouse gas emissions and -1/474% under a scenario of growing emissions. An increasing threat to human life from excess heat now seems almost inevitable, but will be greatly aggravated if greenhouse gases are not considerably reduced. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
Global risk of deadly heat
Camilo Mora1*, Bénédicte Dousset2, Iain R. Caldwell3, Farrah E. Powell1, Rollan C. Geronimo1,
Coral R. Bielecki4, Chelsie W. W. Counsell3, Bonnie S. Dietrich5, Emily T. Johnston4, Leo V. Louis4,
Matthew P. Lucas6, Marie M. McKenzie1, Alessandra G. Shea1, Han Tseng1, Thomas W. Giambelluca1,
Lisa R. Leon7, Ed Hawkins8and Clay Trauernicht6
Climate change can increase the risk of conditions that exceed
human thermoregulatory capacity1–6. Although numerous stud-
ies report increased mortality associated with extreme heat
events1–7, quantifying the global risk of heat-related mortality
remains challenging due to a lack of comparable data on
heat-related deaths2–5. Here we conducted a global analysis
of documented lethal heat events to identify the climatic
conditions associated with human death and then quantified
the current and projected occurrence of such deadly climatic
conditions worldwide. We reviewed papers published between
1980 and 2014, and found 783 cases of excess human
mortality associated with heat from 164 cities in 36 countries.
Based on the climatic conditions of those lethal heat events,
we identified a global threshold beyond which daily mean
surface air temperature and relative humidity become deadly.
Around 30% of the world’s population is currently exposed
to climatic conditions exceeding this deadly threshold for at
least 20 days a year. By 2100, this percentage is projected
to increase to 48% under a scenario with drastic reductions
of greenhouse gas emissions and 74% under a scenario of
growing emissions. An increasing threat to human life from
excess heat now seems almost inevitable, but will be greatly
aggravated if greenhouse gases are not considerably reduced.
Sporadic heat events, lasting days to weeks, are often related to
increased human mortality1,2, raising serious concerns for human
health given ongoing climate change1–3,8–16 . Unfortunately, a number
of challenges have hampered global assessments of the risk of
heat-related death. First, heat illness (that is, severe exceedance
of the optimum body core temperature) is often underdiagnosed
because exposure to extreme heat often results in the dysfunction
of multiple organs, which can lead to misdiagnosis2,3,5,17 . Second,
mortality data from heat exposure are sparse and have not been
analysed in a consistent manner. Here we conducted a global
survey of peer-reviewed studies on heat-related mortality to identify
the location and timing of past events that caused heat-related
deaths. We used climatic data during those events to identify the
conditions most likely to result in human death and then quantified
the current and projected occurrence of such deadly climatic
conditions. Hereafter, we use ‘lethal’ when referring to climatic
conditions during documented cases of excess mortality and ‘deadly’
when referring to climatic conditions that are projected to cause
death. We make this distinction to acknowledge that climatic
conditions which have killed people in the past are obviously capable
of causing death, but whether or not they result in human mortality
in the future could be affected by adaptation. We do not quantify
human deaths per se because the extent of human mortality will
be considerably modified by social adaptation (for example, use of
air conditioning, early warning systems, and so on18–20 ). Although
social adaptation could reduce the exposure to deadly heat18–20, it
will not affect the occurrence of such conditions. Given the speed
of climatic changes and numerous physiological constraints, it is
unlikely that human physiology will evolve the necessary higher
heat tolerance21,22, highlighting that outdoor conditions will remain
deadly even if social adaptation is broadly implemented. Our aim is
to quantify where and when deadly heat conditions occur, which in
turn can provide important information on where social adaptation
will likely be needed.
We searched available online databases for peer-reviewed
publications on heat-related mortality published between 1980 and
2014 (see Methods). From over 30,000 relevant references, we
identified 911 papers that included data on 1,949 case studies
of cities or regions where excess mortality was associated with
high temperatures. Case studies were broadly grouped into those
focusing on temperature–mortality relationships in a specific city,
region, or country (1,166 cases from 273 cities across 49 countries)
and those focusing on heat-related mortality during specific
episodes (783 cases from 164 cities across 36 countries). Cases
were predominantly reported for cities at mid-latitudes, with the
highest concentration in North America and Europe (Fig. 1a), and
included well-documented heatwaves like those in Chicago in 1995
(740 deaths23), Paris in 2003 (4,870 deaths24), Moscow in 2010
(10,860 deaths25) and many other, less publicized events (list
of cases provided at
While data on the number of deaths was inconsistently reported, all
studies provided information on the place and dates when climatic
conditions were lethal, which we used to identify the specific
climatic conditions resulting in heat-related mortality.
To identify the climatic conditions related to lethal heat events,
we assessed daily climatic data (that is, surface air temperature,
relative humidity, solar radiation, wind speed, and several other
metrics, Supplementary Fig. 1) for the duration of lethal heat
episodes reported in the literature and an equal number of non-
lethal episodes (that is, periods of equal duration from the same
cities but from randomly selected dates); then we used Support
1Department of Geography, University of Hawai’i at M¯
anoa, Honolulu, Hawai’i 96822, USA. 2Hawai‘i Institute of Geophysics and Planetology, University of
Hawai‘i at M¯
anoa, Honolulu, Hawai’i 96822, USA. 3Hawai‘i Institute of Marine Biology, University of Hawai‘i at M¯
anoa, K¯
ane‘ohe, Hawai’i 96744, USA.
4Department of Botany, University of Hawai‘i at M¯
anoa, Honolulu, Hawai’i 96822, USA. 5Department of Plant and Environmental Protection Sciences,
University of Hawai‘i at M¯
anoa, Honolulu, Hawai’i 96822, USA. 6Department of Natural Resources and Environmental Management, University of Hawai‘i
at M¯
anoa, Honolulu, Hawai’i 96822, USA. 7Thermal and Mountain Medicine Division, U.S. Army Research Institute of Environmental Medicine,
Natick, Massachusetts 01760, USA. 8National Centre for Atmospheric Science, Department of Meteorology, University of Reading, Reading,
Berkshire RG6 6BB, UK. *e-mail:
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
Average daily temperature (°C)
Average daily relative humidity (%)
a b
Figure 1 | Geographical distribution of recent lethal heat events and their climatic conditions. a, Places where relationships between heat and mortality
have been documented (red squares) and where specific heat episodes have been studied (blue squares). b, Mean daily surface air temperature and
relative humidity during lethal heat events (black crosses) and during periods of equal duration from the same cities but from randomly selected dates
(that is, non-lethal heat events; red to yellow gradient indicates the density of such non-lethal events). Blue line is the SVM threshold that best separates
lethal and non-lethal heat events and the red line is the 95% probability SVM threshold; areas to the right of the thresholds are classified as deadly and
those to the left as non-deadly. Support vectors for other variables are shown in Supplementary Fig. 2.
