Supplemental Information
Global inequities between polluters and the polluted: climate change impacts on coral
reefs
Detailed Methods
Sea surface temperature and bleaching stress
Sea surface temperature (SST) for the years 1865 – 2055 were based on adjusted output
from two AR4 GCMs (CM2.0 and 2.1), using the approach developed by Donner et al. (2007) and
Donner (2009). AR4 GCMs were used because these models have moderate climate sensitivity and
have been used for multiple studies of coral reef futures (Donner, 2009, Foden et al., 2013, Frieler et
al., 2013). The projected evolution of SSTs and thermal stress is similar in recent CMIP5 output (van
Hooidonk et al., 2013). In order to correct for GCM bias in the spatial pattern of SST, monthly SSTs
were computed as the sum of an observed 1985-2000 monthly SST climatology, based on the 0.5° x
0.5° degree resolution AVHRR Pathfinder dataset used by the NOAA Coral Reef Watch program to
predict coral bleaching in real-time, and the GCM anomalies interpolated to the resolution of the
AVHRRR Pathfinder data (Donner, 2009). For example, the SST for December 2020 was computed as
the sum of the observed climatological value for December and the difference between the GCMsimulated value for December 2020 and the value for December in the GCM-simulated climatology.
The historical analysis (1865-2000) was based on eight all-forcings simulations (3 from CM2.0; 5 from
CM2.1) that include observed anthropogenic contribution to atmospheric greenhouse gas and
aerosol concentrations; the future analysis (2001-2050) is based on two simulations (1 each from
CM2.0 and CM2.1) using the SRES A2 scenario, which roughly tracks observed changes in
greenhouse gas concentrations since 2000. Monthly SST in each cell for each year was expressed as
the median of all the model runs for that year.
Bleaching stress in the model was estimated from the accumulation of degree heating
months (DHM), a metric commonly used in modelling studies to estimate thermal stress on coral
reefs. The maximum DHM in each year was calculated as maximum four-month accumulation of SST
in excess of the maximum value from the monthly 1985-2000 SST climatology. The probability of
DHM > x °C·month (where x=1,2,3,…,8) was calculated using DHM values from a ten-year running
period surrounding a given year from all available model runs. Thus, the DHM exceedence
probability is calculated from n=80 (8 model runs x 10 years) for 1865 through 2000 and n=20 (2
model runs x 10 years) for 2001-2050. Because DHM was based on a ten year bin, the 1996-2000
historical results required some blending of future model runs. To maintain n=80 for these years,
future output for the years 2001-2005 was based on eight different future simulations (SRES A1b,
B1, A2 and a commitment scenario each from CM2.1 and CM2.0). This allowed for a smooth
transition between the historical and future simulations.
Aragonite Saturation State
The saturation state of aragonite (Ωar) between year 1860 and 2050 were obtained from the
simulations of University of Victoria Earth System Model (UVic), version 2.8. The same model was
used in to project future ocean acidification and aragonite saturation state of sea water surrounding
coral reefs (Cao & Caldeira, 2008). Using model simulated fields of dissolved inorganic carbon,
alkalinity, sea surface temperature and salinity, the saturation state of aragonite was computed
using the carbonate chemistry routine from OCMIP project, as detailed in Cao and Caldeira (2008).
The UVic model has a spatial resolution of 1.8° latitude by 3.6° longitude. The aragonite saturation
obtained at this resolution was linearly interpolated to produce annual results at 0.5° x 0.5° degree
resolution for use by the coral reef trajectory model.
Coral reef trajectory model
Coral reef trajectories were modelled for each 0.5 x 0.5 degree cell within the global study
area. Coral cover was initiated at 30% in the year 1865 and estimated annually through 2050. Data
are presented for 1875 onward (Figure 2); the first ten years (1865 – 1874) allowed the trajectories
to reach equilibrium, which occurred quickly due to the absence of climate disturbance at the
beginning of the time series. The equilibrium in 1875 was not sensitive to initial conditions in 1865.
Coral cover in each year was predicted from previous years coral cover plus the combined annual
effects of growth and mortality. Mortality was influenced by bleaching in some years and growth
was influenced by temperature and aragonite effects on calcification. Because we wanted to model
only the impacts of climate change on coral trajectories, we excluded local stressors such as fishing,
nutrients and cyclones (Richmond et al., 2007, Wilkinson, 2004). Thus, our model essentially
integrates the combined effects of warming and acidification from a coral perspective.
We ran two separate models, one for the Caribbean and the other for Indo-Pacific, due to
fundamental ecological differences between these biogeographic regions such as the current lack of
fast-growing branching corals in the Caribbean (Roff & Mumby, 2012). In both models, potential
annual growth was first estimated, which was then reduced according to any calculated effects of
bleaching mortality, and/or temperature and aragonite effects on calcification. Indo-Pacific coral
dynamics were captured using two common, widely distributed taxa: a branching (Pocillopora spp.)
and massive (Porites spp.). The Caribbean was parameterized using three ‘typical’ massive taxa
(Montastraea annularis, Porites astreoides and Agaricia agaricites). Trajectories for individual taxa
were recorded, but total coral cover for all taxa were used to estimate climate stress for this study.
