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\begin{document}

\title{North-South disparity in impact of climate change on ``outdoor days''}



\author[1]{Yeonwoo Choi}%
\author[1]{Muhammad Khalifa}%
\author[1]{Elfatih A. B. Eltahir}%
\affil[1]{Ralph M. Parsons Laboratory, Massachusetts Institute of Technology}%


\vspace{-1em}



  
  \date{June 10, 2024}


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ABSTRACT

In a recent study, we have introduced the concept of ``outdoor days'' --
the number of days with moderate temperature, neither too cold nor too
hot, allowing most people to enjoy outdoor activities -- to describe how
climate change can affect quality of life for communities in a different
way compared to previous studies which emphasize changes in surface
temperature. Here, we use the same concept to investigate climate risk,
defined by the combination of vulnerability, exposure, and hazard.
Vulnerability and exposure are known to cause a sharp disparity in
climate risk between the wealthy north and the deprived south, implying
disproportionate risks of climate change. However, we know little about
how climate hazards contribute to this disparity of global climate risk.
Here, we present observational and modeling evidence of north-south
disparity in climate risk caused by changes in ``outdoor days''. Under
high-emissions scenarios, CMIP5 and CMIP6 models project fewer outdoor
days for people living in developing countries, primarily located in
low-latitude regions. Meanwhile, developed countries in middle- and
high-latitude regions could gain more outdoor days, redistributed across
seasons. Our findings help inform ongoing debates on compensation for
losses and damages caused by climate change.

SIGNIFICANCE STATEMENT

Here, we contribute to the understanding of global disparities imposed
by climate risk by introducing the concept of outdoor days -- thermal
comfort conditions allowing for outdoor activities, such as walking,
jogging, cycling, and those related to construction and tourism
industries, by most people. We project that the north-south disparity of
global climate risk is expected to increase considerably by the end of
this century with more frequent outdoor days in the wealthy north and
less frequent outdoor days in the deprived south under high emissions
scenarios. These findings have the potential to provide researchers,
policymakers, and climate advocates with evidence-based knowledge
informing more accurate depiction of climate risk, and more rational
debate regarding compensations for loss and damage.

1. Introduction

Climate change has potentially severe and far-reaching impacts that
affect nearly every Earth's system and industry, putting the lives and
livelihoods of millions of people at risk (Rising et al. 2022; Schewe et
al. 2019). The potential risk of climate change is defined by the
interaction of climate hazards with the human and natural system's
vulnerability and exposure (IPCC 2022) (Fig. 1; see Fig. S1 for
definitions of the three components of climate risk). Since countries
exhibit substantial differences in these elements (Shiogama et al.
2019), especially vulnerability and exposure, there are considerable
variations in the potential risks from changing climate between regions
and countries (Diffenbaugh and Burke 2019). Many previous studies
revealed that while some regions may experience severe negative impacts
from climate change, others may potentially gain some benefits (Kalkuhl
and Wenz 2020; Mendelsohn et al. 2006; Tol 2009). The intersection of
climate change and inequality, referred to as the climate-inequality
nexus (Onbargi 2022), is one of the most pressing challenges of climate
change and has significant social, economic, and environmental
consequences (IPCC 2022).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Fig. 1. Climate risk is defined by three elements: vulnerability,
exposure, and hazard. While the level of certainty is high and the
significance of evidence is well established regarding disparities
between the global north and the global south in vulnerability and
exposure, certainty is low and evidence is not well established when it
comes to climate hazard. Examples of disparities in climate hazard are
few, and the contrast between the global north and south shown by these
examples is largely not sharp.

The geographically different impacts of climate change are not only
limited to a specific sector but emerge in various fields. Some examples
of disparities caused by climate change include differences in its
impact on economic production (Burke et al. 2015; Callahan and Mankin
2022; Diffenbaugh and Burke 2019; King and Harrington 2018), disparities
in how people allocate their time between work and leisure (Zivin and
Neidell 2014), effects on poverty, urbanization, and migration
(Burzy\selectlanguage{polish}ń\selectlanguage{english}ski et al. 2022), disparities in gender-based health (Sorensen et
al. 2018), and contrasting consequences on income in urban and rural
regions within countries (Paglialunga et al. 2022).

