Change in cooling degree days with global mean temperature increasing from 1.5 °C to 2.0 °C

Abstract

Limiting global mean temperature rise to 1.5 °C is increasingly out of reach. Here we show the impact on global cooling demand in moving from 1.5 °C to 2.0 °C of global warming. African countries have the highest increase in cooling requirements. Switzerland, the United Kingdom and Norway (traditionally unprepared for heat) will suffer the largest relative cooling demand surges. Immediate and unprecedented adaptation interventions are required worldwide to be prepared for a hotter world.

Full text

Nature Sustainability | Volume 6 | November 2023 | 1326–1330 1326
nature sustainability
Brief Communication
https://doi.org/10.1038/s41893-023-01155-z
Change in cooling degree days with global
mean temperature rise increasing from
1.5 °C to 2.0 °C
Nicole D. Miranda 1,2,6, Jesus Lizana 1,2,6 , Sarah N. Sparrow 3,
Miriam Zachau-Walker2, Peter A. G. Watson4, David C. H. Wallom 3,
Radhika Khosla 1,5 & Malcolm McCulloch1,2
Limiting global mean temperature rise to 1.5 °C is increasingly out of reach.
Here we show the impact on global cooling demand in moving from 1.5 °C
to 2.0 °C of global warming. African countries have the highest increase
in cooling requirements. Switzerland, the United Kingdom and Norway
(traditionally unprepared for heat) will suer the largest relative cooling
demand surges. Immediate and unprecedented adaptation interventions
are required worldwide to be prepared for a hotter world.
This work identifies regions of high cooling needs using 2,100
simulation runs of global mean surface temperature through the
HadAM4 model
1,2
across three global warming scenarios: historical
(2006–2016), 1.5 °C and 2 °C. Rising extreme heat is already driving
an unprecedented surge in cooling demand, with the energy
required for cooling by 2050 predicted to be equivalent to the
combined electricity capacity of the United States, European Union
and Japan in 2016
3
. But how much more cooling would be required if
the Paris Agreement’s preferred 1.5 °C limit
4
is overshot, and global
mean temperature increases to 2.0 °C? The question is crucial, given
the growing consensus that there is currently ‘no credible pathway
to avoid warming to 1.5 °C’5.
Cooling degree days (CDDs) are a widely used indicator to
examine warming and quantify cooling demand. CDDs measure how
warm a given location is, by comparing the mean outdoor tempera-
tures recorded each day with a standard temperature (usually 65 °F
or 18 °C)
3
. For example, a day with a mean outdoor temperature of
30 °C has 12 CDDs. In this Article, we map annual CDDs and examine
the most affected countries by warming from 1.5 °C to 2.0 °C projec-
tions. These are identified by absolute and relative cooling demand
increases between these two scenarios. Absolute changes (abs-ΔCDD)
show where human exposure to hotter weather will be severe. Relative
changes (rel-ΔCDD) indicate large adaptation challenges in regions not
traditionally prepared for increasing heat.
Previous work has mainly reported CDDs using historical data6,7.
Model-based studies for specific areas of the world have also been
reported
8–11
. Global model data, however, have only been analysed for
specific years, leaving an important gap in predicting and preparing
for cooling demand in fast approaching 1.5 °C and 2.0 °C scenarios.
To calculate CDDs, we simulate 700 members per scenario using
the citizen-science project climateprediction.net (CPDN), obtaining
6-hourly mean temperatures at a spatial resolution of 0.883° × 0.556°.
The findings of this study are summarized in Fig. 1 and Table 1.
Figure 1a maps the difference in CDDs between the 1.5 °C and 2.0 °C
scenarios, and Table 1a highlights the top ten countries with more than
5 million inhabitants that will experience, and subsequently need to
respond to, the largest changes. Extended Data Table 1 includes the top
50 countries with a population of more than 2 million. A more extended
list is provided in Supplementary Note 4. To examine variability, we map
the standard deviation of results in Supplementary Note 3.
