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Spatial Patterns of Human Thermal Comfort Conditions in Russia: Present Climate and Trends

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The assessment of bioclimatic conditions at the national scale remains a highly relevant task. It might be one of the main parts of the national strategy for the sustainable development of different regions under changing climatic conditions. This study evaluated the thermal comfort conditions and their changes in Russia according to gridded meteorological data from ERA-Interim reanalysis with a spatial resolution of 0.75° × 0.75° using the two most popular bioclimatic indices based on the human energy balance: physiologically equivalent temperature (PET) and universal thermal comfort index (UTCI). We analyzed the summer and winter means of these indices as well as the repeatability of different thermal stress grades for the current climatological standard normal period (1981–2010) and the trends of these parameters over the 1979–2018 period. We revealed the high diversity of the analyzed parameters in Russia as well as significant differences between the contemporary climate conditions and their changes in terms of mean temperature, mean values of bioclimatic indices, and thermal stress repeatability. Within the country, all degrees of thermal stress were possible; however, severe summer heat stress was rare, and in winter nearly the whole country experienced severe cold stress. Multidirectional changes in bioclimatic conditions were observed in Russia against the general background of climate warming. The European part of the country was most susceptible to climate change because it experiences significant changes both in summer and winter thermal stress repeatability. Intense Arctic warming was not reflected in significant changes in thermal stress repeatability.
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Spatial Patterns of Human Thermal Comfort Conditions in Russia:
Present Climate and Trends
MIKHAIL VARENTSOV
Research Computing Center/Faculty of Geography, Lomonosov Moscow State University,
and A.M. Obukhov Institute of Atmospheric Physics, Moscow, Russia
NATALIA SHARTOVA
Faculty of Geography, Lomonosov Moscow State University, Moscow, Russia
MIKHAIL GRISCHENKO
Faculty of Geography, Lomonosov Moscow State University, and Faculty of Geography and Geoinformatics,
Higher School of Economics, Moscow, Russia
PAVEL KONSTANTINOV
Faculty of Geography, Lomonosov Moscow State University, Moscow, Russia
(Manuscript received 14 November 2019, in final form 18 June 2020)
ABSTRACT
The assessment of bioclimatic conditions at the national scale remains a highly relevant task. It might be
one of the main parts of the national strategy for the sustainable development of different regions under
changing climatic conditions. This study evaluated the thermal comfort conditions and their changes in
Russia according to gridded meteorological data from ERA-Interim reanalysis with a spatial resolution of
0.75830.758using the two most popular bioclimatic indices based on the human energy balance: phys-
iologically equivalent temperature (PET) and universal thermal comfort index (UTCI). We analyzed the
summer and winter means of these indices as well as the repeat ability of different thermal stress grades for
the current climatological standard normal period (1981–2010) and the trends of these parameters over
the 1979–2018 period. We revealed the high diversity of the analyzed parameters in Russia as well as
significant differences between the contemporary climate conditions and their changes in terms of mean
temperature, mean values of bioclimatic indices, and thermal stress repeatability. Within the country, all
degrees of thermal stress were possible; however, severe summer heat stress was rare, and in winter nearly
the whole country experienced severe cold stress. Multidirectional changes in bioclimatic conditions were
observed in Russia against the general background of climate warming. The European part of the country
was most susceptible to climate change because it experiences significant changes both in summer and
winter thermal stress repeatability. Intense Arctic warming was not reflected in significant changes in
thermal stress repeatability.
1. Introduction
Global warming will lead to an increase in the number
of extreme weather events, such as heat and cold waves,
as well as bioclimatic changes (IPCC 2013). This issue is
important for the assessment of both weather-related
health consequences and spatiotemporal variability of
bioclimatic conditions.
One of the most negative health consequences is an
increase in additional mortality, primarily due to car-
diovascular and respiratory diseases (Kovats and Hajat
2008). Air temperature continues to be an important
risk factor after the most significant heat events in
Chicago, Illinois, in 1995 (Dematte et al. 1998), Europe
in 2003 (de’Donato et al. 2015), Moscow, Russia, in 2010
(Konstantinov et al. 2014;Shaposhnikov et al. 2014), and
the most recent heat wave in Europe in June and July
2019. The number of days with adverse weather events
Corresponding author: Natalia Shartova, shartova@yandex.ru
JULY 2020 V A R E N T S O V E T A L . 629
DOI: 10.1175/WCAS-D-19-0138.1
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may rise in the future, which will lead to an increase in
climate-related mortality.
The development of timely preventive public health
activities requires accurate assessment of thermal sen-
sations. At present, about hundred bioclimatic indices
have been developed worldwide to characterize the
impact of the thermal environment on human beings in
terms of thermal comfort (Blazejczyk et al. 2012).
Thermal comfort can be described as the mental and
physiological contentment with thermal environment
(ASHRAE 1966;Parsons 2003). It is a condition in
which an individual would prefer neither a warmer nor a
cooler temperature.
Most of the bioclimatic indices are based on empirical
meteorological parameters. They are often called sim-
plistic indexes. Small number of indices include human
energy balance models (rational indices) (Katavoutas
and Flocas 2018). The indices based on direct mea-
surements of meteorological values, such as air tem-
perature, wind speed, and ambient humidity, are more
convenient for calculation and more practical to implement.
For example, the United States (Weinberger et al. 2018)and
Canadian (Smoyer-Tomic and Rainham 2001) weather
forecasting services use the heat index, humidex, and wind
chill temperature index. Most of these indices, however,
have a major limitation—their thermophysiological rele-
vance (Mayer and Höppe 1987;Potchter et al. 2018).
The indices based on human energy balance models
provide a more comprehensive and accurate represen-
tation of human thermal perception (Blazejczyk et al.
2012;Shartova and Konstantinov 2019); however, they
require more input environmental data, including radi-
ation fluxes, which should be measured or estimated, as
well as metabolic heat and clothing insulation. This al-
lows physiologically significant evaluation of thermal
conditions (Matzarakis et al. 1999) and considers the
physical environment and human physiology, as well as
associated psychological responses (Vanos et al. 2010).
As early as 1938, Büttner (1938) indicated that we need
an energetic approach to interactions between the human
body and the environment. However, the development of
energy balance models of the human body became pos-
sible only with the advent of computer technology in the
1970s and 1980s (Höppe 1997). Currently, a variety of
powerful tools is applied worldwide, including energy
balance models of the human body. The most useful
tools include the Comfort Formula energy budget model
(COMFA model; Brown and Gillespie 1986), Rayman
model (Matzarakis et al. 2007), and Solar Longwave
Environmental Irradiance Geometry model (SOLWEIG
model; Lindberg et al. 2008) and the detailed three-
dimensional ‘‘ENVI-met’’ micrometeorological model
(Fabbri et al. 2017) and the Parallelized Large-Eddy
Simulation Model (PALM; Fröhlich and Matzarakis
2020). These tools and models are actively used to in-
vestigate the methods of creating optimal thermal com-
fort in real urban environments (Vanos et al. 2010).