Compensating for disproportionate loss and damage imposed by climate
change has been an integral, however, one of the most debatable and
complex subjects in the international forums (e.g., the
27\textsuperscript{th} Conference of Parties (COP27), held in Egypt in
November 2022) that discuss climate change policies (Dorkenoo et al.
2022) (see text in the online supplementary material regarding the
concept of loss and damage imposed by climate change). This topic is
especially relevant considering the debate about the distribution of
responsibility for causing climate change and who should bear the costs
(Farber 2007), which are difficult to quantify (Rising et al. 2022), but
certainly high (Dietz et al. 2018). Industrial and relatively rich
countries (thereafter, the global north) have historically been the main
emitters of greenhouse gases (GHGs) (Althor et al. 2016; Wei et al.
2016). Being legally liable for GHG emissions has been the main concern
of the global north around the issue of responsibilities and
compensation over climate change. However, the relatively poor
developing countries (thereafter, the global south), which have
contributed less to the problem, are disproportionately impacted by
climate change (IPCC 2022).

In the current research, we emphasize that discussions over differential
climate risk and compensation for the associated loss and damage have
widely been tackled mainly from the lens of disparities between the
global north and global south in terms of vulnerability and exposure.
This is mainly because vulnerability and exposure display clear
differences between these two groups of countries and the evidence for
these disparities are highly significant, certain, and well-established
(Fig. 1). But when it comes to climate hazard - the third component that
defines climate risk - there are only a few significant evidence of
sharp disparities between the global north and the global south.
Additionally, evidence for such a differential pattern are either based
on variables with relatively less significance to society or the
contrast between the global north and south is not sharp. This area of
research regarding north-south disparities in climate hazard has
received relatively little attention (IPCC 2014) albeit its centrality
in shaping climate change impact and defining losses, damages, and
responsibilities.

2. Data and Methods

a. Observations and CMIP data

The observed 3-hourly temperature, dew point temperature and surface
pressure data with a spatial resolution of 0.25\selectlanguage{ngerman}° covering the 1959-2021
period were from the ERA5 reanalysis (Hersbach et al. 2020) (available
at http://apps.ecmwf.int/datasets/). We used the daily output of
temperature over the 1976--2100 period from 31 CMIP5 GCMs forced by
historical forcing until 2005 and Representative Concentration Pathway
8.5 (RCP8.5) scenario thereafter (Taylor et al. 2012) and 29 CMIP6 GCMs
forced by historical forcing up to 2014 and Shared Socioeconomic Pathway
5-8.5 (SSP5--8.5) scenario thereafter (Eyring et al. 2016). RCP8.5
represents a business-as-usual scenario with radiative forcing, reaching
about 8.5\selectlanguage{english} Wm\textsuperscript{-2} in 2100. SSP5-8.5 is approximately
equivalent to RCP8.5 (Russo et al. 2019). For each model, one ensemble
member was used. The list of global climate models and their details
were provided in Table S1. All model outputs from CMIP6 (respectively
from CMIP5) were re-gridded to a common 1deg\selectlanguage{english} x\selectlanguage{english} 1deg grid (respectively
1.5deg\selectlanguage{english} x\selectlanguage{english} 1.5deg grid). The anomalies in the model outputs are obtained for
the period 1976-2005. The gridded population density was from the
Gridded Population of the World (CIESIN 2018). The gridded global GDP
was provided by Kummu et al. (2018).

b. Derivation of wet-bulb temperature

Wet-bulb temperature (TW) was computed adopting a method developed by
Davies-Jones (Davies-Jones 2008) using surface temperature, humidity,
and pressure, derived from the 3-hourly CMIP outputs and the ERA5
reanalysis.

c. Outdoor days

Our earlier study introduced the novel concept of ``outdoor days''
defined as the number of days with moderate temperature, neither too
cold nor too hot, that allows most people to enjoy outdoor activities
(Choi et al. 2023). Here, we adopt the same definition, following Choi
et al. (2023), to estimate ``outdoor days'' based on dry-bulb and
wet-bulb temperature. That is, the range of dry-bulb temperature from 10
\selectlanguage{english} to 25 \selectlanguage{english} is assumed to be suitable for outdoor activity (This range of
temperature is comparable to a wet\selectlanguage{english}-bulb temperature ranging from 8 \selectlanguage{english} to
15 \selectlanguage{english}). We assume this definition is valid for all locations on Earth.
However, the exact range of temperature defining an outdoor day may, in
general, vary slightly depending on geographical location, tolerance
levels of the local population, and what exact fraction of the
population is meant when we say ``most people''. Our definition of an
outdoor day is discussed further below, where it is demonstrated that
our results are not sensitive to the exact range of temperature we
assume in this analysis. To explore this sensitivity, please visit
https://eltahir.mit.edu/globaloutdoordays/.