The results show that regions surrounding the Equator, particu-
larly the Sub-Saharan region, will experience the largest increase in
cooling demand (Fig. 1a). Table 1a shows that ten African countries are
the nations with the largest change in CDDs, with important implica-
tions for their planning and building climate resilience. These coun-
tries align in a west–east band in central Africa. They mainly border
Mauritania, Niger and Sudan, identified in ref. 6 to have the highest
extreme heat historically. Mali and Chad were also previously reported
Received: 21 December 2022
Accepted: 24 May 2023
Published online: 13 July 2023
Check for updates
1Future of Cooling Programme, Oxford Martin School, University of Oxford, Oxford, UK. 2Energy and Power Group, Department of Engineering Science,
University of Oxford, Oxford, UK. 3Oxford e-Research Centre, University of Oxford, Oxford, UK. 4School of Geographical Sciences, University of Bristol,
Bristol, UK. 5Smith School of Enterprise and the Environment, School of Geography and the Environment, University of Oxford, Oxford, UK. 6These authors
contributed equally: Nicole D. Miranda, Jesus Lizana. e-mail: jesus.lizana@eng.ox.ac.uk
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Brief Communication https://doi.org/10.1038/s41893-023-01155-z
0 100 200 300
abs-∆CDDs
rel-∆CDDs
aAbsolute ∆CDD18 from 1.5 °C to 2 °C
bRelative ∆CDD18 from 1.5 °C to 2 °C
10%0 20% 30% 40%
Fig. 1 | Global CDD difference between 1.5 °C and 2 °C global warming
scenarios. a, Absolute delta cooling degree days (abs-ΔCDD) from 1.5° to 2 °C
global warming scenarios. b, Relative delta cooling degree days (rel-ΔCDD)
from 1.5 °C to 2 °C global warming scenarios. Delta (Δ) refers to the incremental
change in the variable. The absolute and relative delta from 1.5 °C to 2 °C
scenarios were calculated using the mean annual CDDs per coordinate across
ensemble members per scenario, involving 700 simulations each. Administrative
boundaries were used from EuroGeographics.
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Nature Sustainability | Volume 6 | November 2023 | 1326–1330 1328
Brief Communication https://doi.org/10.1038/s41893-023-01155-z
to have the highest historical CDD6, and here we show that they will
also experience a large increment in CDDs from a 1.5 °C to a 2.0 °C
scenario. Indeed, the central African population not only had the
highest requirements for cooling historically (2009–2018) but would
also experience the highest surge in heat exposure and wide-ranging
adaptation requirements.
Notably, the results of relative changes in CDDs (Fig. 1b and
Table 1b) show that the Global North will experience dramatic rela-
tive increases in the number of days that require cooling. Table 1b is
the first to rank the top ten most affected countries by their relative
increases in CDDs globally. Eight of ten are European nations, which
are traditionally unprepared for high temperatures and will require
large-scale adaptation to heat resilience.
Globally, Switzerland and the United Kingdom will see the
largest relative variation in cooling demand (30%). This is relevant, as
current cooling studies for Switzerland and the United Kingdom are,
at best, limited. For Switzerland, only two studies in 2006 and 2021
were found12,13, which warned of the accelerating demand for cooling
(compared with heating demand). In the case of the United Kingdom,
the country with the second-largest relative increase in CDDs, only one
2009 predictive study is found
14
. The latter aligns with the large relative
change of our results (but for different temperature increases), report-
ing that the energy (and emissions) from air conditioners almost dou-
bles from 2004 to 2030 in London. However, these 2009 study results
were not set in the global context we provide. Additional statistics
on the relative (and absolute) increase in CDDs in countries with
more than 2 million inhabitants are provided in Supplementary Note 5,
this time exclusively considering urban areas. This urban area-weighted
analysis identifies Ireland, the United Kingdom and Finland as the
top three most affected countries—foreshadowing important ques-
tions about prioritizing sustainable cooling access and heat resilience
strategies in their cities.
Our results enhance and complement the existing literature.