Many studies have been aimed at defining thermal
conditions for humans in the outdoor environment and
grading thermal sensation (Potchter et al. 2018;Bauche
et al. 2013;Vitkina et al. 2019;Kajtar et al. 2017). The
pioneering predicted mean vote (PMV) model of ther-
mal comfort, which was based on college-aged students,
was created by P. O. Fanger in the late 1960s, but it is still
used worldwide in environmental engineering. This in-
dex requires improved predictive ability, which might
be achieved through better specification of the model’s
input parameters and accounting for special groups (van
Hoof 2008). The use of the PMV index in urban areas in
Sweden shown that the steady-state models are not ap-
plicable in case of short-term outdoor thermal comfort
assessment due to the complication in analysis of tran-
sient exposure (Thorsson et al. 2004). A similar study of
subarctic climate (Umea, Sweden) showed that local resi-
dents are more adapted to the subarctic summer than
nonlocals (Yang et al. 2017). Neutral physiologically
equivalent temperature (PET) values for summer and
winter were calculated according to regression lines that
were based on output of interviews and PET values during a
field survey in Rome, Italy, using over 1000 questionnaires
(Salata et al. 2016). The studies were performed both for
the European region and for Russian far east regions, where
the combinations of meteorological factors form different
thermal comfort conditions (Vitkina et al. 2019).
Although a large number of indices have been de-
veloped, only four of them [PET, PMV, universal
thermal comfort index (UTCI), and standard effective
temperature (SET)] are widely used for outdoor ther-
mal perception studies (Potchter et al. 2018). The PMV
index is more widespread as a tool for outdoor and
indoor thermal assessments for building design and
redesign (Ricciu et al. 2018). SET is similarly used in
urban semioutdoor and outdoor environments, for ex-
ample, streets, railway stations, bus shelters, ferry ter-
minals, and parks (Hwang and Lin 2007;Zhao et al.
2016). The PET and UTCI indices are widely applied in
different regions with various spatial resolutions—from
the local to regional level (Blazejczyk et al. 2012). Some
of these applications have been specifically focused on
thermal comfort investigations of resting places (parks
and squares) in urban environments (Égerházi et al.
2013;Thorsson et al. 2004). According to Potchter et al.
(2018), PET has been constantly used since 2006, and
since 2012, it has become the most frequently used in-
dex. The UTCI first appeared in 2012, and since then, it
has been used more often.
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Despite the rapidly increasing use of urban or intraurban
assessments of thermal comfort around the world (Mayer
et al. 2008;Matzarakis et al. 2009;Grigorieva and
Matzarakis 2011;Matzarakis et al. 2013;Chi et al. 2018),
obtaining information about bioclimatic conditions at the
national scale remains a highly relevant task (Vinogradova
2009;Jacobs et al. 2013;Emelina et al. 2014;Vinogradova
and Zolotokrylin 2014;Giannaros et al. 2018;Wu et al.
2019). It is necessary to develop a national strategy for
the sustainable development of different regions under
changing climate conditions. The use of reanalysis data
(gridded meteorological data with global coverage)
might be one of the possible solutions (Di Napoli et al.
2018). This issue is relevant, especially for countries with
large territories, where the spatiotemporal variations in
thermal comfort based on human thermal balance in-
dices have not been investigated on a national scale.
The task of the national-scale assessment of biocli-
matic conditions is very relevant, but it is challenging for
the Russian Federation, which is the world’s largest state
(17.1 million km
2
) and has very diverse climatic condi-
tions (Kobysheva 2001). According to the Koppen–
Geiger climate classification, the types of the Russian
climate vary from relatively mild Cfa in the southwest to
extremely severe Dfc, Dfc, and Dwd in Siberia and
tundra (ET) and ice cap (EF) in the Arctic (Kottek et al.
2006). Wide areas of Russia, especially in its northern
regions, are hot spots of recent climate warming (IPCC
2013;Serreze et al. 2009). However, the bioclimatic
conditions in Russia have not yet been evaluated in
terms of the convenient thermal stress indices on the
national scale. The few previous studies that have at-
tempted to evaluate bioclimatic conditions in Russia are
limited only by the analysis of simple indices, which are
not connected with physiology (Emelina et al. 2014;
Vinogradova and Zolotokrylin 2014), or by the analysis
of the monthly mean UTCI values (Vinogradova 2019).
These generalizations do not allow for more detailed
analysis of the spatiotemporal relationships and ten-
dencies. Additionally, these studies are published only
in Russian, which decreases the availability of their re-
sults for the international scientific community.
In the current study, we aimed to evaluate the spatial
patterns of thermal comfort conditions in Russia and to
investigate their long-term trends for a contemporary
climate based on state-of-the-art biometeorological in-
dices and widely used gridded meteorological data,
namely, ERA-Interim reanalysis.
2. Data and methods
The biometeorological indices could be calculated based
on specialized microclimatic observations (van Hove et al.
2015), observations at regular weather stations (Urban
and Kyselý2014), the results of high-resolution numerical
modeling (Fröhlich and Matzarakis 2020), and other
types of meteorological data. However, the assessment
of the bioclimatic conditions on such large spatial scales
as the area of Russia and on such long temporal scales as
decades remains exacting tasks. The network of regular
weather stations is too sparse for such a task, especially
in remote northern and eastern Russian regions, while
high-resolution regional simulations for such a large
area would require unprecedentedly large computa-
tional resources. The rapid development of gridded
meteorological datasets opens up new cost-effective
opportunities for such studies.
The novelty of our study lies in the use of gridded
meteorological data, namely, the ERA-Interim atmo-
spheric reanalysis for the assessment of the thermal
comfort conditions and trends in their changes on a
national scale. The global reanalysis products are pro-
duced via data assimilation—a process that relies on
both observations and model-based numerical forecasts
(Parker 2016). The numerical models of the atmosphere,
ocean, and other Earth system components provide
continuous and physically consistent fields of atmo-
spheric variables on a regular grid, while the data as-
similation systems involve observations from a variety
of sources to make the model as realistic as possible
(Kalnay et al. 1996;Parker 2016). The main advantage of
reanalysis products is continuous spatial and temporal
coverage over the entire globe for various variables, many
of which are practically inaccessible from observational
data. They provide comprehensive snapshots of conditions
at regular intervals over decades (Parker 2016).
Global and regional reanalysis products have been
rapidly developed and improved since the release of the
first global NCEP–NCAR reanalysis (Kalnay et al.
1996). Currently, atmospheric reanalysis products are
among the most commonly used datasets in weather and
climatic studies (Parker 2016). They have been used to
study atmospheric dynamics (e.g., Kidston et al. 2010),
to investigate climate variability (Ivanov et al. 2019;
Semenov and Latif 2015), to evaluate global climate
models (e.g., Gleckler et al. 2008) and to supply high-
resolution regional climate simulations in initial and
boundary conditions (e.g., Varentsov et al. 2018) as well
as for ecological applications (Mislan and Wethey 2011).
The first pioneering studies have already shown the high
potential of using reanalysis data for bioclimatic as-
sessments (Jacobs et al. 2013;Di Napoli et al. 2018).
a. Research data
The study used the ERA-Interim reanalysis (Dee et al.
2011), which was conducted by the European Centre for
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Medium-Range Weather Forecasts (ECMFW) and is
freely available from the ECMWF data portal (https://
apps.ecmwf.int/datasets/data/interim-full-daily/levtype5sfc/).
