The increase in global mean temperatures resulting from climate change has wide reaching consequences for the earth's ecosystems and other natural systems. Many studies have been devoted to evaluating the distribution and effects of these changes. We go a step further and propose the use of the heat index, a measure of the temperature as perceived by humans, to evaluate global changes. The heat index, which is computed from temperature and relative humidity, is more important than temperature for the health of humans and animals. Even in cases where the heat index does not reach dangerous levels from a health perspective, it has been shown to be an important factor in worker productivity and thus in economic productivity.
We compute the heat index from dew point temperature and absolute temperature 2
The essential cause of climate change is the additional entrapment of thermal energy in the earth's many natural systems through carbon dioxide from anthropogenic sources. The speed at which this is occurring is, on climatological and geological timescales, extremely rapid, often requiring faster adaptation than would be expected under normal circumstances.
This additional heat energy has manifold consequences, many of them indirect.
All of them, in one way or another, affect humans. For example, additional heat
modifies the earth's water household, reducing agricultural yields and in this
way affecting human health and well-being
Many studies have analyzed the effects of climate change on global temperatures
and their distribution in space and time
We present a new data set of globally gridded heat index values computed from
reanalysis results for the years 1979–2013. These values are aggregated on
several temporal and spatial scales. The data are presented in the context of
global climate change and its direct effects on human health. We several
temporal and spatial scales. Furthermore, we describe the effects of climate
change on the global distribution of heat index and investigate these effects
for different countries through the study's time period. The data are available
for further use by the scientific community
High-quality, consistent data measured at the same place over climate-scale time periods are extremely difficult to obtain. For this reason, we use reanalysis data in order to create the data set on heat index.
Reanalysis data are not an equivalent to observation data and should be used
carefully
The ERA-Interim reanalysis by ECMWF is well suited for this task. It uses the
same data assimilation system and dynamic modeling core over a long period of
time – from 1979 extended up until the present. The model used to produce the
reanalysis, the ECMWF's Integrated Forecast System (IFS), uses three fully coupled
components for atmosphere, land surface and ocean waves. This improves accuracy
especially for areas surrounded mostly by ocean. Because the model was used to
produce a reanalysis, which did not have to be published in a time critical
fashion, observations from all over the globe could be assimilated, even if
they were only available after a normal forecast model's cutoff time. These
observations can be quality-controlled before being assimilated into the model.
Using a model rather than, for example, a simpler interpolation approach makes it
possible for the model to propagate information obtained through observations
through variable domains, space and time
The ERA-Interim reanalysis used four assimilation cycles per day, at 00:00, 06:00,
12:00 and 18:00 UTC. The original data were produced on a reduced Gaussian grid with
approximately uniform spacing for surface fields of 79
We use data from the entire available time period of 1979–2013. The data
were
downloaded after interpolation from the Gaussian onto a regular
0.75
Heat index has been computed using a variety of algorithms in different
studies. We chose the currently operational method used by the
The chosen algorithm uses relative humidity and temperature in
Heat index was computed using an algorithm beginning with a simple
approximation:
If HI is
It should be noted that the heat index, which was created for the purpose of
measuring physiological stress due to high heat loads, is not adapted for
measuring stress due to coldness. Also, above a certain level the heat index is
oversaturated, so that no additional information can be gained from it. For
this reason, we rounded extreme heat index values into the range of 40–140
The primary reason that heat index is so relevant in the context of climate change is its direct and indirect effects on human health and the anthropogenic systems connected to it. Thus, we expect that our data on the heat index can and will be used in many further studies, in which they will be connected to other data.
The heat index is calculated for each grid point and for each point in time for which the ERA-Interim reanalysis is available. However, using the calculated heat index in further studies usually implies that data on a daily or even monthly or yearly basis are necessary. Therefore, we aggregated the heat index to daily levels. For each day, the four assimilations were combined in order to produce gridded daily minima, means and maxima. We consider this a good approximation of the nighttime heat index, which represents the daily minimum in most cases; the actual local mean heat index over the course of the day; and the daily midday heat index, which is the maximum in most cases. In addition to producing these daily aggregates, the daily metrics were aggregated to monthly and yearly temporal levels.
In addition to the temporal aggregate, the combination with other data will also make a spatial aggregation necessary in many studies. Other data are often given on a regional or national level. Therefore, we also examine the heat index on the level of countries. For studying the effect on humans and human activities, the heat index in populated areas is especially relevant, as dangerous heat exposure in areas where no people are affected is at most tangentially connected to human well-being.