A previous study examining predictions of CDDs in Europe
9
reports
changes in CDDs between Representative Concentration Pathways
(RCP4.5 and RCP8.5) in different years from historical (1981–2010) to
the period 1981–2100. It models temperature at different years rather
than forcing specific global warming scenarios, as in our analysis.
While the results are analogous regarding the highest absolute increase
in Europe to be in Mediterranean countries, no relative changes are
reported. Another study reports European CDDs (that is, Mediterra-
nean) in a 2.0 °C scenario (with spatial resolution >200 km
2
)
15
, showing
that the further south, the more the absolute change of CDD increases.
In our study, other large regions of high CDD relative increase are
found in the mountain ranges of the Andes in South America, cross
-
ing the continent from North to South, and the Himalayas in Central
Asia, which extend into the Southwest of China. This brings additional
insight for sustainability planning as previous CDD predictions
16,17
for China under different RCP scenarios did not highlight this region
for its relative increase in cooling demand. Further research on
changing climate in these regions is needed as no additional studies
have been found.
Supported by these results, we argue how immediate and
unprecedented climate adaptation interventions are required world-
wide to be prepared for a hotter world. An increasing number of
stocktake studies
4,5
make clear that limiting a surge in global mean
temperature to 1.5 °C is increasingly out of reach. We show that moving
from a 1.5 °C to 2.0 °C warmer planet would dramatically exacerbate
heat exposure and energy demand for cooling. There has already
been an increase in global surface temperature of 1.09 °C above
pre-industrial levels between 2011 and 2020
4,18
. The total difference
in cooling demand from today to a 2.0 °C warmer planet would
be greater than our analysis maps, requiring a key focus on an issue
that has traditionally been a blind spot for sustainability debates19.
For this study, the differences in CDDs reported are built on the
largest ensemble of 700 simulations for each scenario to ensure inter-
nal climate variability and at the current highest available temporal
resolution of temperatures. The 6-hourly mean temperature predic-
tions result in high granularity of cooling demand variations. The
geographical resolution of 0.833° × 0.556° allows examination of the
whole planet under one lens while managing the computational inten-
sity of large datasets.
The absolute change in CDDs values shows that African
countries will experience the highest increase in cooling demand. These
conditions will pose further stress to the continent’s socio-economic
development and energy networks, and their implications for equitable
access to cooling, issues that require much additional research given
the limited studies of this rising threat in the African context
20
. Further,
the results on relative changes indicate that countries that will experi-
ence the most drastic increases in CDDs are traditionally prepared for
heating, not cooling. These countries will require acute and long-term
adaptation to make their populations and the built environment more
heat resilient, including broad cooling access through sustainable
pathways21. Much can be shared and learnt from countries across the
world as they tackle this global challenge.
Overall, CDDs are a valuable indicator of normalized tempera-
ture exposure, and are useful to enable a top-down comparison of
global warming scenarios between regions. As research grows, addi-
tional socio-economic, technical and environmental variables, such
as humidity, solar irradiance and wind speed, are needed for more
precise cooling demand estimations. It should also be noted that indi-
vidual thermal comfort expectations differ across communities and
countries, depending on conventions, physiology and cultural norms,
among others3.
Several important policy implications stem from these results.
First, this work clearly indicates that every small increase in global
warming will affect heat exposure and cooling demand worldwide,
driving the need for immediate, unprecedented and localized adap-
tation. Second, it is in the national interest of all Global North and
South countries to work towards the 1.5 °C target, given that they will be
the most affected by the relative and absolute change in CDDs, respec-
tively. Current planning and implementation of energy and climate
Table 1 | Ranking of the top ten countries that will suffer the
highest increase (absolute and relative) in area-weighted
mean CDDs from 1.5°C to 2.0°C
Top ten countries by
absolute change abs-∆CDD18 Top ten countries by
relative change rel-∆CDD18
Central African
Republic 266 Switzerland 30%
Burkina Faso 254 United Kingdom 30%
Mali 253 Norway 28%
South Sudan 251 Finland 28%
Nigeria 245 Sweden 28%
Congo 241 Austria 24%
Democratic Republic
of The Congo 240 Canada 24%
Chad 236 Denmark 24%
Uganda 232 New Zealand 24%
Cameroon 228 Belgium 21%
Countries with more than 5 million inhabitants in 2020 are listed. Annual CDDs were
calculated using a temperature baseline of 18°C. Delta (∆) refers to the incremental change
in the variable. The rankings use the area-weighted mean values per country rather than
grid-speciic relative values, as the latter can distort results with large percentage values for
speciic latitude–longitudes that go from no/negligible CDDs in a 1.5°C to having notable
CDDs in a 2.0°C. The full list of countries with more than 2M population is provided in
Supplementary Notes 4 and 5, following different statistical criteria.