It is still not perfect for high-latitude regions but provides
the best verification results for wind speed, near-surface
temperature, and radiative fluxes in comparison to six
other global reanalysis products, as is shown in a detailed
evaluation study (Lindsay et al. 2014). The spatial
resolution of the ERA-Interim reanalysis data is 0.758
latitude 30.758longitude; the temporal resolution is
3 h. The data processing and analysis were performed
for territory of the Russian Federation, which was
limited to 208E–1708W, 408–808N. The study used the
data collected in the period from 1 January 1981, to
31 December 2010 (30 years), which corresponds to
the current climatological standard normal period ac-
cording to the World Meteorological Organization
(WMO) for the evaluation of the contemporary thermal
comfort conditions, and the data for a longer period
from 1979 to 2018 (40 years) for the trend assessment.
Recently, the ERA-Interim reanalysis was replaced
by the new ERA5 reanalysis in the chain of ECMWF
products (Hersbach et al. 2019). ERA5 data have higher
spatial (0.258) and temporal (1 h) resolutions and likely
lower biases. However, as far as the authors are aware,
the quality of ERA5 reanalysis has not yet been evalu-
ated for northern Eurasia and specifically for Russia.
Additionally, the processing of ERA5 data with hourly
resolution on the decadal time scale for such a large area
becomes a challenging and computationally demanding
task because the data volumes is 12 times higher than
that of the ERA-Interim reanalysis. For these reasons,
the present pioneering study was limited by the ERA-
Interim data, while the migration to ERA5 is planned
for further research.
b. Bioclimatic indices
The study used the two most valuable bioclimatic
indices based on the human energy balance—PET
and UTCI—to evaluate both heat and cold stress. The
PET is defined as the equivalent of the air temperature
in a standardized indoor setting and for a standardized
person, reproducing the core and skin temperatures
that are observed under current meteorological con-
ditions (Matzarakis et al. 2010). The UTCI is based
on the advanced multinode dynamic model of human
thermal physiology and comfort (Fiala et al. 2012), and
it is combined with a clothing model (Havenith et al.
2012). The outcome is expressed as a temperature
equivalent to that in the reference conditions, imply-
ing the same physiological response as under the
conditions to be assessed (Jendritzky et al. 2012).
The basic difference between the UTCI and PET lies
in the fact that UTCI was validated for all climates and
seasons, while PET application generally has been lim-
ited to western-central European, Mediterranean, and
East Asian climatic conditions (Coccolo et al. 2016).
In general, there is good agreement and a relation-
ship between the PET and UTCI for warm conditions
(Potchter et al. 2018). The UTCI and PET are equally
suitable for hot conditions, whereas UTCI is better for
warm and humid environments. A study in Guangzhou
showed that the linear relationship between the UTCI
and relative humidity was more evident and significant
(Fang et al. 2018). For cold conditions, the UTCI pro-
vides more details about cold stress because of adjusted
clothing insulation (Matzarakis et al. 2014). In further
analysis, the PET was used to assess heat stress, whereas
the UTCI was used to assess cold stress.
The Munich Energy-Balance Model for Individuals
and the Fiala multinode model provided the basis for the
PET and UTCI calculations, respectively. Using the
unique software Rayman Pro 3.1 (VDI 1998;Matzarakis
et al. 2007), it is possible to calculate radiation fluxes and
bioclimatic indices at a specific point in time and place
for individual anthropometric characteristics (age, gen-
der, weight, etc.).
The reanalysis data were used to assess the following
atmospheric variables, which are required for further
calculation of thermal comfort indices: the 2 m air tem-
perature and 2 m dewpoint temperature, wind speed at a
height of 10 m, total cloud-cover fraction, and surface
temperature. The preliminary processing of the re-
analysis data included calculation of the relative hu-
midity from the dewpoint using the Magnus equation,
unit conversation for the cloud fraction, and moving the
wind from a height of 10 m to a height of 1.1 m using the
logarithmic wind profile (Oke 1987) and fixed roughness
length parameter z
0
50.01 m, which corresponded to a
short-cut meadow and is the default for UTCI calculation
(Havenith et al. 2012). The radiative fluxes were parame-
terized by the built-in equations in the Rayman model us-
ing the information on location, elevation, time, and total
cloud-cover fraction (Matzarakis et al. 2010).
The thermophysiological parameters were set up as
follows: male, 35 years old, 1.75 m tall, with a weight of
75 kg, an internal heat production of 80 W, and a heat
transfer resistance of clothing of 0.9 clo.
c. Statistical analysis
The developed technology for the PET and UTCI cal-
culations on gridded reanalysis data includes MATLAB
software and a conceptual framework for its consistent
application from downloading the source data from
the ECMWF website to final visualization of biocli-
matic values. The technology provides cartographic
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visualization directly in MATLAB and translates the
results into the GeoTIFF raster format for further pro-
cessing in GIS software.
The output data included the PET and UTCI values
for each grid cell and each time moment within the
considered period (with a 3-h resolution) as well as a set
of statistical parameters on their contemporary clima-
tology. These parameters included the long-term means
as well as the repeatability of the different thermal stress
categories according to Table 1 for the different months
and seasons.
To investigate the contemporary changes in the ther-
mal stress conditions, we performed a linear trend anal-
ysis of the seasonal means of the PET and UTCI indices
as well as for the repeatability of the different thermal
stress categories for the period from 1979 to 2018. We
used the Mann–Kendall nonparametric statistical test to
evaluate the significance level of the trends and the Sen’s
slope estimator (Helsel and Hirsch 1992)toevaluatethe
rates of changes. These statistical methods can be applied
even if the time series do not conform to a normal dis-
tribution (Helsel and Hirsch 1992;Meals et al. 2011);
therefore, they have been widely used in recent climate
and environmental studies (a detailed review is pre-
sented, e.g., in Kocsis et al. 2017). Trend analysis was
performed in MATLAB software using external libraries
(Fatichi 2020;Tilgenkamp 2020). For each reanalysis
grid cell and each analyzed parameter, we obtained the
Sen’s slope coefficients kand minimum confidence
levels p, at which the trends would be statistically sig-
nificant according to the Mann–Kendall test.
3. Results and discussion
In this section, the maps of the most important bio-
climatic parameters and their changes are presented and
discussed for two contrasting seasons—summer (June,
July, and August) and winter (December, January, and
February).
a. Spatial patterns of air temperature and long-term
mean PET and UTCI for modern climates
For clarity, we compared the bioclimatic parameters with
air temperature, which was the simplest and most under-
standable indicator of climatic conditions. Considering the
average winter (Fig. 1a)andsummer(Fig. 1b) temperature
distributions over the territory of Russia, it should be noted
that the thermal differences in winter were more pro-
nounced than those in summer. The winter temperature
magnitude exceeded 408C. It varied greatly from the
coldest regions in eastern Siberia to positive tempera-
tures on the Black Sea and Caspian Sea coasts (up to
08–58C). The western part of Russia (Kaliningrad re-
gion, which is located between Poland and Lithuania)
was slightly colder than southern Russia because of the
Baltic maritime climate. Here, the winter mean temper-
ature was approximately 08C. In summer, the temperature
was more dependent on latitude, with the exception of in
mountainous areas. The greatest difference was observed
between the subtropics in the south (above 258C) and the
Arctic coast (as low as 22.58C), especially on the islands of
the Severnaya Zemlya Archipelago, which separate the
Kara Sea and the Laptev Sea.