Most existing studies that investigated concepts similar to outdoor days
presented herein are limited to local and regional scales (Choi et al.
2023; Gao et al. 2018; Hanlon et al. 2021; Heng and Chow 2019; Lin et
al. 2019; Spagnolo and de Dear 2003; Wu et al. 2017; Zhang et al. 2022;
Zhang 2016). Previous studies referred to pleasant weather conditions as
mild weather (Lin et al. 2019; van der Wiel et al. 2017; Zhang et al.
2022), mild days (Day et al. 2021), good weather (Zhang 2016), outdoor
thermal comfort (Heng and Chow 2019), thermal comfort condition (Gao et
al. 2018), comfortable days (Wu et al. 2017), and thermal comfort
(Spagnolo and de Dear 2003).

Climate variables used in previous studies to analyze changes in the
characteristics of mild weather include temperature (Choi et al. 2023;
Hanlon et al. 2021; Zhang et al. 2022), dewpoint temperature (van der
Wiel et al. 2017), wet-bulb temperature (Choi et al. 2023),
precipitation (van der Wiel et al. 2017; Zhang 2016), relative humidity,
wind speed, sunshine duration (Lin et al. 2019; Zhang et al. 2022),
shortwave radiation, diffuse shortwave radiation, longwave radiation,
and velocity (Spagnolo and de Dear 2003). Temperature is the primary and
common variable used in most, if not all, of the previous studies that
investigated the impact of climate change on concepts similar to outdoor
days. However, the optimum ranges used for the daily maximum temperature
to define mild weather vary considerably. The optimum daily maximum
temperature considered in these studies ranges between 18 \selectlanguage{english} and 31.6 \selectlanguage{english}.
A summary of these literatures can be found in the online supplementary
material (Table S2).

The studies of van der Wiel et al. (2017) and Zhang et al. (2023) are
the only ones that investigated mild weather conditions on a global
scale. These studies show a clear contrast in the change of mild weather
between the global north and the global south. However, both studies
considered applying only one or a few GCMs, limiting the reliability of
future projections. Additionally, disparities between the global north
and south were not the central focus of van der Wiel et al. (2017). To
the best of our knowledge, our study is the first to provide more
evidence of its scale in terms of data and models on the north-south
disparities in outdoor days, improving our understanding of how
disparities in climate hazard shape the contrasting risk of climate
change on the global scale.

3. Results

In the current climate, outdoor days occur frequently in most regions of
the world (Fig. 2). On average, across the land areas of the world,
approximately 91 outdoor days are experienced every year (i.e., about
25\% of the days of a year), although seasonal and/or regional
differences are evident. If we restrict ourselves to residential areas
(i.e., areas with a population density above 1 person per square
kilometer), more outdoor days are found (165 days per year; i.e., about
45\% of the days of a year). Particularly, the global south stands out
due to more frequent outdoor days, compared to the global north. On a
regional scale, high-latitude countries, such as Canada and Russia, are
too cold (Fig. S2a), resulting in fewer outdoor days, especially during
cold months, whereas countries such as Angola and regions like the
southern parts of Brazil show frequent outdoor days regardless of season
(Fig. 2).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Fig. 2. Global distribution of outdoor days. Climatology of (a) annual,
(b) spring, (c) summer, (d) autumn, and (e) winter outdoor days for the
period 1959-2021. These global maps and time series are derived from
ERA5.

Here, we show that the recent global warming due to climate change has
disproportionately affected outdoor days in the global north and south
(Fig. 3 and Fig. S2b). Based on the modern climate record, annual
outdoor days show a decreasing trend in the global land areas while
exhibiting an asymmetric pattern between developing countries in the
south and developed countries in the north (Fig. 3). The enhanced risk
of a climate hazard, in the form of reduced outdoor days, is
particularly significant in the tropical regions. Outdoor days in these
regions have decreased by about 13\% in the last three decades compared
to the period 1961-1990, showing a significant downward trend
(\emph{p-value} \textless{} 0.01). Meanwhile, high-latitude countries
have benefited from recent global warming, with a 16\% increase in the
number of outdoor days. Furthermore, on a seasonal time scale, outdoor
days in the tropical regions show sharp reductions of outdoor days in
the relatively warm season (Fig. 4). Meanwhile, the net change of
outdoor days in middle-latitude countries is mostly positive but small
because of cancellation between increasing and decreasing trends during
the winter and summer, respectively (Fig. 4).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Fig. 3. Trend of outdoor days. Time series of outdoor days for the (a)
global, (b) low-latitude, (c) mid-latitude, and (d) high-latitude
residential areas for the period 1959-2021. Residential areas are
defined as having a population density above 1 person per square
kilometer. Horizontal black lines denote the 1961-1990 mean and the
1991-2020 mean. Difference (1991-2020 minus 1961-1990) in the number of
outdoor days is represented in each plot. These time series are derived
from ERA5.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Fig. 4. Observed change in outdoor days. Normalized change in outdoor
days in 1991--2020 with respect to 1961-1990. The changes are normalized
by the 1961-1990 mean. These global maps are derived from ERA5.