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policies across countries must be designed to be prepared for and build
resilience to a hotter local climate. It is important to recognize that
the dramatic, and often inequitable, rise in cooling demand can no
longer be ignored but rather be addressed through socio-technical
levers of change19, which support holistic sustainable solutions.
Methods
Ensembles of 2,100 global climate simulations for mean temperature
for three scenarios were generated using the HadAM4 Atmosphere-only
General Circulation Model
1,2
from the UK Met Office Hadley Centre. The
scenarios followed the half-a-degree additional warming prognosis
and projected impacts experiment design protocol
22
, specifically:
historical (2006–2016), 1.5 °C and 2 °C above pre-industrial levels.
Thus, the model was forced to achieve the increase in temperature for
scenarios 1.5 °C and 2.0 °C, regardless of when this occurs. The simula-
tions output 6-hourly mean temperatures at a horizontal resolution
of 0.833 longitude and 0.556 latitude, where each scenario involves
70 individual members for a 10 year period (700 runs per scenario),
aiming to ensure internal variability. This simulation experiment ran
within the CPDN climate simulation environment
23
. CPDN uses the
Berkeley Open Infrastructure for Network Computing
24
framework,
tasking more than 30,000 globally distributed volunteer members
of the public.
Biases in simulated temperature were identified and corrected
using a quantile mapping approach. The bias correction was performed
in the entire ensemble using reference temperature data from ERA5 for
the same timeframe of the historical scenario (2006–2016). Biases are
calculated for each percentile in the cumulative distribution function
from the historical scenario compared with ERA5 observations. Then,
the calculated biases are added to the simulations of the historical,
1.5 °C and 2 °C scenarios to correct the biases of each percentile, assum-
ing that the bias is unchanging between scenarios. This ensures the
preservation of the ensemble’s internal variability, and the cumulative
distribution of the ensemble aligns with the cumulative distribution of
the observations. Further details and validation of the climate model
are provided in Supplementary Note 1.
CDDs were used to compare global warming scenarios. CDDs are
a widely used indicator to measure temperature exposure and cooling
demand through dry bulb temperature. Annual CDDs were calculated
for the ensemble members per scenario (700 simulated years) in all
coordinates according to equation (1):
CDD 
t󰅗m
󰞍
t󰅗0
Tt󰁝Tbase
n
TtTthreshold
(1)
where
t
is the time step,
m
is the last time step of the year,
n
is the
number of time steps in one day (n = 4, given 6-hourly data),
Tt
is the
mean outdoor temperature at time t, T
base
is the reference temperature
used to calculate the temperature difference, and Tthreshold is the outdoor
temperature value above which the temperature differences are cal-
culated. T
threshold
and the baseline temperature, T
base
, was defined as
18 °C, following the most widespread approach in previous studies to
enable comparison3. However, this methodology can have several
modifications depending on available data, context and application
(Supplementary Note 2). It should be noted that since we are evaluating
the absolute and relative change between scenarios, the modification
of CDD calculation criteria has few implications in the findings.
Then, mean annual CDDs and standard deviation per coordi-
nate across ensemble members (700 simulations) were obtained for
the 1.5 °C and 2 °C scenarios, and deltas were computed. Finally, the
area-weighted statistics per country were calculated using QGIS geo-
graphic information system. Supplementary Note 4 lists the top 100
countries with more than 2 M population. Additionally, Supplementary
Note 5 also introduces the top 100 countries by considering only urban
area-weighted statistics per country to consider the dimension of urban
contexts. This last ranking should be considered carefully since 44% of
the population still lives in rural areas25.