The areas of extremes of average temperatures in
the warmest and coldest months did not always coin-
cide with the areas with extreme comfort indices
(Fig. 1). For example, the winter temperature mini-
mums were concentrated in eastern Yakutia, where
the mean winter temperature according to reanalysis
data reached 2408C and where the lowest air tempera-
ture in the Northern Hemisphere had been recorded.
Two sites in this region are known as the northern ‘‘Pole
of Cold’’—Verkhoyansk (267.68C in 1892) and Oymyakon
(267.78C in 1933) (Stepanova 1958;Kobysheva 2001;
TABLE 1. Thermal stress categories according to the PET and UTCI (Blazejczyk et al. 2012;Matzarakis et al. 2014) and minimum,
maximum, and mean repeatabilities of days with a heat/cold stress of a given category or stronger over all of Russia. The repeatabilities
were calculated for the 1981–2010 period for summer (June–August) for the heat stress categories and for winter (December–February)
for the cold stress categories.
Thermal stress category PET (8C) UTCI (8C) Min repeatability (%) Max repeatability (%) Mean repeatability (%)
Cold stress
Extreme cold stress ,14,240 0 99 49.2
Very high cold stress From 240 to 227 0 100 86.1
High cold stress 14–18 From 227 to 213 41 100 98.9
Moderate cold stress 18–118 From 213 to 0 93 100 99.9
Heat stress
Moderate heat stress 123–129 126–132 0 98 28.7
High heat stress 129–135 132–138 0 88 11.5
Very high heat stress 135–141 138–146 0 63 2.5
Extreme heat stress .141 .146 0 25 0.3
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Darack 2013). However, the minimum values of the mean
winter UTCI were found on the northern coast of Siberia,
where the local extrema fell below 2508C. Such a mismatch
of minima in the fields of mean winter UTCI and temper-
ature could be caused by strong and persistent wind at the
Arctic coast and, conversely, the typically calm weather in
the center of the Siberian high (Yakutia).
The spatial distributions of the mean summer air
temperature (Fig. 1b) and PET (Fig. 1d) were very
similar: the maximum (up to 258C) PET were observed
near the Caspian Sea coast, and the minimum (as low
as 22.58C) PET were observed on the Arctic coast.
Thus, the long-term means of PET and UTCI dem-
onstrated significant differences within the Russian
territory. In summer, such differences were precondi-
tioned first by latitude, whereas in winter, they were
shaped first by the contrasts between maritime and
continental climates (Kobysheva 2001).
b. Spatial patterns of cold and heat stress frequency
for modern climate
The bioclimatic conditions could be characterized not
only by the mean values of thermal indices but also by
the frequency of the different categories of heat or cold
stress (Table 1).Duetothelargeareaofthecountry,
the Russian climate was represented by all categories
of heat stress in summer and cold stress in winter. In
summer, days with at least moderate heat stress
(PET .238C) or even with at least high heat stress
(PET .278C) were not rare, with a country-mean
repeatability of 28.7% and 11.5%, respectively. The
mean repeatability of the days with at least very high
heat stress (PET .358C) and the days with extreme
heat stress (PET .418C) was low, at only 2.5% and
0.3%, respectively. In winter, permanent cold stress
wastypicalforalmostthewholeareaofRussia:the
country-mean repeatability of the days with at least
high cold stress (UTCI ,2138C) was almost 100%.
Stronger cold stress was also frequent: the country-
mean repeatability of the days with at least very high
cold stress (UTCI ,2278C) was 86%, and the country-
mean repeatability of the days with extreme cold
stress (UTCI ,2408C) was 49%. Of course, the
country-mean values were not very informative for a
country such as Russia. We performed a more de-
tailed spatial analysis for those thermal stress cate-
gories, which had significant country-mean repeatability
and pronounced variations in repeatability within the
FIG. 1. Spatial distribution of mean (a) winter and (b) summer air temperatures for Russia, as well as the (c) mean winter UTCI and
(d) mean summer PET, for 1981–2010.
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country, namely, the high heat stress for summer and the
very high cold stress for winter.
The spatial distribution of frequency of the days with
heat/cold stress was not similar to the spatial patterns
of the mean PET and UTCI values (Fig. 2). The wide
area, including most of Siberia and the far east of
Russia, was inhomogeneous in terms of the mean
winter UTCI, but it was characterized by a high (almost
100%) repeatability of days with very high cold stress,
with UTCI ,2278C. Within the whole Far East region,
the repeatability of such days was noticeably less than
100% only in the south of the Kamchatka Peninsula
and in the southeastern part of the region (close to the
border with North Korea). In summary, the repeat-
ability of very high cold stress was more than 95%
during the winter in approximately 50% of Russian
territory, mainly in the east of the country. Relatively
‘‘hot’’ areas were only observed in the Kamchatka
Peninsula and extreme south of the Primorskiy Kray
territory (close to the border with North Korea). In the
European part of Russia, high cold stress repeatability
(more than 95%) was observed only in the Arctic
(Novaya Zemlya islands and Barents Sea coast). The
major part of the east European plain was character-
ized by high cold stress repeatability, which ranged
from 45% to 65%. In the southern Russia (Caucasus,
coasts of Black and Caspian Seas only), the repeat-
ability was lower than 5%.
The spatial distribution of the days with high heat
stress frequency (PET .129) for summer, in general,
followed the same pattern as the mean temperature
and PET values (Fig. 2b). The highest frequency of
heat stress was observed in southern Russia, between
the Black and Caspian Seas, reaching a maximum of up
to 80% near the coast of the Caspian Sea and the
Kazakhstan border. In the rest of Russia, the repeatability
ofthesummerdayswithhighheatstresswaslowerthan
40% and further decreased to zero toward the Arctic coast
and northeastern Pacific coast. The frequency of such days
was almost zero and did not exceed 1%–2% within wide
areas in northern Russia, almost anywhere north of 648N,
and in northeastern Russia, including the Kamchatka
Peninsula.
c. Contemporary changes and trends
Recent global climate change is associated with tem-
perature growth. Hence, we compared the change rates
of the thermal indices and thermal stress repeatability
with the temperature change rates. Linear trend analysis
clearly showed the pronounced heterogeneity of the
rates of the long-term changes of the mean temperature,
PET and UTCI values (Fig. 3), and the thermal stress
repeatability (Fig. 4) against the background of the
general warming trend. For both summer and winter
seasons, the spatial patterns of the linear trend slopes
for the temperature and PET/UTCI indices were gen-
erally similar, which indicated the leading role of the
temperature changes in the changes in the indices.
However, the rates and significance of the changes in
temperature and thermal stress indices could differ.
For example, winter warming in northwestern Russia
was more strongly expressed in terms of UTCI than in
terms of temperature, while the rates of mean summer
PET growth in southwestern Russia were higher than
the rates of mean temperature growth. If we considered
the anomaly of PET and UTCI trends with respect
to the temperature trends (Figs. 3e,f), we could see
that mean summer PET is increasing slightly faster
than mean temperature almost everywhere except in
western Siberia, while mean winter UTCI was increas-
ing faster than mean temperature in the European part
of Russia and in western Siberia. Such amplified rates
of changes in UTCI and PET indicated the addi-
tional contribution from the long-term changes in other
FIG. 2. Spatial distribution of (a) repeatability of days with cold stress during the winter (UTCI ,2278C; index categories ‘‘very high
cold stress’’ and stronger) and (b) repeatability of days with strong heat stress during the summer (PET .1298C; index categories ‘‘high
heat stress’’ and stronger) for 1981–2010.