The Global Rural-Urban Mapping Project (GRUMP)
Because of the large number of changes in administrative boundaries and
population distribution in the years following the dissolution of the Soviet
Union in 1991, the authors of GRUMP were often forced to combine heterogeneous
data sources into their results
Furthermore, for the sake of consistency, we aggregated the population data
into current political boundaries
The following steps were used to aggregate our data to the country level.
First, the heat index data for the areas covered by each country were rasterized
onto the same coordinate system as the GRUMP data. This made it possible to
discretely sum the population inside each country according to the GRUMP
estimates. Per-grid-point population weights were produced by calculating the
proportion of population within that country that contained the grid point in
question, as follows:
The per-country weighted mean heat index was then computed as follows:
Weighted means were produced for each country with available data and each
temporal aggregation level, as outlined in Sect.
As mentioned above, we expect that the heat index as it is calculated here can
and will be used in many future studies. To give some first impression we
discuss the change of the heat index between the time periods 1979 and 1999 and
2000 and 2013. Although neither of these periods represents a typical 30-year
climate period, this was considered a good compromise which placed the bulk of
the data in the 1970–1999 and 2000–2029 climate periods while splitting the
data into temporal chunks of similar lengths. All data visualization is done
using ggplot2
Typical heat index for an exemplary day (2 June 1996). Upper left: maximum heat index; lower left: minimum heat index; upper right: mean heat index; lower right: diel range of heat index.
Figure
Differences between yearly temporal statistics for each reference
period (1979–1999, 2000–2013). The left column shows, from top to bottom,
the differences in maximum, mean and minimum heat index for the entire year
for the entire globe. The right column shows the frequencies of heat index
changes worldwide in number of cells for each aggregate statistic.
Continents are added for orientation
The change between both reference periods is shown in Fig.
Changes between both reference periods (1979–1999, 2000–2013)
in monthly mean heat index. The inset numbers refer to the mean increase in
heat index for the month in question between both reference periods.
Continents are added for orientation
An evaluation of the change in monthly mean heat index across the globe for
both reference periods, as shown in Fig.
One of the most important applications of our data is the evaluation of danger
due to high heat loads. We classified danger due to high heat index according
to the criteria outlined in Table
Heat index danger levels according to
Change in the probability that the maximum heat index will exceed the
threshold for “extreme danger” for a given day in each month in
2000–2013 compared to 1979–1999
For each reference period and each of the classification criteria shown above we
calculate the probability that the peak heat index of each day exceeds the
threshold for extreme danger in each month. Then, we compare the exceedance
likelihood between the two reference periods. The results,
shown in Fig.
Another interesting application of the new data set is the classification of
countries according to their heat index climatologies. We use the
population-weighted heat index minima, means and maxima in each month and apply
an iterative
The clustering is applied to both reference periods: eight clusters are created. This number of clusters matched both reference periods well – more clusters did not seem to produce any substantial gains, whereas fewer clusters would have meant a larger sum of squared distance between points inside individual clusters.
The clusters were examined using ordination plots based on the methods by
Countries and the clusters they were grouped into. The map at the top shows country clusters for the first reference period, and the map in the middle shows county clusters for the second. The map at the bottom indicates whether the cluster that a country was grouped into changed between both periods.
In this paper, we introduce a new data set containing gridded heat index values
2
It is shown that, for the two periods chosen for our study (1979–1999, 2000–2013), the distribution of heat index across the globe has changed. The worldwide mean heat index has risen, both for the entire year and for each month. The likelihood of daytime heat index values that indicate “extreme danger” has also increased across the globe since the 20th century. This analysis is meant as an example usage of these data and could be repeated for different thresholds, with a finer quantile resolution, or focused on more specific geographic areas or time periods in order to obtain more meaningful information.
It is also shown that heat index data can be used for studies on the country level, e.g., for classifying countries into heat index “climate zones”. Such a country-level analysis is only a first example of possible ways of using these data. Examining them on a finer spatiotemporal scale and combining them with additional data could reveal more information and aid in analyzing, understanding and predicting the connection between heat index and various components of human systems.
The data are available for general use
We thank the European Centre for Medium-Range Weather Forecasts for providing the original data. We also extend our thanks to all contributors to the several open source projects which were used in analyzing, manipulating and visualizing our data. Edited by: D. Carlson