This study has the following limitations. CDDs were calculated
using the dry bulb temperature following the standard approach,
which does not account for the influence of humidity or other envi-
ronmental variables on perceived thermal comfort. CDDs may
also be underestimated in urban areas since the urban heat island
effect was ignored.
Supplementary Information provides additional details of the
methods and results associated with the climate model (Supplemen-
tary Note 1), CDDs (Supplementary Note 2), additional statistical results
(Supplementary Note 3) and a more extended ranking of countries
according to different criteria (Supplementary Notes 4 and 5).
Reporting summary
Further information on research design is available in the Nature Port-
folio Reporting Summary linked to this article.
Data availability
The data of absolute and relative changes in CDDs (to reproduce the
maps of this work) are found in the Oxford University Research Archive
ORA at https://doi.org/10.5287/ora-9rbzrxxgz. Further data are avail-
able from the corresponding author on request.
Code availability
The atmosphere-only HadAM4 model was used to generate the data
from the Met Office Hadley Centre. In addition, the CPDN project
simulation facility is open for collaboration and has an academic licence
for the HadAM4 MetOffice software, which can be shared with official
collaborators. The code with the ensemble bias correction method
using the quantile mapping approach is available at https://github.
com/lizanafj/ensemble-bias-correction. Further codes are available
from the corresponding author on request.
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Acknowledgements
The research was supported by the Oxford Martin School, through
its Future of Cooling Programme. J.L. was funded by the European
Union’s Horizon 2020 research and innovation programme under
the Marie Skłodowska-Curie grant agreement no. 101023241.
S.N.S. and P.A.G.W. were supported by the UKRI (NE/P002099/1). For
the purpose of open access, the author has applied a CC BY public
copyright licence to any author accepted manuscript version arising
from this submission. We also thank R. Renaldi for supporting the
conceptualization of the research.
Author contributions
N.D.M. and J.L. contributed equally. N.D.M. and J.L. coordinated the
study and performed the data pre-processing and data analytics
of the models. They developed the bias correction, inal statistics
and visualizations, and jointly wrote the paper draft. S.N.S. and
D.C.H.W. ran the CPDN model, and led the extraction of data.
S.N.S. and P.A.G.W. provided expertise in data analytics and bias
correction. M.Z.-W. extracted data from the model. R.K., D.C.H.W.
and M.M. conceptualized the work, and proposed and reviewed the
content of the paper.
Competing interests
The authors declare no competing interests.
Additional information
Extended data is available for this paper at
https://doi.org/10.1038/s41893-023-01155-z.
Supplementary information The online version
contains supplementary material available at
https://doi.org/10.1038/s41893-023-01155-z.
Correspondence and requests for materials should be addressed to
Jesus Lizana.
Peer review information Nature Sustainability thanks Yukihiro
Kikegawa, Hussain Athar and Yuya Takane for their contribution to the
peer review of this work.
Reprints and permissions information is available at
www.nature.com/reprints.