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meteorological factors, which were further analyzed in
the next subsection.
The heterogeneity of the change rates of the thermal
stress conditions was especially obvious in winter. The
highest warming rates in terms of mean temperature
and UTCI, up to 28C decade
21
, were registered in
northern Russia near the Arctic coast (Fig. 4). Such a
pattern as consistent with an overall intensive warming
in the Arctic, which is known as one of the areas with the
most intensive regional warming rates in the world
(IPCC 2013;Serreze et al. 2009). However, wintertime
warming in northern and northwestern Russia is ac-
companied by near-zero changes in the wide areas of
Siberia and in the far east of Russia and even by negative
FIG. 3. Spatial distribution of Sen’s slope coefficients of linear temperature trends for (a) winter and (b) summer, (c) UTCI for winter,
(d) PET for summer, (e) trend anomaly defined as the difference between the UTCI trend slope and the temperature trend slope for
winter, and (f) PET trend slope and temperature trend slope for summer for 1979–2018. Stippling in (a)–(d) indicates grid cells for which
the trend was significant at p,0.05 according to the Mann–Kendall test.
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trends in the mean temperature and UTCI in southern
Siberia. The latter could be explained by the atmo-
spheric response to the strongly reduced Arctic sea ice
cover, which was expressed as an anticyclonic surface
pressure anomaly and negative air temperature anomaly
in southern Siberia (Semenov and Latif 2015).
The spatial patterns of the mean temperature and
UTCI change rates were only weakly correlated with the
changes in winter cold stress repeatability (Fig. 4a).
The rapid warming and increase in the mean UTCI in
the Arctic were not expressed in terms of cold stress
repeatability because the area is still too cold and windy.
The same situation takes place in southern Siberia,
where significant cooling trends are not expressed in
terms of the cold stress repeatability. A significant de-
crease in cold stress repeatability was observed only in
western Russia, while in most of Siberia and the whole
far east of Russia, hardly any changes were observed.
This means that the winter thermal stress conditions
became softer in those regions where they were already
relatively mild, while the regions with the most severe
conditions did not exhibit significant changes in cold
stress repeatability.
For the summer season, the spatial patterns of the
change rates of the mean temperature and PET were
more homogeneous in comparison to those in winter. In
almost all territories of Russia, we found steady warm-
ing trends in terms of PET and temperature, with the
only exception of western Siberia, where near-zero
changes were observed. The latter was consistent with
previous studies on climate change in Russia (e.g.,
Ippolitov et al. 2014). The hot spots of summer warming
were southern Siberia as well as the western and
southwestern regions of Russia, where the warming
rates exceeded 18C decade
21
. The rates of changes in
the heat stress repeatability have more heterogeneous
spatial patterns (Fig. 4b), which were only partially
correlated with the changes in the mean temperature
and PET. The heat stress danger was rising in western
Russia, with a hot spot near the western border of the
country, and in the southern parts of Siberia and the Far
East. The rest of the country, including its northwestern
regions, western Siberia, the northern parts of eastern
Siberia and the Far East, exhibited near-zero changes in
heat stress repeatability. The latter may be due to the
absence of warming trends in some regions (e.g., in
western Siberia) as well due to the cold climate of other
regions (e.g., Arctic coast), where the days with a high
thermal stress were not registered in principle, despite
the overall warming trend.
d. Trends of the driving meteorological parameters
To investigate the driving factors responsible for
faster or slower change rates of the thermal indices in
comparison with air temperature change rates, we ana-
lyzed the trends for other meteorological variables used
to calculate the thermal indices—namely, the wind
speed, total cloud cover, and relative humidity (Fig. 5).
In winter, the increased rates of the UTCI were regis-
tered in the European part of Russia and western
Siberia; they were caused by the decreasing wind speed
in these regions, which was shown by reanalysis data as
well as by regional climate change studies, which were
reviewed in Wu et al. (2018). The mean winter cloudi-
ness and humidity were not affected by significant
trends. In summer, the areas with amplified rates of PET
growth—the European part of Russia and southern Siberia
(see Fig. 3f)—were characterized by significant negative
trends in total cloud cover, which meant an increase in solar
radiation and explained an additional increase in the PET
index. A downward trend in the summer cloudiness in the
European part of Russia was consistent with the results of
FIG. 4. Spatial distribution of Sen’s slope coefficients of linear trends (a) for the repeatability of days with cold stress (UTCI ,2278C;
index categories very high cold stress and stronger) for the winter season and (b) for the repeatability of days with strong heat stress
(PET .298C; index categories high heat stress or stronger) for the summer season for 1979–2018. Stippling indicates grid cells for which
the trend was significant at p,0.05 according to the Mann–Kendall test.
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(Chernokulsky et al. 2011) based on the cloudiness obser-
vations at weather stations, which also showed a decrease
in overcast frequency. The high-quality observations at the
meteorological observatory of Lomonosov Moscow State
University also showed a downward trend in cloudiness and
overcast frequency and an upward trend in sunshine fre-
quency (Gorbarenko 2019). The areas with downward
trends in cloudiness were also characterized by negative
trends in relative humidity, which should partially com-
pensate for the effect of decreasing cloudiness according to
the PET index. However, the contribution from decreasing
humidity seemed to be smaller than the contribution from
decreasing cloudiness, which is consistent with the small
sensitivity of the PET index to humidity (Fang et al. 2018).
e. Current trends in the global context
In the present study, we evaluated Russian thermal
comfort conditions for the first time using state-of-the-art
thermal indices based on convenient gridded me-
teorological data with a 3-h temporal resolution. The
FIG. 5. Spatial distribution of wind speed trends for (a) winter and (b) summer, cloudiness trends for (c) winter and (d) summer, and
relative humidity trends for (e) winter and (f) summer for 1979–2018. Stippling indicates grid cells for whichthe trend was significant at p,
0.05 according to the Mann–Kendall test.
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novelty of this approach lies in the possibility of
assessing not only the average values of thermal in-
dices but also the repeatability of various gradations
of thermal stress. Such studies have not ever been
performed for any part of Russia in principle, which
complicates the comparison of the results with those
of other studies. We could only postulate that our
results confirmed the boundaries of territories with
maximum cold stress in winter according to a previous
study (Bauche et al. 2013;Vinogradova 2019)and
compared our results for Russia with results for other
regions.
The marked decreasing trend of cold stress in the
European part of Russia clearly corresponds to the same
cold extremes tendency in the Euro-Mediterranean re-
gion (Giannaros et al. 2018) and in northern Serbia
(Basarin et al. 2018), as well as the upward trend in heat
stress repeatability. An assessment of bioclimatic con-
ditions in western Poland (the Lubuskie Voivodeshi)
conducted from 1971 to 2006 (Ma˛ kosza 2013) showed a
negative trend in days with categories related to UTCI
cold stress. In Crete, the annual number of days with
mean daily extreme values of PET/UTCI tended to
neither increase nor to decrease during the 30-yr period
from 1975 to 2004 (Bleta et al. 2014). An increase in heat
stress during summer in the main part of eastern and
southern Siberia could be compared to a similar warm-
ing trend in China (Wu et al. 2017). The tendency to-
ward increasing heat stress in the territory near the
Caspian Sea corresponded to the results of an Iranian
bioclimatic condition assessment (Roshan et al. 2018).