Based on the General Circulation Model (GCM) projections from the CMIP5
and CMIP6 archives, the observed warming trend is projected to continue
towards the end of the 21\textsuperscript{st} century (Fig. 5 and Fig.
S2). For the period 2071-2100, most land areas in the globe will likely
experience significant warming by an average of 5.0  from CMIP5 models
and by an average of 5.4  from CMIP6 models under high-emissions
scenarios, though with some spatial variability. The overall warming
across the global land surface shows no evidence for a north-south
disparity, except for the projection that high latitudes may warm more
significantly. The consistency between the CMIP5 and CMIP6 models is
robust, ensuring the reliability of future projections.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Fig. 5. Projected change in temperature. Spatial distribution of change
in annual mean temperature in 2071-2100 with respect to 1976-2005,
derived from (a) 31 CMIP5 GCMs and (b) 29 CMIP6 GCMs. Superimposed
hatching indicates that more than 80\% of models agree on the sign of
the change. Zonal-mean changes are indicated by the right corner for
each panel. Thick solid blue line in each panel indicates an ensemble
mean of CMIP6 models.

The projected warming due to elevated greenhouse gas concentrations in
the atmosphere could enhance the north-south disparity in the number of
outdoor days (Fig. 6). Consistent with observed trends in the historical
record, we project relatively large drops in the tropical regions, and a
significant increase in the northern high-latitude regions towards the
end of the century. It implies that countries in the global south (for
example, Colombia, Ivory Coast, Sudan, Indonesia, and Bangladesh), that
are contributing less to the emissions of greenhouse gases (Fig. S3),
are disproportionately affected by the negative impacts of climate
change through reduced outdoor days (Fig. 7 and Fig. S4 derived from
CMIP5 models). Meanwhile, developed countries, such as Canada, France,
the United Kingdom, Germany, and Japan, are marginally affected or
benefit from climate change by gaining more outdoor days (Fig. 7 and
Fig. S4 derived from CMIP5 models).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

Fig. 6. Projected change in outdoor days. Spatial distribution of change
in annual outdoor days in 2071-2100 with respect to 1976-2005, derived
from (a) 31 CMIP5 GCMs and (b) 29 CMIP6 GCMs. The changes in (a-b) are
normalized by the 1976-2005 mean. Superimposed hatching indicates that
more than 80\% of models agree on the sign of the change. Zonal-mean
changes (not normalized) are indicated by the right corner for each
panel. Thick solid blue line in each panel indicates an ensemble mean of
CMIP6 models.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

Fig. 7. Temporal evolution of annual outdoor days derived from CMIP6
models. Time series of annual outdoor days (day) derived from 29 CMIP6
GCMs under the historical and SSP5-8.5 scenarios. Thick solid blue line
indicates an ensemble mean of CMIP6 models. Horizontal black and blue
lines denote the 1976-2005 mean and the 2071-2100 mean, respectively.
Difference (2071--2100 minus 1976--2005) in the number of outdoor days
is represented in each plot. The background image was obtained from NASA
Visible Earth.

Underlying mechanisms responsible for the north-south disparity in
climate hazard have been investigated by studying changes in the
probability distribution of temperature over six selected countries
across all climate zones (Fig. 8). Modeling results show an evident
shift of the probability distribution of temperature toward warmer
temperature across the countries (Fig. 8a). The warming shift of the
probability distribution of temperature induces a significant increase
in outdoor days in the European Union and to a less degree in the United
States. The latter enjoys a broader range of climatic zones than the
former. In both regions, climate change causes fewer outdoor days during
the warm season. However, an increase in outdoor days in the cold season
compensates for this decreasing trend (Fig. 9). A large fraction of the
population in the northern high-latitude regions, such as Russia and
Canada, will likely see large increases in outdoor days, benefiting
significantly from climate change. In contrast, the projected
probability distribution of temperature in Brazil, Nigeria, and India,
are likely to move away from the conditions of thermal comfort, limiting
outdoor activities significantly in these large population centers of
the South (Fig. 8). The results of Figure 8 show clearly that the
north-south disparity is rooted in the background climatology of
temperature, mainly the position of the probability distribution of
temperature relative to the range of values used to define an outdoor
day. Similar results and conclusions are obtained by analyzing
projections by CMIP5 models (Fig. S5).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