1950 2000 2050 2100
Global land area (%)
1950 2000 2050 2100
Global human population (%)
RCP 8.5
RCP 4.5
RCP 2.6
Reanalysis data b
Figure 2 | Current and projected changes in deadly climatic conditions. a,b, Area of the planet (a) and percentage of human population (b) exposed to
climatic conditions beyond the 95% SVM deadly threshold (red line in Fig. 1b) for at least 20 days in a year under alternative emission scenarios. Bold lines
are the multimodel medians, black lines are the results from reanalysis data and faded lines indicate the projections for each Earth System Model. Time
series were smoothed with a 10-year-average moving window. Area of the planet and human population exposed to dierent lengths of time are shown in
Supplementary Fig. 4. Results correcting for climatological mean biases between the reanalysis data and each Earth System Model are shown in
Supplementary Figs 8 and 10.
Vector Machines (SVMs) to identify the climatic conditions that
best differentiated lethal and non-lethal episodes. SVMs generate
a threshold that maximizes the difference in the attributes of two
or more groups, allowing for classification of objects in either
group based on where their given attributes fall with respect to
the threshold. In our case, SVM was used to generate a decision
threshold that maximizes the difference in climatic conditions of
lethal and non-lethal episodes, with the conditions on one side of the
threshold being lethal and those to the other side being non-lethal
(for example, Fig. 1b). Among all possible pair combinations of the
variables analysed here (Supplementary Figs 1 and 2), the SVM
using mean daily surface air temperature and relative humidity most
accurately distinguished between past lethal and non-lethal heat
episodes (that is, 82%, blue line in Fig. 1b); accuracy was measured
as the ratio of the number of correctly classified lethal and non-
lethal cases to the total number of cases. Adding other variables
to the temperature–humidity SVM resulted in less parsimonious
SVMs with minimal increases in accuracy (for example, the SVM
model including all 16 variables analysed here was only 3% more
accurate, Supplementary Fig. 3). SVM also allows for estimation
of a classification probability that increases with the distance of an
observation to the decision threshold; the use of a 95% probability
for the temperature–humidity SVM (red line in Fig. 1b) resulted
in 100% accurate predictions of true positives (that is, only prior
lethal heat episodes were on the deadly side of the 95% probability
SVM decision boundary). While our analysis used data on local
climatic conditions, the resulting pattern between temperature
and relative humidity allowed us to accurately classify lethal heat
events of different cities worldwide using a single common SVM
threshold (Fig. 1b).
The fact that temperature and relative humidity best predict
times when climatic conditions become deadly is consistent with
human thermal physiology, as they are both directly related to
body heat exchange2–4. First, the combination of an optimum body
core temperature (that is, 37 C), the fact that our metabolism
generates heat (100 W at rest) and that an object cannot dissipate
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
0 50 100 150 200 250 300 350
Number of days per year above deadly threshold
RCP 2.6
RCP 4.5
RCP 8.5
Figure 3 | Geographical distribution of deadly climatic conditions under
dierent emission scenarios. ad, Number of days per year exceeding the
threshold of temperature and humidity beyond which climatic conditions
become deadly (Fig. 1b), averaged between 1995 and 2005 (a, historical
experiment), and between 2090 and 2100 under RCP 2.6 (b), RCP 4.5 (c)
and RCP 8.5 (d). Results are based on multimodel medians. Grey areas
indicate locations with high uncertainty (that is, the multimodel standard
deviation was larger than the projected mean; coecient of variance >1).
The expected lower number of deadly days at higher latitudes (Fig. 4) may
help explain the large variability among Earth System Models in the
projected number of deadly days at higher latitudes31 (for example, in the
case for New York (illustrated in Fig. 4j) the one model projects nine deadly
days by 2100; yet any other model projecting 18 days will double the
variability). The uncertainty presented in this figure should be interpreted
with that caution in mind.
heat to an environment with equal or higher temperature (that is,
the second law of thermodynamics22), dictates that any ambient
temperature above 37C should result in body heat accumulation
and a dangerous exceedance of the optimum body core temperature
(hyperthermia5). Second, sweating, the main process by which the
body dissipates heat, becomes ineffective at high relative humidity
(that is, air saturated with water vapour prevents evaporation of
sweat); therefore, body heat accumulation can occur at temperatures
lower than the optimum body core temperature in environments
of high relative humidity. These properties help to explain why
the boundary at which temperature becomes deadly decreases
with increasing relative humidity (Fig. 1b) and why in our results
some heat mortality events occurred at relatively low temperatures
(Fig. 1b). These consequences of temperature and humidity are why
both of these variables are included in traditional thermal indices
such as humidex26 and wet-bulb globe temperature22,27 .
To quantify the global extent of current deadly climatic
conditions, we applied the 95% probability SVM decision boundary
between mean daily surface air temperature and relative humidity
(red line in Fig. 1b, hereafter referred to as deadly threshold)
to current global climate data (see Methods). Using data from
a climate reanalysis (see Methods), we found that in 2000,
13.2% of the planet’s land area, where 30.6% of the world’s
human population resides, was exposed to 20 or more days
when temperature and humidity surpassed the threshold beyond
which such conditions become deadly (Fig. 2, extended results in
Supplementary Fig. 4). Comparatively, using climate simulations
for the year 2000 (that is, historical experiment) developed for
the Coupled Model Intercomparison Project phase 5 (CMIP5), we
found that 16.2% (±8.3% standard deviation, s.d.) of the planet’s
land area, where 37.0% (±9.7% s.d.) of the world’s population
resides, was exposed to 20 or more days of potentially deadly
conditions of temperature and humidity (results are multimodel
medians and standard deviations among Earth System Models;
Fig. 2). Both the reanalysis and historical CMIP5 data revealed
increasing trends in the area and population exposed to deadly
climates during the time period for which such datasets can be
compared, although the trends in the reanalysis data are slightly
weaker than in the Earth System Models (Fig. 2). Overall, there
was 3% mismatch in the area of the planet exposed to deadly
climates (6.4% in global population) between the reanalysis
and the multimodel median, and thus, results based on CMIP5
simulations should be interpreted with that error in mind. However,
the effects of this mismatch and the uncertainty among Earth
System Models were smaller than the predicted changes in deadly
days (Supplementary Fig. 10). It is worth noting that most scientific
publications on deadly heat events have focused in developed mid-
latitude countries (Fig. 1a); yet, deadly heat conditions also occur in
developing tropical countries (Fig. 3). This suggests that the risk of
deadly heat could be currently underestimated in tropical regions,
which has been noted in prior studies28.