We identified the tendency that the mean summer
PET was increasing faster than the mean temperature
almost everywhere in Russia. This corresponded to
the tendency for heat stress that was revealed across
Australia using high-resolution ERA-Interim reanalysis
data over the period from 1979 to 2010 (Jacobs et al.
2013). Generally, the apparent temperature rose faster
than the air temperature, amplifying the expected ex-
posure to discomfort due to global warming in the
subtropical region.
4. Conclusions
In this study, we evaluated the thermal comfort con-
ditions in Russia based on PET and UTCI biometeo-
rological indices and gridded meteorological data from
the ERA-Interim reanalysis. The climatological means
of the thermal indices and repeatability of the different
grades of cold and heat stress were evaluated for the
WMO’s current climatological standard normal period
(1981–2010); the long-term changes in these parameters
were analyzed for the 1979–2018 period.
By analyzing the spatial patterns of the mean PET,
UTCI, and temperature fields, we found the mismatch of
the winter extreme in terms of temperature and UTCI.
While the lowest winter temperatures were found in
Yakutia, the lowest winter UTCI values were found on
the coast of the Arctic Ocean, where low temperatures
were combined with high wind speeds. In summer, the
fields of the mean temperature and PET were almost
similar.
For the first time, we evaluated the repeatability of the
different grades of cold and heat stress in Russia. We
found that in winter, nearly the whole area of Russia was
permanently subject to at least high cold stress accord-
ing to the UTCI index, and more than 50% of the ter-
ritory was permanently under at least very high cold
stress, and even extreme cold stress was quite frequent,
with a country-mean repeatability of 46%. In summer,
days with at least high heat stress according to the PET
index were quite frequent south of 608N, with the
probability of such days reaching 80% in the south of
Russia. However, the Arctic and northern regions of
Russia were almost invulnerable to high heat stress. We
registered very few days with stronger heat stress, with a
country-mean repeatability not higher than 3%.
Analysis of the summer and winter trends of the two
different thermal comfort indices and air temperature
showed spatial inhomogeneity of their changes. In
summer, warming trends were observed all over the
country except for western Siberia, with hot spots in its
southwestern parts and in southern Siberia. In winter,
warming was observed in the northwestern part of the
country in the Arctic. The warming trends in terms of
thermal indices generally followed the temperature
trends; however, they could have different slopes and
significance due to the contributions of other meteo-
rological factors. In winter, decreasing wind speed led
to an increase in the mean UTCI in the European part
of Russia and in western Siberia, while the summer
decrease in cloud cover led to an increase in PET in the
European part of Russia and southern Siberia.
The changes in the mean thermal indices did not al-
ways correspond to the changes in heat or cold stress
repeatability. For example, the intense wintertime
warming in the Arctic was not reflected in changes in
the cold stress repeatability. The most pronounced
changes in the very high cold stress repeatability were
observed in the European part of Russia, especially in
the northwest of the country, while the most pro-
nounced changes in the summertime high heat stress
repeatability were observed in the European part of
Russia and in the southern parts of Siberia and Far
East. In summary, we can conclude that the European
part of Russia is most susceptible to changes in bioclimatic
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conditions since it experiences both changes in the winter
frequency of cold stress and changes in the summer fre-
quency of heat stress.
From a methodological point of view, an important
conclusion of the study was the difference between the
spatial patterns of contemporary climate conditions and
their changes in terms of mean temperature, mean values
of bioclimatic indices and the repeatability of heat or cold
stresses. Such results clearly illustrate the importance of
selecting appropriate indicators for applied tasks related
to thermal stress assessment and analysis.
Acknowledgments. This research was funded by
the Russian Science Foundation (Grant 17-77-20070
‘‘Assessment and Forecast of the Bioclimatic Comfort
of Russian Cities under Climate Change in the 21st
Century’’).
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... UTCI better represents the temporal variability of thermal conditions than other indices . Many previous studies have also found this index useful in assessing human thermal discomfort/heat stress across the globe (Di Napoli et al. 2018Napoli et al. , 2019Varentsov et al. 2020;Urban et al. 2021;Ullah et al. 2022;Shukla et al. 2022). ...
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We have attempted to investigate the spatiotemporal patterns and Cuctuations in summer thermal heat stress/discomfort over the Indian region-based universal thermal climate index (UTCI) in this study. We have calculated UTCI using hourly ERA5 data of 10 m wind speed, 2 m air temperature, 2 m dew point temperature, and solar radiation for the period 1990-2020. To determine the eAect of radiation Cuxes and soil moisture on temperature and UTCI, we have used ERA5 data on cloud fraction (CF), surface heat Cuxes (SLHF and SSHF), and soil moisture (SM) for the study period. Maximum heating and discomfort have been reported in May for most of the regions. Except for the west region, the progress of the monsoon provides some relief in June. Maximum discomfort is observed around 08-09Z. We have observed over 50% of India experiencing a decreasing trend in UTCI in different summer months despite over 50% of India experiencing an increasing trend in temperature. This is due to the inCuence of factors such as solar radiation, cloudiness, wind speed, soil moisture, etc., on UTCI. The UTCI in summer months demonstrates spatial heterogeneity. UTCI increases significantly in the west region in April and the east region in June. In April and May, some portions of the South-Central region, particularly Maharashtra, exhibit an increasing trend in UTCI. The majority of the North-Central region has a noticeable decreasing tendency in UTCI in all the summer months. We have not found any significant trend in the frequency of days with 'very high heat stress' or higher discomfort. Except in the eastern region, there is no noticeable trend in the frequency of discomfort hours with UTCI in the ranges 38 \ UTCI \ 46°C and UTCI [ 46°C. The Eastern region exhibits an increasing trend in the frequency of discomfort hours with UTCI in the range of 38 \ UTCI \ 46°C in April. The Eastern region has a rising trend in the frequency of discomfort hours, with UTCI in the range of 38 \ UTCI \ 46°C in April.
... Aiming at addressing the above issues, especially in regions characterized by limited spatial coverage of surface observational networks, recent studies exploited climate retrospective analysis (reanalysis) datasets [13][14][15]23,[29][30][31][32][33][34][35][36] . The wealth of data provided by reanalyses allows for retrieving simple metrics related to the thermal environment or even computing rational indices, such as the physiological equivalent temperature (PET) 37,38 and universal thermal climate index (UTCI) 39,40 . ...
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Existing assessments of the thermal-related impact of the environment on humans are often limited by the use of data that are not representative of the population exposure and/or not consider a human centred approach. Here, we combine high resolution regional retrospective analysis (reanalysis), population data and human energy balance modelling, in order to produce a human thermal bioclimate dataset capable of addressing the above limitations. The dataset consists of hourly, population-weighted values of an advanced human-biometeorological index, namely the modified physiologically equivalent temperature (mPET), at fine-scale administrative level and for 10 different population groups. It also includes the main environmental drivers of mPET at the same spatiotemporal resolution, covering the period from 1991 to 2020. The study area is Greece, but the provided code allows for the ease replication of the dataset in countries included in the domains of the climate reanalysis and population data, which focus over Europe. Thus, the presented data and code can be exploited for human-biometeorological and environmental epidemiological studies in the European continent.