Fig. 8. Probability Mass Function (PMF) of daily mean temperature ()
and temporal evolution of annual outdoor days derived from CMIP6 models.
a) The PMFs are generated for two groups of countries -- developing
countries (Brazil, Nigeria, and India) and developed countries (the
United States, the EU, and Russia) -- for each GHG scenario: historical
(black) and SSP5-8.5 (blue). The bin interval is 1 . The vertical
dashed red line in a) indicates the range of temperature (from 10  to
25 ), defined in this study for outdoor days. The background image in
a) was obtained from NASA Visible Earth. b) Time series of annual
outdoor days (day) derived from 29 CMIP6 GCMs under the historical and
SSP5-8.5 scenarios. Thick solid blue line in b) indicates an ensemble
mean of CMIP6 models. Horizontal black and blue lines denote the
1976-2005 mean and the 2071-2100 mean, respectively. Difference
(2071--2100 minus 1976--2005) in the number of outdoor days is
represented in each plot.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

Fig. 9. Projected change in outdoor days for four seasons. Normalized
change in outdoor days in 2071--2100 with respect to 1976-2005 for four
seasons. The changes are normalized by the 1976-2005 mean. Superimposed
hatching indicates that more than 80\% of models agree on the sign of
the change. These global maps are derived from 29 CMIP6 GCMs.

4. Discussion

Climate change studies usually focus on climate extremes and/or changes
in the mean climate conditions, which are certainly important aspects of
climate change (Choi et al. 2021, 2022; Choi and Eltahir 2022, 2023;
Fischer and Knutti 2015; Im et al. 2017; Kang and Eltahir 2018; Pal and
Eltahir 2016; Pfahl et al. 2017; Tuel and Eltahir 2020; Zhao et al.
2021). However, investigating mild weather, as done here for outdoor
days, is of significant importance to society (Lin et al. 2019; van der
Wiel et al. 2017; Zhang et al. 2022) since it could shape the
contrasting risk of climate change on the global scale as revealed in
this study. Meanwhile, future changes in rainfall and temperature -- the
two main climate variables -- due to climate change show no clear
north-south disparity in climate risk. According to IPCC (2022), the
overall trend for temperatures is to increase almost uniformly in the
future across the globe. An overall wetting trend in precipitation is
expected, except for a few regions, like the Mediterranean (Tuel and
Eltahir, 2020). Therefore, future changes in rainfall and temperature do
not provide significant evidence for a north-south disparity in climate
risk.

Although substantial scientific and public attention has focused on the
asymmetric distribution of vulnerability and exposure to climate change
(Diffenbaugh and Burke 2019), some climate extremes could possibly
result in somewhat north-south disparity (Carleton et al. 2020; Crowther
et al. 2016; Hirabayashi et al. 2013; Kam et al. 2021; Park et al. 2018;
Shiogama et al. 2019; Vicedo-Cabrera et al. 2021). For instance,
Shiogama et al. (2019) displayed that the regions with relatively large
increases in several hazard indicators, i.e., extremely hot days, heavy
rainfalls, and high stream flow coincide with countries characterized by
small CO\textsubscript{2} emissions, low income, and high vulnerability.
The frequency and pattern of river flood occurrence due to climate
change exhibit a north-south gradient (Kam et al., 2021), with flood
frequency projected to increase in regions such as Southeastern Asia and
Eastern Africa and decrease in regions such as North America and Europe
(Hirabayashi et al., 2013). The effect of climate hazard might be
extended to soil systems with considerable losses in soil carbon stocks
in high-latitude areas and an expected increase in tropical regions
(Crowther et al., 2016). This suggests a reverse disparity where
developing countries are benefiting from climate change while developed
countries are losing. Moreover, the timing of climate hazards also
exposes a north-south contrast as reported by Park et al. (2018).
According to Park et al. (2018), while the time for the emergence of
aridification due to climate change in the global south is expected to
mostly occur before 2050, it might occur later than 2050 in the global
north. Until recently, climate hazard did not seem to affect the large
discrepancies in climate risk between countries - the global north and
the global south and/or rich and poor countries and/or high and low
emitters of greenhouse gases (Fig. S3).