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Extended Data Table 1 | Ranking of the top ifty countries with more than 2 million inhabitants that will suffer the highest
increase (absolute and relative) in area-weighted mean CDDs from 1.5°C to 2.0°C
a, Countries by absolute change
abs-ΔCDD18
b, Countries by relative change
rel-ΔCDD18
1
Central African Republic 266.2
1
Ireland 37.9%
2 Burkina Faso 254.5
2
Switzerland 30.3%
3 Mali 252.6
3
United Kingdom 29.8%
4
South Sudan 251.4
4
Norway 28.2%
5
Nigeria
244.9
5
Finland
27.8%
6 Congo 241.0
6
Sweden 27.6%
7
Democratic Republic of The Congo 240.1
7
Austria 24.5%
8
Chad
235.6
8
Canada
24.4%
9 Uganda 231.6
9
Denmark 24.4%
10
Cameroon 227.5
10
New Zealand 23.7%
11 Brazil 226.9
11
Lesotho 21.4%
12
Guatemala 224.9
12
Belgium 20.9%
13 United Arab Emirates 220.4
13
Czechia 20.4%
14 Benin 220.0
14
Germany 20.3%
15
Sudan 219.7
15
Netherlands 20.0%
16 Saudi Arabia 219.5
16
Slovenia 20.0%
17 Côte d'Ivoire 218.6
17
Russian Federation 19.5%
18
Honduras 215.9
18
Slovakia 19.2%
19 Mauritania 214.6
19
Kyrgyzstan 19.2%
20 Venezuela 213.5
20
Bosnia and Herzegovina 18.4%
21
Guinea 212.8
21
Poland 18.3%
22 Togo 212.8
22
Armenia 17.9%
23 Botswana 212.1
23
Lithuania 17.4%
24
Niger 211.5
24
Belarus 17.3%
25
Angola
211.1
25
Serbia
17.3%
26 Paraguay 209.5
26
North Macedonia 16.9%
27
Eritrea 209.2
27
Georgia 16.7%
28 Senegal 207.0
28
Chile 16.7%
29
Sierra Leone 205.9
29
Croatia 16.4%
30
Oman 205.1
30
Hungary 16.3%
31
Liberia
204.8
31
Romania
16.1%
32 Zambia 203.7
32
Mongolia 15.5%
33 United Republic of Tanzania 203.6
33
Albania 15.5%
34 Myanmar/Burma 203.1
34
Rwanda 14.5%
35
Kuwait 202.2
35
Bulgaria 14.3%
36 Colombia 201.6
36
Burundi 14.3%
37 Nicaragua 199.8
37
Ukraine 13.5%
38
Qatar 197.9
38
Moldova 13.4%
39 Thailand 196.7
39
North Korea 13.3%
40 Laos 196.2
40
Italy 13.2%
41
Gabon 194.9
41
Spain 13.1%
42 Ghana 193.3
42
France 12.7%
43 El Salvador 192.6
43
United States 12.7%
44
Kenya 190.6
44
Portugal 11.9%
45
Cambodia
188.5
45
Turkey
11.4%
46 Yemen 188.0
46
Greece 11.2%
47
Algeria 187.9
47
Kazakhstan 11.2%
48 Bangladesh 187.5
48
Zambia 10.9%
49
Ethiopia 187.2
49
China 10.7%
50 Mozambique 185.6
50
South Korea 10.6%
Countries with more than 2 million inhabitants in 2020 are listed. Annual CDDs were calculated using a temperature baseline of 18°C. Delta (Δ) refers to the
incremental change in the variable. The rankings use the area-weighted mean values per country rather than grid-specific relative values, as the latter can distort
results with large percentage values for specific latitude-longitudes that go from no/negligible CDDs in a 1.5C to having notable CDDs in a 2.0 ºC. The full list of
countries with more than 2M population is provided in SN4 and SN5, following different statistical criteria.
Ranking of the top ifty countries by absolute and relative changes in CDDs with global mean temperature increasing from 1.5° to 2.0°C. Only countries with more than 2 million inhabitants in
2020 are listed. Annual CDDs were calculated using a temperature baseline of 18°C.
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Corresponding author(s): Jesus Lizana
Last updated by author(s): May 5, 2023
Reporting Summary
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Data generated in this analysis are available here: https://doi.org/10.5287/ora-9rbzrxxgz
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Study description This paper shows the change on cooling demand with global mean temperature increasing from 1.5ºC to 2.0ºC
Research sample Ensembles of 700 climate simulations for three global scenarios were generated using the HadAM4 Atmosphere-only General
Circulation Model (AGCM) from the UK Met Office Hadley Centre. This simulation experiment ran within the climateprediction.net
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Sampling strategy The scenarios followed the half-a-degree additional warming prognosis and projected impacts (HAPPI) experiment design protocol,
being: historical (2006-16), 1.5ºC and 2ºC above pre-industrial levels.
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temperatures at a horizontal resolution of 0.833 longitude and 0.556 latitude, globally.
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