To predict the global extent of future deadly climates, we applied
the deadly SVM threshold to mean daily surface air temperature and
relative humidity projections from the CMIP5 Earth System Models
under low, moderate, and high emissions scenarios (Representative
Concentration Pathways, RCPs, 2.6, 4.5, and 8.5, respectively). We
found that by 2100, even under the most aggressive mitigation
scenario (that is, RCP 2.6), 26.9% (±8.7% s.d.) of the world’s
land area will be exposed to temperature and humidity conditions
exceeding the deadly threshold by more than 20 days per year,
exposing 47.6% (±9.6% s.d.) of the world’s human population to
deadly climates (using Shared Socioeconomic Pathways projections
of future human population29 relevant to each of the CMIP5 RCPs,
see Methods). Scenarios with higher emissions will affect an even
greater percentage of the global land area and human population.
By 2100, 34.1% (±7.6% s.d.) and 47.1% (±8.9% s.d.) of the
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2 d 9 d 27 d 50 d
Distance from
the deadly threshold (unitless)
Distance from
the deadly threshold (unitless)
Distance from
the deadly threshold (unitless)
Distance from
the deadly threshold (unitless)
Average daily
relative humidity (%)
Average daily
relative humidity (%)
−40 −30 −20 −10 0 10 −40 −30 −20 −10 0 10 −40 −30 −20 −10 0 10 −40 −30 −20 −10 0 10
525 25 5
aHistorical RCP 2.6 RCP 4.5 RCP 8.5
Days in a year (%)
Latitude (° N)
New York
+1.6 °C +2.7 °C +5.5 °C
11 d 117 d 274 d 365 d
+0.9 °C +1.7 °C +3.8 °C
Average daily temperature (°C) Average daily temperature (°C) Average daily temperature (°C) Average daily temperature (°C)
Average daily temperature (°C) Average daily temperature (°C) Average daily temperature (°C) Average daily temperature (°C)
Figure 4 | Latitudinal risk of deadly climates. ad, Distribution of the percentage of days in a given year (that is, colour gradients), at each latitude, as a
function of their distance to the deadly threshold (red line in Fig. 1b). Displayed here are the last year in the historical experiment (that is, 2005; a) and the
year 2100 under RCP 2.6 (b), RCP 4.5 (c) and RCP 8.5 (d). These plots illustrate that higher latitudes have fewer days near the deadly threshold compared
with the tropics. el, As examples, we show mean temperature and relative humidity for each day in the year 2005 in the historical experiments and the
year 2100 for all the RCPs in Jakarta (eh) and New York (il), with consecutive days connected by lines. The 95% SVM threshold is shown as a red line
with numbers on the upper right hand corner indicating the number of days that cross the threshold and the dierence in temperature between 2100 and
2005. Examples are based on a single simulation of a randomly chosen model (that is, CSIRO-Mk3-6-0).
global land area will be exposed to temperature and humidity
conditions that exceed the deadly threshold for more than 20 days
per year under RCP 4.5 and RCP 8.5, respectively; this will expose
53.7% (±8.7% s.d.) and 73.9% (±6.6% s.d.) of the world’s human
population to deadly climates by the end of the century (Fig. 2,
extended results in Supplementary Fig. 4).
The projected number of days per year surpassing the deadly
threshold increases from mid-latitudes to the equator (Figs 4a–c,
5a and Supplementary Fig. 5a,d,g). By 2100, mid-latitudes (for
example, 40N or S) will be exposed to 60 deadly days per year
compared to almost the entire year in humid tropical areas under
RCP 8.5 (Figs 3b–d, 4b–d and 5a). This latitudinal pattern was con-
sistent among all scenarios (Supplementary Fig. 5a,d,g) and is largely
determined by the fact that the number of days with temperatures
close to the deadly threshold declines with increasing latitude (that
is, due to greater seasonality; Supplementary Fig. 6b–d28). At mid-
latitudes (for example, New York, Fig. 4i–l) temperatures approach
the deadly threshold only during the summer, which represents
a smaller proportion of the year; compared to tropical locations
(for example, Jakarta, Fig. 4e–h), which have consistently warm
temperatures near the deadly threshold year-round (Supplementary
Fig. 6). Although tropical humid areas will experience less warming
than higher latitudes (Fig. 5b, see also ref. 30), they will be exposed
to the greatest increase in the number of deadly days over time,
because higher relative humidity in tropical areas requires lower
temperatures to cross the deadly threshold (Figs 4e–h and 5e); a
condition that could be further aggravated by projected increases
in relative humidity of tropical areas (Fig. 5a). Subtropical and
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Latitude (° N)
1960 2000 2040 2080
Change in deadly days
−300 −200 −100 0 100 200 300
Change in temperature (°C)
−4 4−2 20
Change in relative humidity (%)
−10 10−5 50
Temperature (°C)
26 28 30 32 34
Relative humidity (%)
50 60 70 80 90 100
Latitude (° N)
1960 2000 2040 2080
Latitude (° N)
1960 2000 2040 2080
Latitude (° N)
1960 2000 2040 2080
Latitude (° N)
1960 2000 2040 2080
Figure 5 | Simulated spatio-temporal changes in deadly climatic conditions in Earth System Models. a, Average changes over time in the number of days
per year exceeding the deadly threshold. b,c, Changes in temperature (b) and changes in relative humidity (c) during those deadly days, relative to mean
values between 1995 and 2005. d,e, Mean temperature (d) and relative humidity (e) during deadly days. Results are grouped by latitude and are based on
the multimodel medians for the historical experiment, which runs from 1950 to 2005, and RCP 8.5, which runs from 2006 to 2100. Results for all scenarios
are shown in Supplementary Fig. 5.
mid-latitude areas will have fewer days beyond the deadly threshold,
but such deadly days will be much hotter in the future (Figs 4e–h
and 5b,d). This general variability in the climatic conditions of
deadly days (Fig. 5b–d and Supplementary Fig. 7) is probably related
to mean global climate patterns associated with the general cir-
culation of the atmosphere: equatorial convection (that is, warm,
moist air rising) produces high humidity in low latitudes whereas
subtropical atmospheric subsidence (that is, cool, dry air sinking)
creates low-precipitation, low-humidity zones, where high sensible
heat flux contributes to extreme high temperatures at mid-latitudes
(Supplementary Figs 5i and 7).