... Reanalysis datasets provide a comprehensive description of the observed past climate and have been widely used for various geophysical applications [1]. In recent years, their exploitation for human-biometeorological and heat-related epidemiological studies has also emerged [2,3]. Such applications are motivated by the fact that networks of surface weather stations may be characterized by limited spatial coverage, especially with respect to epidemiological information. ...
Article
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In recent years, a considerable body of research has demonstrated the suitability of global and regional reanalysis data for human-biometeorological applications. These applications include the assessment of the outdoor thermal environment and the investigation of its relation to human health, especially in areas where the spatial coverage of surface observational networks is sparse. Here, we present the first comprehensive evaluation of the most recent pan-European regional reanalysis, namely the Copernicus European Regional Reanalysis (CERRA) dataset at 5.5 km spatial resolution, in terms of simulating the observed human bioclimate, as expressed by the modified physiologically equivalent temperature (mPET) that is computed through the RayMan Pro model, and its meteorological drivers. The validation was performed over Greece using up to 11 years of records of 2 m air temperature and relative humidity, 10 m wind speed and global solar radiation derived from 35 sites of the nationwide network of surface weather stations operated by the METEO Unit at the National Observatory of Athens. The ERA5-Land dataset at~9 km spatial resolution, which represents the current state-of-the-art reanalysis, was also compared against the same observations. Our findings show that the CERRA dataset performs significantly better compared to the ERA5-Land reanalysis with respect to the replication of the examined meteorological variables and mPET. The added value of the CERRA data is particularly evident during the warm period of the year and in regions that are characterized by complex topography and/or proximity to the coastline. Combining the CERRA dataset with population and mortality data, we further showcase its applicability for human-biometeorological and heat-health studies at a local scale, using the regional unit of Rethymno (Crete) as a pilot area for the analysis.
... There are other models and options available for these purposes, such as the RayMan model, which was created for applied climatology research in urban climates [13]. "Even though several indices have been created, only four of them [PET, PMV, universal thermal comfort index (UTCI), and standard effective temperature (SET)] are extensively utilized for outdoor thermal perception research" [15]. The present study sought to assess the behaviour of four bioclimate indices (SET, UTCI, PET, PMV) in Baghdad city using RayMan model. ...
Article
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The evaluation of bioclimatic conditions on a local scale is still an important topic. It might be a key component of the national plan for the sustainable development of various areas under changing climatic conditions. The present study aimed to examine the behaviour of four bioclimatic indices namely physiologically equivalent temperature (PET), Standard Effective Temperature (SET*), Universal thermal climate index(UTCI) and, Predictive mean vote(Pmv) derived from energy budget models for Baghdad city. The monthly means data of air temperature, relative humidity, wind speed, and solar radiation during the period (1981-2021) were used in this study. All calculations of this study were extracted using RayMan Model. The relationship between PET, Pmv, SET, and UTCI indices in addition to meteorological parameters was also conducted in this study. The results indicate that July and August had the highest values of Pmv (4), SET* (39.4), PET (43.9), and UTCI (39.4). For the majority of the studied indices, the comfortable thermal perception occurred only in March, April, and October. PET and UTCI revealed acceptable comparability in terms of temperature perception when compared to other indices for most months of the year. The maximum heat stress was reported for all indices between May and September, with the lowest stress occurring between January and December. April was the most comfortable month of the year, according to the data. Correlations between UTCI and meteorological parameters found a substantial association with air temperature (0.99) and relative humidity (0.98), respectively. The lowest correlation coefficient (R2=0.69) was found with wind speed. All other indicators indicate a substantial association with the UTCI.
... These alterations in parameters affect also OHTCCs since it is the combined effect of mentioned parameters on human body. It is important to gain insights about the size and trend of mid-and long-term differences in OHTCCs, which are calculated using seasonal, daily and hourly meteorological values for a city by comparing it with its close proximity or rural to determine whether there is a need to take physical, social and economic measures by preparing a strategic road map for the mitigation of the unfavourable conditions (Endler & Matzarakis, 2011;Grigorieva & Matzarakis, 2011;Li et al., 2022;Varentsov et al., 2020;Zölch et al., 2019). ...
Article
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Diyarbakır city, in the torrid southeast Anatolia region, harbours significant socioeconomic potentials and a population of over 2 million. Climate factors affect human activities in the city due to heat stress in summer which increases with climate change. Therefore, it is important to analyse outdoor human thermal comfort conditions (OHTCCs) to take required actions for the sustainability and improvement of socioeconomic life. This paper evaluates OHTCCs for the city over a 10-year period for rural and semi-urban sites and 5-year period for urban site. Physiological Equivalent Temperature (PET) and RayMan, a bioclimate model, were chosen to determine OHTCCs from meteorological parameters. Results indicate that the frequency of heat stress is higher in urban (32.0%) than that in rural (25.6%) and in semi-urban (23.4%) sites while that of comfortable conditions is highest in urban (17.2%), followed by semi-urban (17.1%) and rural (15.8%) sites. Percentage of the cold stress is lowest (13.6%) in urban, followed by rural (21.4%) and semi-urban (22.4%) sites. High variability of summer heat stress over the sites can be explained by surface characteristics and local air circulation patterns. Semi-urban site provides some advantages for experiencing less heat stress since it has the mixed surface characteristics with vegetated and structured zones which can supply moisture to the environment to moderate OHTCCs. It is suggested that authorities should consider the urban planning and implementation actions to improve physical environment and human quality of life and to ensure the sustainability of economic activities.
... But in the scenario of global warming and climate change, temperature is rising and it will result in a rise in thermal discomfort. Many past studies also tried to Bnd the thermal discomfort/heat stress in different parts of the world and found similar results as above (Luo and Lau 2019;Varentsov et al. 2020;Dasari et al. 2021). ...
Article
Full-text available
In the scenario of global warming and climate change, human thermal discomfort is about to rise. A rise in human thermal discomfort will undermine human health and well-being. It will also undermine labour productivity (as workers have to reduce work intensity and take longer breaks from work to prevent heat stress-related illness and injuries) and boost energy demand (as people will have to use more cooling instruments such as ACs, coolers, fan, etc., to get relief from thermal discomfort). Hence an assessment of spatio-temporal variability of thermal discomfort is necessary to develop a national strategy for the sustainable development of the country under changing climate scenarios. In this study, we have tried to analyze spatio-temporal variations of summertime thermal discomfort in India with the help of the Discomfort Index (DI). To calculate the DI, we have used high resolution (0.25°×0.25°) ERA-5 hourly 2-m air temperature and 2-m dewpoint temperature data. It is seen that March is the month of minimum discomfort and June is the month of maximum discomfort. In June, maximum discomfort occurs in the western region. The east coastal region and western region of India, particularly Rajasthan, experience maximum discomfort in terms of severity and prolonged discomfort hours. We have also calculated trends in DI, RH and temperature over the Indian region for March to June and observed a generally increasing trend with some spatial variations across India. It is also observed that the DI trend is more prominent in the western region in March and April, the southern region in May and the eastern region in June. We have also calculated the diurnal variations of thermal discomfort and the number of days with DI greater than 27°C and 29°C for different regions. It is observed that in most of the regions, DI reaches its peak around 09–10Z. Except for the north region, most of the regions show increasing trends in the number of discomfort days in April, May and June.