Our results present some important caveats. First, the simulated
temperature from CMIP5/CMIP6 models has systematic biases of temperature
(Fig. S6). In turn, the biases may be transmitted to the historical runs
and future projections of outdoor days to some degree (Fig. S7).
Nevertheless, the overall pattern of outdoor days is well captured by
the climate models. Note that, compared to the CMIP5 models, the CMIP6
models tend to better represent the observed spatial patterns of
temperature and outdoor days over the world for the historical period,
due to their remarkable improvements, in terms of spatial resolution,
physical processes, and biogeochemical cycles (Eyring et al. 2016).
Second, the human feeling of weather is complex and a widely subjective
matter, and therefore, the definition of mild weather is non-trivial
(van der Wiel et al. 2017). However, it can broadly be defined as
pleasant weather conditions allowing most people to enjoy outdoor
activities such as walking, jogging, cycling, or those related to
construction and tourism industries (Lin et al. 2019; van der Wiel et
al. 2017; Zhang 2016). Although we, here, defined outdoor days assuming
a range of temperature from 10  to 25 , the exact range of temperature
or variable used does not significantly affect the global distribution
of the climate risk induced by changes in outdoor days (Fig. 10; Table
S3; see interactive visualization at
https://eltahir.mit.edu/globaloutdoordays/). In particular, considering
other variables, such as wet-bulb temperature (Fig. S8) and different
ranges of temperature resulted in a broadly similar pattern, supporting
the north-south disparity. Third, analysis for some specific countries
might show contrasting responses of outdoor days to climate change
within the different climate regions of a country as shown by Choi et
al. (2023) for the United States.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}

Fig. 10. Change in outdoor days with different definitions. Normalized
change in annual outdoor days in 1991--2020 with respect to 1961-1990.
Various definitions considered in this study are indicated in each plot.
The changes are normalized by the 1961-1990 mean. These global maps are
derived from ERA5.

5. Conclusions

Here, we used the state-of-the-art reanalysis data and projections from
60 GCMs (31 CMIP5 and 29 CMIP6) to propose that the climate hazard
associated with the change in outdoor days (i.e., the number of days
with pleasant weather that allows for outdoor activities by most people)
could contribute to the north-south disparity that sustains inequality
and injustice of climate change. We project that this disparity is
expected to increase considerably in the future, assuming the high
emissions scenarios.

Our results have important implications for the injustice of climate
change. That is, the negative impacts of climate change accompanied by
the decreased outdoor days will significantly affect tropical countries,
including Colombia, Brazil, Ivory Coast, Nigeria, Sudan, Indonesia,
Bangladesh, and India, which are developing countries with large
populations but relatively minor emitters of carbon dioxide (Figs. S3
and S9). Meanwhile, some of the historically largest emitters of carbon
dioxide, including the United States, Canada, the European Union,
Russia, and Japan may benefit to varying degrees from the increased
outdoor days. It is important to note that climate risk inferred from
the existing literature may be substantially underestimated, especially
in tropical regions since they do not consider risk caused by the
climate hazard related to outdoor days, as evident from our analysis.

The findings reported here are not only important from the point of view
of climate risk and how it varies spatially and temporally, but they
also contribute to informing the ongoing discussions regarding
compensations for loss and damage imposed by climate change.

Acknowledgments.

We acknowledge support from the Abdul Latif Jameel Water and Food
Systems Lab (J-WAFS) at MIT, and the MIT Climate Grand Challenges
project: Jameel Observatory- Climate Resilience Early Warning System
Network (CREWSnet).

Data Availability Statement.

All original CMIP5 GCMs, CMIP6 GCMs, and the ERA5 reanalysis used in
this study are publicly available at
https://esgf-node.llnl.gov/projects/cmip5/,
https://esgf-node.llnl.gov/projects/cmip6/,
https://doi.org/10.24381/cds.adbb2d47, respectively. CMIP5 and CMIP6
models used in this study are listed in Table S1. The gridded population
density of the world is from the Center for International Earth Science
Information Network (CIESIN 2018). The gridded global datasets for GDP
are available from Kummu et al. (2018). GDP per capita and
CO\textsubscript{2} emissions per capita are from
https://ourworldindata.org/grapher/gdp-per-capita-maddison-2020 and
https://ourworldindata.org/grapher/co-emissions-per-capita,
respectively.

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