Our study underscores the current and increasing threat to
human life posed by climate conditions that exceed human ther-
moregulatory capacity. Lethal heatwaves are often mentioned as a
key consequence of ongoing climate change, with reports typically
citing past major events such as Chicago in 1995, Paris in 2003,
or Moscow in 20101–6. Our literature review indicates, however,
that lethal heat events already occur frequently and in many more
cities worldwide than suggested by these highly cited examples.
Our analysis shows that prior lethal heat events occurred beyond
a general threshold of combined temperature and humidity, and
that today nearly one-third of the world’s population is regularly
exposed to climatic conditions surpassing this deadly threshold. The
area of the planet and fraction of the world’s human population
exposed to deadly heat will continue to increase under all emission
scenarios, although the risk will be much greater under higher
emission scenarios. By 2100, almost three-quarters of the world’s
human population could be exposed to deadly climatic conditions
under high future emissions (RCP 8.5) as opposed to one-half
under strong mitigation (RCP 2.6). While it is understood that
higher latitudes will undergo more warming than tropical regions30 ,
our results suggest that tropical humid areas will be dispropor-
tionately exposed to more days with deadly climatic conditions
(Fig. 5a), because these areas have year-round warm temperatures
and higher humidity, thus requiring less warming to cross the deadly
threshold (Fig. 4 and Supplementary Fig. 6). The consequences
of exposure to deadly climatic conditions could be further aggra-
vated by an ageing population (that is, a sector of the popula-
tion highly vulnerable to heat2–4) and increasing urbanization (that
is, exacerbating heat-island effects2–4). Our paper emphasizes the
importance of aggressive mitigation to minimize exposure to deadly
climates and highlights areas of the planet where adaptation will be
most needed.
Methods, including statements of data availability and any
associated accession codes and references, are available in the
online version of this paper.
Received 2 June 2016; accepted 17 May 2017;
published online 19 June 2017
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We thank the Gridded Human Population of the World Database and the National
Center for Environmental Prediction and Department of Defense reanalysis database for
making their data openly available and B. Jones for sharing human population
projections. We acknowledge the World Climate Research Programme’s Working Group
on Coupled Modelling, which is responsible for CMIP5, and thank the climate modelling
groups (listed in Supplementary Table 1) for producing and making available their model
outputs. We also thank D. Schanzenbach, S. Cleveland and R. Merrill from the University
of Hawai’i Super Computer Facility for allowing access to computing facilities and
Hawai’i SeaGrant for providing funds to acquire some of the computers used in these
analyses. Q. Chen, A. Smith, C. Dau, R. Fang and S. Seneviratne provided valuable
contributions to the paper. The opinions or assertions contained herein are the private
views of the authors and are not to be construed as official or as reflecting the views of the
Army or the Department of Defense. We thank R. Carmichael, M. Deaton, D. Johnson
and M. Smith in ESRI’s Applications Prototype Lab for the creation of the online mapping
application. This paper was developed as part of the graduate course on ‘Methods for
Large-Scale Analyses’ in the Department of Geography, University of Hawai’i at M¯
Author contributions
All authors contributed to the design of the paper. C.M., B.D., I.R.C., F.E.P., R.C.G.,
C.R.B., C.W.W.C., B.S.D., E.T.J., L.V.L., M.P.L., M.M.M., A.G.S., H.T. and C.T. collected
data. C.M. and I.R.C. performed analysis. All authors contributed to the writing of
the paper.
Additional information
Supplementary information is available in the online version of the paper. Reprints and
permissions information is available online at Publisher’s note:
Springer Nature remains neutral with regard to jurisdictional claims in published maps
and institutional affiliations. Correspondence and requests for materials should be
addressed to C.M.
Competing financial interests
The authors declare no competing financial interests.
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
Survey of published cases of heat-related mortality. We searched for
peer-reviewed studies published between 1980 and 2014 on heat-related mortality
in Google Scholar, PubMed, and the Web of Science using the following keywords:
(human OR people) AND (mortality OR death OR lethal) AND (heat
OR temperature). We searched for papers primarily in English, but also included
papers in Spanish, French, Japanese and Chinese when found. We reviewed the
titles and abstracts of the first 30,000 citations in Google Scholar and all citations
from other databases and selected any peer-reviewed publications on heat-related
human mortality (we also searched for additional sources in the references). These
efforts resulted in 911 peer-reviewed papers from which we collected information
on the place and dates of lethal heat events. Several papers noted that human
mortality may have occurred beyond the dates in which the extreme climatic
conditions occurred (‘mortality displacement’); in those cases, we extracted the
dates for which the extreme climatic conditions were reported in the given studies.
Our goal was to identify the dates in which climatic conditions triggered human
mortality regardless of whether mortality was displaced or not.
Climatic conditions related to prior cases of heat-related mortality. For the cases
in the literature review that reported the place and time of lethal heat events, we
assessed information for 16 climatic metrics based on mean daily surface air
temperature, relative humidity, solar radiation, and wind speed (Supplementary
Fig. 1). For each of the lethal heat events, we also assessed the same climatic
variables for a paired ‘non-lethal’ event of the same duration and from the same
city but from a randomly chosen date. Climatic conditions were characterized
using daily data from an atmospheric reanalysis of past climate (NCEP-DOE
Reanalysis 2). We used the NCEP-DOE Reanalysis database because it is among the
most studied and is well characterized relative to newer databases. We used
Support Vector Machine (SVM) modelling to separate the climatic conditions
associated with prior lethal heat events from those associated with non-lethal
events. Using SVM, we generated a decision vector/threshold that maximized the
distance between lethal and non-lethal episodes, with the conditions on one side of
the threshold being lethal and those to the other side being non-lethal (for example,
Fig. 1b). We developed such SVM models for all combinations of the variables
collected and then compared the accuracy of models to choose the most
parsimonious and best performing one.
Projected occurrence of deadly climatic conditions. To quantify the number of
days in a year that surpass the threshold beyond which conditions become deadly
under alternative emission scenarios, we applied the 95% SVM probability
threshold between mean daily surface air temperature and relative humidity of
prior lethal heat events to daily climate projections of the same variables. We used
the 95% SVM probability threshold because it resulted in a much more accurate
classification of prior lethal heat events, and because it restricts projected lethal
heat events to much more extreme conditions, hence yielding more conservative
results. We used daily climate projections of mean surface air temperature and
relative humidity from 20 Earth System Models under four alternative emissions
scenarios developed for the recent Coupled Model Intercomparison Project Phase 5
(Supplementary Table 1). We used the ‘historical’ experiment, which includes the
period from 1950 to 2005 and the Representative Concentration Pathways 2.6, 4.5
and 8.5 (RCP 2.6, 4.5 and 8.5, respectively), which include the period from 2006 to
2100. The historical experiment was designed to model recent climate (reflecting
changes due to both anthropogenic and natural causes) and allows the validation of
model outputs against available climate observations (Supplementary Figs 8 and 9).