... These indices are based on the thermal exchange between the human and surrounding environments or empirical relationships gained by studying human responses to various environmental factors, varying in complexity, applicability, and capacity (Staiger et al., 2019). For example, the heat index (HI) is used for meteorological service (NWS, 2011); wet-bulb temperature (WBT) is used to measure the upper physiological limit of human beings (Raymond et al., 2020); physiologically equivalent temperature (PET) and UTCI are used to estimate human thermal comfort (Varentsov et al., 2020). Therefore, a high-resolution dataset that contains different commonly used human thermal stress indices is urgently called for in global and regional studies, particularly for those with complex climate conditions (e.g., China). ...
Article
Full-text available
Human-perceived thermal comfort (known as human-perceived temperature) measures the combined effects of multiple meteorological factors (e.g., temperature, humidity, and wind speed) and can be aggravated under the influences of global warming and local human activities. With the most rapid urbanization and the largest population, China is being severely threatened by aggravating human thermal stress. However, the variations of thermal stress in China at a fine scale have not been fully understood. This gap is mainly due to the lack of a high-resolution gridded dataset of human thermal indices. Here, we generated the first high spatial resolution (1 km) dataset of monthly human thermal index collection (HiTIC-Monthly) over China during 2003–2020. In this collection, 12 commonly used thermal indices were generated by the Light Gradient Boosting Machine (LightGBM) learning algorithm from multi-source data, including land surface temperature, topography, land cover, population density, and impervious surface fraction. Their accuracies were comprehensively assessed based on the observations at 2419 weather stations across the mainland of China. The results show that our dataset has desirable accuracies, with the mean R2, root mean square error, and mean absolute error of 0.996, 0.693 ∘C, and 0.512 ∘C, respectively, by averaging the 12 indices. Moreover, the data exhibit high agreements with the observations across spatial and temporal dimensions, demonstrating the broad applicability of our dataset. A comparison with two existing datasets also suggests that our high-resolution dataset can describe a more explicit spatial distribution of the thermal information, showing great potentials in fine-scale (e.g., intra-urban) studies. Further investigation reveals that nearly all thermal indices exhibit increasing trends in most parts of China during 2003–2020. The increase is especially significant in North China, Southwest China, the Tibetan Plateau, and parts of Northwest China, during spring and summer. The HiTIC-Monthly dataset is publicly available from Zenodo at 10.5281/zenodo.6895533 (Zhang et al., 2022a).
... In 2009, following 10 years of development, the Universal Thermal Climate Index (UTCI) was presented to the scientific community to address these shortfalls (Błażejczyk, 2021). In the 13 years since its finalization, the UTCI has been applied extensively across the global north (Di Napoli et al., 2018;Mölders, 2019;Varentsov et al., 2020;Antonescu et al., 2021;Krüger, 2021), yet little work has been undertaken to use this index to consider thermal stress in African or southern African contexts Shukla et al., 2022). Since anthropogenically induced climate change has heightened the probability of extreme temperature events (ETEs), and the incidence of thermal stress (Masson-Delmotte et al., 2021), it is important to assess changes in thermal comfort at a regional scale over recent decades, using a comprehensive multifactoral index such as the UTCI. ...
Article
Full-text available
The 6th Assessment of the Intergovernmental Panel on Climate Change projects increasing thermal‐associated morbidity and mortality under anthropogenically induced warming. Over 100 indices exist to quantify thermal stress, and among these, the Universal Thermal Climate Index (UTCI) was developed for regional investigations of thermal stress influences on human health. Although by definition a universal index, current applications are mainly limited to Europe. For regions such as Africa, use of the UTCI has been hampered by a lack of available requisite input variables from ground‐based meteorological stations. To overcome this, a gridded dataset, derived from ERA5 reanalysis, of UTCI equivalent temperatures was developed by the European Centre for Medium‐Range Weather Forecasts. Using this dataset for daily average, minimum and maximum UTCI values, we explore spatiotemporal patterns and changes thereof over annual, seasonal, and monthly scales across southern Africa from 1979 to 2021. Across these scales, 9 of 10 UTCI thermal stress categories were observed, ranging from very strong cold stress to extreme heat stress. Spatially, no thermal stress was most widespread for daily mean values, whereas for daily maximum (minimum) values there was a wider heat (cold) stress incidence, with frequent occurrences of moderate and strong heat stress (slight and moderate cold stress). Interannually, a clear El Niño–Southern Oscillation influence on thermal stress was evident during summer, with El Niño (La Niña) phases extending (reducing) heat stress incidences by up to 13.8% (2.9%). Over the study period, heat stress increased at statistically significant rates in many instances, with the strongest, most widespread increases, for the daily average and maximum (minimum), during spring (summer), averaging 0.28 and 0.29°C·decade⁻¹ (0.23°C·decade⁻¹); few regions experienced statistically significant decreasing trends. Overall, the trend results highlight regions vulnerable to significant thermal climate changes, and thus should be considered in decision‐making regarding outdoor activities.
Chapter
This chapter presents a comprehensive literature review of peer-reviewed papers on the UTCI with special focus on its applications. A search in Scopus and Web of Science has been conducted in February 2021 yielding 320 and 304 documents, respectively, for the time frame from 2000 to March 2021. Results have been classified according to 8 different categories, which roughly define the areas of application of the index: (1) Outdoor Thermal Comfort (OTC) and thermal stress; (2) Urban Climate and Planning studies; (3) Climate-related impacts on human health; (4) Bioclimate; (5) Comparisons with other thermal comfort indices; (6) Meteorological analyses; (7) Climate change research; (8) Tourism. The bulk of research carried out on the UTCI is primarily concentrated on the first two topics, reaching about 60% of papers output. Clusters identified in VOSviewer from co-occurrences of author keywords closely match the main areas of application of the index. Research output shows an intrinsic multidisciplinary nature but it is still concentrated in a few countries. Areas of application such as public weather service and climate-related impacts on the health sector still need to become aware of the potentialities and practicalities of the UTCI.KeywordsUTCILiterature reviewVOSviewerBibliometric analysis
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The paper uses the universal thermal climate index (UTCI) to estimate the bioclimate in Russia, initiated by the Commission of the International society of Biometeorology. The UTCI index can be described as equivalent environment temperature (°C), which provides the same physiological impact on humans as the actual environment. Assessment of bioclimatic conditions is shown for the territory of Russia in the period of modern climate change (2001–2015). Cold stress conditions (from low to extreme) were observed in the almost all territory of Russia for about 8–11 months a year. During the rest of the year, the conditions are neutral or comfortable. The period of extreme and very high cold stress is reduced during the modern climate warming (compared to the period 1961–1990), especially in the Arctic, in the European part of Russia, in Western and Eastern Siberia. At the same time, the period with neutral and comfortable thermal conditions increases.
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