RCP pathways represent contrasting mitigation efforts between rapid greenhouse
gas reductions (RCP 2.6) and a business-as-usual scenario (RCP 8.5). All analyses
were run at the original resolution of each climate database and the results were
interpolated to a common 1.5grid cell size using a bilinear function.
Projections of global land coverage and risk to human populations from deadly
climatic conditions. To calculate the amount of land area and fraction of the
human population that are likely to be exposed to deadly climates each year, we
summed the land area and human population for all cells experiencing varying
numbers of days in a year beyond the deadly threshold (Fig. 2 and Supplementary
Fig. 4). We used the Gridded Population of the World from the Socioeconomic
Data and Applications Center (
population-count-future-estimates/data-download#) to estimate human exposure
up to the year 2005 and human population projections consistent with the different
emission scenarios used in the CMIP5 to estimate exposure between 2006 and
2100. For the population projections, we specifically used the spatially explicit
global population scenarios consistent with the Shared Socioeconomic Pathways
(SSP) developed by Jones et al.29, pairing RCP 2.6 with SSP1, RCP 4.5 with SSP3,
and RCP 8.5 with SSP5.
Limitations. There are several potential limitations to our study. First, the lethality
of deadly climatic conditions can be mediated by various demographic (for
example, age structure), socio-economic (for example, air conditioning, early
warning systems) and urban planning (for example, vegetation, high albedo
surface) factors that were not considered in our study. Consideration of these
factors would improve the understanding of global human vulnerability to heat
exposure and may reduce the number of human deaths, but they are unlikely to
affect the occurrence of deadly climatic conditions, which is what we estimated.
Second, our survey of cases of heat-related mortality was restricted to the period
between 1980 and 2014, and any bias or temporal heterogeneity in the monitoring
of lethal heatwaves and epidemiological studies in this period may influence the
cases we studied and the resulting SVM model. Third, while general agreement
among models was found in the predictions of deadly climatic conditions in
tropical areas, greater variability among models was seen in such projections at
higher latitudes (grey areas in Fig. 3). Because deadly conditions are more rare at
higher latitudes (Fig. 4), a larger number of model ensembles might allow for more
definitive statements about the risk of deadly climates in such regions, as has been
suggested for similar cases of rare events31. Finally, it is possible that some lethal
heat events were not documented in peer-reviewed publications and, if the dates of
those undocumented events happened to be selected as part of the non-lethal
events in our analysis, this could affect the resulting SVM model. However, this
error is likely minimal because there is a low probability of randomly selecting such
rare and brief events from a 30-year period in the given cities.
Data availability. The data that support the findings of this study are available
from the corresponding author upon request.
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
... Moreover, the likelihood of concurrent heat waves affecting multiple cities at the same as well as compound heat wave-drought events are expected to increase (IPCC 2022). One analysis found that 30% of the global population is already exposed to potentially lethal heat for at least 20 days per year (Mora et al. 2017). This figure could rise to approximately 48% by 2100, even with significant reductions in greenhouse gas emissions. ...
... The blue line is the threshold that statistically separates lethal and nonlethal heat events, and the red line is the 95% probability threshold; areas to the right of the thresholds are classified as deadly and those to the left as non-deadly. Source: Mora et al. (2017). ...
Technical Report
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This report was prepared under the Asian Development Bank (ADB) regional technical assistance (TA) project Advancing Inclusive and Resilient Urban Development Targeted at the Urban Poor (TA9513-REG). The project is financed by the Urban Climate Change Resilience Trust Fund (UCCRTF), which is administered by ADB with financial support from the Rockefeller Foundation and the governments of Switzerland and the United Kingdom. The report was prepared under the overall guidance of Arghya Sinha Roy, principal climate change specialist (Climate Change Adaptation), Sustainable Development and Climate Change Department (SDCC). The development of the report was led by a team of technical experts coordinated by Robert Wilby. The consultant team included Ashna Singh Mathema (housing), Belinda Tato (urban planning and urban design including related graphics), Katherine Gough (livelihoods), Mohamed El-Sioufi (urban basic services and infrastructure), Robert Wilby (physical climate risk and health), and Tord Kjellstrom (local economy and productivity). Tom Matthews produced the heat index maps in Chapter 2, Kae Sugawara edited the manuscript, and Lowil Espada produced the layout. Production and finalization were supported by Sugar Gonzales, climate change officer (Climate Change Adaptation), SDCC. The report benefited significantly from comments received from Joris van Etten, senior urban development specialist, Southeast Asia Department; Tiffany M. Tran, human settlements expert (consultant), Southeast Asia Department; Hikaru Shoji, senior urban development specialist, South Asia Department; members of the UCCRTF team: Virinder Sharma, principal urban development specialist, SDCC, and Joy Amor Bailey (consultant); and Rowena Mantaring (TA coordinator). The report also benefited from inputs and discussions with Charles Rodgers, senior climate adaptation advisor (consultant); and Alex Fowler, climate resilience specialist (consultant).
... The impacts of temperature-related climate disasters are on the rise under global warming (WMO UNEP IPCC et al. 2021;Watts et al. 2019;Thiery et al. 2021;IPCC 2022a;WMO 2022). In particular, intensified heat extremes which are often characterized by daily maximum and/or minimum surface air temperatures produce profound devastating effects on human health, urban and rural infrastructure, agricultural yields, energy demand, natural ecosystem and biodiversity, water resources, and more (Horton et al. 2016;Mora et al. 2017;Obradovich et al. 2017;Yang and Zhang 2020;Yang et al. 2021;WMO 2021WMO , 2022. Achieving the carbon neutrality by the middle of the century can effectively prevent rapidly increasing climate change risks relative to current development pathways and represents a pivotal milestone towards the Paris Agreement goal of 1.5 ℃ (IPCC 2021(IPCC , 2022a(IPCC , 2022b. ...
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Future climate projections provide vital information for preventing and reducing disaster risks induced by the global warming. However, little attention has been paid to climate change projections oriented towards carbon neutrality. In this study, we address projected changes in daily maximum (Tmax) and minimum (Tmin) temperatures as well as diurnal temperature range (DTR) over East Asia for the carbon neutrality period of 2050–2060 under the newly available SSP1-1.9 pathway of sustainable development by using CMIP6 model simulations. CMIP6 multi-model ensemble results show that Tmax and Tmin will significantly increase with varying magnitudes during the carbon neutrality period of 2050–2060 under SSP1-1.9 over the whole East Asia while both upward and downward changes will occur for the DTR. Projected Tmax, Tmin, and DTR changes all exhibit new spatial patterns during 2050–2060 under SSP1-1.9 compared with those over the same period under SSP2-4.5 and SSP5-8.5. Compared to 1995–2014, projected Tmax and Tmin averaged over East Asia during 2050–2060 will significantly warm up by 1.43 ℃ and 1.40 ℃ under SSP1-1.9, while the warming magnitudes are 1.93 ℃ and 2.04 ℃ under SSP2-4.5, and 2.67 ℃ and 2.85 ℃ under SSP5-8.5. Research on carbon neutrality-oriented climate change projections needs to be strengthened for jointly achieving a net-zero future.
... Currently 30% of the global population is exposed to deadly heat waves and this percentage by 2100 is projected to increase to ~48% under a drastic mitigation scenario to ~74% under a scenario of growing emissions. (Mora et al., 2017). ...
Extreme temperatures have reached unprecedented levels in many regions of the globe due to climate change, and a further increase is expected. Besides other consequences, high temperatures increase the mortality risk and severely affect the labour productivity of workers. We perform a high-resolution spatial analysis to assess the impacts of heat on mortality and labour productivity in Switzerland and project their development under different Representative Concentration Pathway (RCP) scenarios, considering that no socio-economic changes take place. The model is based on the risk framework of the Intergovernmental Panel on Climate Change (IPCC), which combines the three risk components: hazard, exposure, and vulnerability. We model the two impact categories in the same spatially explicit framework, and we integrate uncertainties into the analysis by a Monte Carlo simulation. We model first that about 658 deaths are associated with heat exposure currently each year in Switzerland. Second, the economic costs caused by losses in labour productivity amount to around CHF 665 million (approx. USD 700 million) per year. Should we remain on an RCP8.5 emissions pathway, these values may double (for mortality) or even triple (for labour productivity) by the end of the century. Under an RCP2.6 scenario impacts are expected to slightly increase and peak around mid-century, when climate is assumed to stop warming. Even though uncertainties in the model are large, the underlying trend in impacts is unequivocal. The results of the study are valuable information for political discussions and allow for a better understanding of the cost of inaction.
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LINK TO THE PAPER: Climate change is taking place on a global scale and it is substantially affected by human activity, including increasing greenhouse gas emissions. One of the thematic objectives of EU’s new financial objective is a more environmentally friendly low-emission Europe that promotes clean and fair energy transformation, green investments, and a circular economy, among others. The Polish economy is mainly based on energy production from conventional sources (fossil fuels). Considering that the demand for electricity in Poland is predicted to increase by as much as 50% until 2040, it is necessary to take action aimed at increasing the share of renewable energy sources. The subject of analysis is the Opolskie Voivodeship (a NUTS 2 type region), the capital of which features the biggest Polish coal power plant. In 2014–2019, it was expanded by two units with 1800 MW in total capacity, thereby indicating that investments in energy obtained from conventional sources are still implemented and to a large extent at that (the expansion has been the biggest infrastructural investment in Poland since 1989). The Opolskie region is characterised by substantial excess in acceptable environmental burden (dust pollution, among others). The aim of the paper is to evaluate the key environmental conditions for the Opolskie region’s development in terms of the assumptions of the domestic and EU energy policies. The Opolskie region’s developmental challenges in the environmental area were determined on the basis of selected indicator estimations up to 2030. The research hypothesis assumes that the environmental conditions for the Opolskie region’s development are unfavourable. The methodological part features an analysis of the cause and effect dependencies in the “environment” area, which enabled an assessment of the Opolskie Voivodeship’s current situation as well as an analysis of the dependencies relevant to the region’s development. This was followed by an estimation of selected indicators in the “environment” area until 2030, which allowed for an assessment of their probable levels and thereby a specification of the region’s development conditions. The estimation was conducted using the data available in public statistics, i.e., Statistics Poland’s data. The indicators estimated for 2030 were presented using three forecasting methods: (a) the monotonic trend, (b) the yearly average change rate, and (c) the logarithmic trend. Keywords: region development; environmental conditions; air pollution; renewable energy; energy policy
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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19, emerged in late 2019, halfway through the preparation of the IPCC WGII Sixth Assessment Report. This Cross-Chapter Box assesses how the massive shock of the pandemic and response measures interact with climate-related impacts and risks as well as its significant implications for risk management and climate resilient development.
Urban agglomerations are currently facing regional thermal environment deterioration. However, the relationship between thermal environment changes in urban agglomerations in response to urban expansion and the underlying urban morphology-driven mechanisms is not clear. This study utilized data from the three largest urban agglomerations in China for 2000, 2010, and 2020 to explore the response of regional heat island changes to urban morphological variations induced by urban expansion through the quantification of urban landscape form, correlation analysis, and relative importance analysis. The results indicate that the distribution of heat source and built-up areas in urban agglomerations has clear spatial and temporal consistency. Moreover, a high regional heat island intensity (RHII) cluster was shown in a “strip-like” form in Beijing–Tianjin–Hebei and the Yangtze River Delta, while the Pearl River Delta, with the most rapid expansion and contiguity of heat source areas, showed a “ring-like” form. RHII was positively correlated with the area of urban clusters and the proportion of built-up areas. However, configuration metrics, such as patch aggregation, also positively affected RHII. Thus, different landscape structures with the same impervious surface area percentage resulted in different RHII values. The relative importance of urban form metrics varied in different urbanization stages; the impervious layer rate was dominant for low and high urban intensity levels, while the shape complexity of urban patches primarily mitigated the thermal environment at the medium urban development level. These results revealed the response relationship between the regional thermal environment and urban morphology, providing insights into how we can improve the regional thermal environment through targeted strategies for optimizing urban form patterns for areas at different urbanization stages.
Buildings play a significant role in indoor and outdoor exposure to heat in urban areas. In this study, we quantify the heat mitigation potential of typical building energy efficiency measures that are often not considered as urban heat mitigation strategies, such as added insulation. We combined whole-building energy and urban climate simulations to compare indoor and outdoor (pedestrian-level) heat exposure with different levels of energy efficiency and under different climate timeframes in a soon-to-be-built public housing project in Phoenix, AZ. We found that improved energy efficiency reduces indoor and outdoor exposure to heat while climate change increases both. Considering the 2018 version of the energy code as the baseline, the mitigating impact of upgrading energy efficiency on indoor exposure to heat (as defined by % of year Tindoor > Tcooling setpoint +1 ˚C) exceeded the increase caused by climate change. Our estimates show a 6.6 % increase caused by climate change vs. 20.7 % reduction due to improved efficiency. Furthermore, our results indicate that energy upgrades may also have an impact on outdoor heat exposure (as defined by % of year with Toutdoor> 40 ˚C) due to reduced heat emitted from the buildings and their HVAC systems. We found a 2% increase in exposure caused by climate change vs. 1.4 % reduction due to by improved efficiency. This suggest that upgrading energy efficiency of buildings may at least partially offset the impact of climate change on outdoor exposure to heat in the modelled urban canyon.
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Indigenous knowledge refers to the understandings, skills and philosophies developed by societies with long histories of interaction with their natural surroundings (UNESCO, 2018; IPCC, 2019a). Local knowledge refers to the understandings and skills developed by individuals and populations, specific to the places where they live (UNESCO, 2018; IPCC, 2019a). Indigenous knowledge and local knowledge are inherently valuable but have only recently begun to be appreciated and in western scientific assessment processes in their own right (Ford et al., 2016). In the past these often endangered ways of knowing have been suppressed or attacked (Mustonen, 2014). Yet these knowledge systems represent a range of cultural practices, wisdom, traditions and ways of knowing the world that provide accurate and useful climate change information, observations and solutions (very high confidence) (Table Cross-Chapter Box INDIG.1). Rooted in their own contextual and relative embedded locations, some of these knowledges represent unbroken engagement with the earth, nature and weather for many tens of thousands of years, with an understanding of the ecosystem and climatic changes over longer-term timescales that is held both as knowledge by Indigenous Peoples and local peoples, as well as in the archaeological record (Barnhardt and Angayuqaq, 2005; UNESCO, 2018).
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The projected size and spatial distribution of the future population are important drivers of global change and key determinants of exposure and vulnerability to hazards. Spatial demographic projections are widely used as inputs to spatial projections of land use, energy use, and emissions, as well as to assessments of the impacts of extreme events, sea level rise, and other climate-related outcomes. To date, however, there are very few global-scale, spatially explicit population projections, and those that do exist are often based on simple scaling or trend extrapolation. Here we present a new set of global, spatially explicit population scenarios that are consistent with the new Shared Socioeconomic Pathways (SSPs) developed to facilitate global change research. We use a parameterized gravity-based downscaling model to produce projections of spatial population change that are quantitatively consistent with national population and urbanization projections for the SSPs and qualitatively consistent with assumptions in the SSP narratives regarding spatial development patterns. We show that the five SSPs lead to substantially different spatial population outcomes at the continental, national, and sub-national scale. In general, grid cell-level outcomes are most influenced by national-level population change, second by urbanization rate, and third by assumptions about the spatial style of development. However, the relative importance of these factors is a function of the magnitude of the projected change in total population and urbanization for each country and across SSPs. We also demonstrate variation in outcomes considering the example of population existing in a low-elevation coastal zone under alternative scenarios.
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It has been argued that climate change is the biggest global health threat of the 21st century. The extreme high temperatures of the summer of 2003 were associated with up to seventy thousand excess deaths across Europe. Previous studies have attributed the meteorological event to the human influence on climate, or examined the role of heat waves on human health. Here, for the first time, we explicitly quantify the role of human activity on climate and heat-related mortality in an event attribution framework, analysing both the Europe-wide temperature response in 2003, and localised responses over London and Paris. Using publicly-donated computing, we perform many thousands of climate simulations of a high-resolution regional climate model. This allows generation of a comprehensive statistical description of the 2003 event and the role of human influence within it, using the results as input to a health impact assessment model of human mortality. We find large-scale dynamical modes of atmospheric variability remain largely unchanged under anthropogenic climate change, and hence the direct thermodynamical response is mainly responsible for the increased mortality. In summer 2003, anthropogenic climate change increased the risk of heat-related mortality in Central Paris by ∼70% and by ∼20% in London, which experienced lower extreme heat. Out of the estimated ∼315 and ∼735 summer deaths attributed to the heatwave event in Greater London and Central Paris, respectively, 64 (±3) deaths were attributable to anthropogenic climate change in London, and 506 (±51) in Paris. Such an ability to robustly attribute specific damages to anthropogenic drivers of increased extreme heat can inform societal responses to, and responsibilities for, climate change.
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Climate change has led to significant rise of 0.8˚C–0.9˚C in global mean temperature over the last century and has been linked with significant increases in the frequency and severity of heat waves (extreme heat events). Climate change has also been increasingly connected to detrimental human health. One of the consequences of climate-related extreme heat exposure is dehydration and volume loss, leading to acute mortality from exacer-bations of pre-existing chronic disease, as well as from outright heat exhaustion and heat stroke. Recent studies have also shown that recurrent heat exposure with physical exertion and inadequate hydration can lead to CKD that is distinct from that caused by diabetes, hypertension, or GN. Epidemics of CKD consistent with heat stress nephropathy are now occurring across the world. Here, we describe this disease, discuss the locations where it appears to be manifesting, link it with increasing temperatures, and discuss ongoing attempts to prevent the disease. Heat stress nephropathy may represent one of the first epidemics due to global warming. Government, industry, and health policy makers in the impacted regions should place greater emphasis on occupational and community interventions.
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Using ensembles from the Community Earth System Model (CESM) under a high and a lower emission scenarios, we investigate changes in statistics of extreme daily temperature. The ensembles provide large samples for a robust application of extreme value theory. We estimate return values and return periods for annual maxima of the daily high and low temperatures as well as the 3-day averages of the same variables in current and future climate. Results indicate statistically significant increases (compared to the reference period of 1996–2005) in extreme temperatures over all land areas as early as 2025 under both scenarios, with statistically significant differences between them becoming pervasive over the globe by 2050. The substantially smaller changes, for all indices, produced under the lower emission case translate into sizeable benefits from emission mitigation: By 2075, in terms of reduced changes in 1-day heat extremes, about 95 % of land regions would see benefits of 1 °C or more under the lower emissions scenario, and 50 % or more of the land areas would benefit by at least 2 °C. 6 % of the land area would benefit by 3 °C or more in projected extreme minimum temperatures and 13 % would benefit by this amount for extreme maximum temperature. Benefits for 3-day metrics are similar. The future frequency of current extremes is also greatly reduced by mitigation: by the end of the century, under RCP8.5 more than half the land area experiences the current 20-year events every year while only between about 10 and 25 % of the area is affected by such severe changes under RCP4.5.
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Human thermoregulation and acclimatization are core components of the human coping mechanism for withstanding variations in environmental heat exposure. Amidst growing recognition that curtailing global warming to less than two degrees is becoming increasing improbable, human survival will require increasing reliance on these mechanisms. The projected several fold increase in extreme heat events suggests we need to recalibrate health protection policies and ratchet up adaptation efforts. Climate researchers, epidemiologists, and policy makers engaged in climate change adaptation and health protection are not commonly drawn from heat physiology backgrounds. Injecting a scholarly consideration of physiological limitations to human heat tolerance into the adaptation and policy literature allows for a broader understanding of heat health risks to support effective human adaptation and adaptation planning. This paper details the physiological and external environmental factors that determine human thermoregulation and acclimatization. We present a model to illustrate the interrelationship between elements that modulate the physiological process of thermoregulation. Limitations inherent in these processes, and the constraints imposed by differing exposure levels, and thermal comfort seeking on achieving acclimatization, are then described. Combined, these limitations will restrict the likely contribution that acclimatization can play in future human adaptation to global warming. We postulate that behavioral and technological adaptations will need to become the dominant means for human individual and societal adaptations as global warming progresses.
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Recent investigations have reported a decline in the heat-related mortality risk during the last decades. However, these studies are frequently based on modelling approaches that do not fully characterize the complex temperature-mortality relationship, and are limited to single cities or countries. To assess the temporal variation in heat-mortality associations in a multi-country data set using flexible modelling techniques. We collected data for 272 locations in Australia, Canada, Japan, South Korea, Spain, UK and USA, with a total 20,203,690 deaths occurring in summer months between 1985 and 2012. The analysis was based on two-stage time series models. The temporal variation in heat-mortality relationships was estimated in each location with time-varying distributed lag non-linear models, expressed through an interaction between the transformed temperature variables and time. The estimates were pooled by country through multivariate meta-analysis. Mortality risk due to heat appeared to decrease over time in several countries, with relative risks associated to high temperatures significantly lower in 2006 compared with 1993 in the USA, Japan, and Spain, and a non-significant decrease in Canada. Temporal changes are difficult to assess in Australia and South Korea due to low statistical power, while we found little evidence of variation in the UK. In the USA, the risk seems to be completely abated in 2006 for summer temperatures below their 99th percentile, but some significant excess persists for higher temperatures in all the countries. We estimated a statistically significant decrease in the RR for heat-related mortality in 2006 compared to 1993 in the majority of countries included in the analysis.
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Background: Studies have examined the effects of temperature on mortality in a single city, country, or region. However, less evidence is available on the variation in the associations between temperature and mortality in multiple countries, analyzed simultaneously. Methods: We obtained daily data on temperature and mortality in 306 communities from 12 countries/regions (Australia, Brazil, Thailand, China, Taiwan, Korea, Japan, Italy, Spain, United Kingdom, United States, and Canada). Two-stage analyses were used to assess the nonlinear and delayed relation between temperature and mortality. In the first stage, a Poisson regression allowing overdispersion with distributed lag nonlinear model was used to estimate the community-specific temperature-mortality relation. In the second stage, a multivariate meta-analysis was used to pool the nonlinear and delayed effects of ambient temperature at the national level, in each country. Results: The temperatures associated with the lowest mortality were around the 75th percentile of temperature in all the countries/regions, ranging from 66th (Taiwan) to 80th (UK) percentiles. The estimated effects of cold and hot temperatures on mortality varied by community and country. Meta-analysis results show that both cold and hot temperatures increased the risk of mortality in all the countries/regions. Cold effects were delayed and lasted for many days, whereas heat effects appeared quickly and did not last long. Conclusions: People have some ability to adapt to their local climate type, but both cold and hot temperatures are still associated with increased risk of mortality. Public health strategies to alleviate the impact of ambient temperatures are important, in particular in the context of climate change.
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Projections of changes in climate extremes are critical to assessing the potential impacts of climate change on human and natural systems. Modeling advances now provide the opportunity of utilizing global general circulation models (GCMs) for projections of extreme temperature and precipitation indicators. We analyze historical and future simulations of ten such indicators as derived from an ensemble of 9 GCMs contributing to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR4), under a range of emissions scenarios. Our focus is on the consensus from the GCM ensemble, in terms of direction and significance of the changes, at the global average and geographical scale. The climate extremes described by the ten indices range from heat-wave frequency to frost-day occurrence, from dry-spell length to heavy rainfall amounts. Historical trends generally agree with previous observational studies, providing a basic sense of reliability for the GCM simulations. Individual model projections for the 21st century across the three scenarios examined are in agreement in showing greater temperature extremes consistent with a warmer climate. For any specific temperature index, minor differences appear in the spatial distribution of the changes across models and across scenarios, while substantial differences appear in the relative magnitude of the trends under different emissions rates. Depictions of a wetter world and greater precipitation intensity emerge unequivocally in the global averages of most of the precipitation indices. However, consensus and significance are less strong when regional patterns are considered. This analysis provides a first overview of projected changes in climate extremes from the IPCC-AR4 model ensemble, and has significant implications with regard to climate projections for impact assessments.
Increased temperature will result in longer, more frequent, and more intense heat waves. Changes in temperature variability have been deemed necessary to account for future heat wave characteristics. However, this has been quantified only in Europe and North America, while the rest of the globe remains unexplored. Using late century global climate projections, we show that annual mean temperature increases is the key factor defining heat wave changes in most regions. We find that commonly studied areas are an exception rather than the standard and the mean climate change signal generally outweighs any influence from variability changes. More importantly, differences in warming across seasons are responsible for most of the heat wave changes and their consideration relegates the contribution of variability to a marginal role. This reveals that accurately capturing mean seasonal changes is crucial to estimate future heat waves and reframes our interpretation of future temperature extremes.
Download Free Sample Heat illnesses exist along a continuum starting with the mild condition of heat exhaustion and progressing to heat injury and heat stroke. Heat stroke is a life-threatening condition clinically characterized by a severe elevation in body temperature with central nervous system dysfunction that often includes combativeness, delirium, seizures, and coma. Classic heat stroke is experienced primarily by the very young or elderly during annual heat waves. Exertional heat stroke is a condition experienced by young, fit individuals during strenuous physical activity in hot or temperate environments. Heat stroke sequelae are a consequence of heat injury to the tissues in combination with coagulopathies and a systemic inflammatory response syndrome (SIRS) that often culminates in multi-organ system dysfunction or death. Endotoxin leakage across ischemic-damaged gut membranes is thought to initiate the SIRS with cytokines and other inflammatory mediators involved in this multi-factoria...