Coral growth and climate stress
Coral growth in the Indo-Pacific was modeled using two logistic growth functions (for
Pocillopora and Porites) parameterized from empirical data (Figure S1) from French Polynesia
(Adjeroud et al., 2009)and the Great Barrier Reef (Halford et al., 2004). Although competition
between the two taxa was not explicitly included, parameterization of the growth functions included
competition implicitly via the reduced carrying capacity (K) of Porites in the presence of faster
growing branching corals. Coral growth in the Caribbean was quantified using taxa specific lateral
extension rates (for Montastraea annularis, Porites astreoides and Agaricia agaricites),
parameterized from empirical data (Chornesky & Peters, 1987, Highsmith et al., 1983, Huston, 1985,
Maguire & Porter, 1977, Van Moorsel, 1988) and developed for a previously published and tested
coral reef simulation model (Mumby, 2006, Mumby et al., 2006). A version of this simulation model
was used to create annual coral cover recovery matrices for each of the three taxa, including their
competition; these recovery matrices were then used here to determine potential growth per taxa
per year.
In both the Indo-Pacific and Caribbean, coral cover per year (t) per taxa was estimated by
first determining the potential coral cover multiplied by the temperature and aragonite effects on
calcification (Eq. 1) and then by bleaching mortality (Eq. 2). Total coral cover is just the sum of the
taxa specific coral covers (from Eq. 3). Climate stress, used for the analysis here, is recorded as 100 –
the total percent (%) coral cover (Eq. 4).
Eq. 1: Coral covert, taxa = Coral Covert-1, taxa + (Potential coral covert, taxa x Total relative calcificationt, taxa)
Eq. 2: Coral covert, taxa, final = Coral covert, taxa – (Coral covert, taxa x Bleaching mortalityt, taxa)
n
Eq. 3: Coral covert, total =

Coral covert, taxa, final
taxa1
Eq. 4: Climate stresst = 100 - Coral covert, total
Total Relative Calcification
Total relative calcification (Eq. 1) ranges between 0 and 1 and has two components: first is
the relationship between coral community calcification and aragonite saturations state (Ωar)
described by Langdon et al. (2000) (Eq. 5).
Eq. 5: Langdon relative calcification (OAReduc) = −0.2647 + (0.2758 × Ωar)
Coral community calcification has been predicted to decline by 11-44% (Chan & Connolly, 2013,
Langdon et al., 2000, Leclercq et al., 2002) in the next 100 years, or an average of 32% due to the
decrease in CO3 (Langdon et al., 2000); second is the Gaussian response of coral calcification to
temperature change (Kemp et al., 2011, Vaughan, 1916). Coral growth typically peaks at an optimum
temperature, declining both below and above the optimum (Carricart-Ganivet et al., 2012, Jokiel &
Coles, 1977, Marshall & Clode, 2004). The optimum temperature to which corals are acclimated
varies according to the ambient temperature of the coral’s environment (Marshall & Clode, 2004).
The relationship between SST and relative calcification is defined as (Eq. 6):
Eq. 6: Temperature relative calcification (TempReduc)= 𝐴 x 𝑒
(−0.5x
𝑆𝑆𝑇−𝑇𝑜𝑝𝑡 2
)
𝑠𝑑𝑅𝑒𝑙𝐶𝑎𝑙𝑐
Where A is 1 (amplitude of curve), 0.5 is the standard for hump-shaped functions, SST is the monthly
sea surface temperature for a given year and Topt is the temperature of optimal calcification and
sdRelCalc is the standard deviation of the optimal temperature curve. Here, the optimal SST is
defined as the monthly pre-industrial climatology (1865-1875). Response to the same temperature
change varies between taxa (Carricart-Ganivet et al., 2012) (Figure S2), with relationships derived
from calcification data for Montastraea faveolata (Carricart-Ganivet, 2004) in the Caribbean and
Porites (Lough & Barnes, 2000) and Pocillipora in the Indo-Pacific (Marshall & Clode, 2004). In the
Caribbean, the relationship derived for M. faveolata was also applied to Porites astreoides and
Agaricia agaricites.
For the model, these two separate components of calcification effects are combined, additively, to
estimate total relative calcification (Eq. 7):
Eq. 7: Total relative calcification = 1 – (1 – OAReduc) – (1 - TempReduc)
Bleaching mortality
Because DHMs were expressed as a probability (probability of DHM = 1,2,3,….,8 per year;
see “Sea surface temperature and bleaching stress” section above), a random number (0 to 1) was
generated in the model for each year, each simulation to determine which DHM to apply. The
highest DHM that had a probability exceeding the random number was selected for a given year simulation combination. 1,000 simulations were performed and median annual coral cover values
were calculated to use in the analysis.
Based on the DHM, taxa specific bleaching mortality was applied (see Eq. 2) annually in the
model (Table S1). In the Indo-Pacific, the greater susceptibility of fast growing, branching corals to
thermal stress than slow growing, massive taxa, has been well established (Loya et al., 2001,
Marshall & Baird, 2000, McClanahan et al., 2007, van Woesik et al., 2004). , Differences have also
been observed among the massive taxa modeled in the Caribbean (Smith et al., 2013). The mortality
estimates used here (Table S1) were based on empirical data from both basins, including French
Polynesia(Mumby et al., 2001), Belize(McField, 1999) and the wider Caribbean (Eakin et al., 2010).
Emissions and population statistics
National emissions data were downloaded from the Carbon Dioxide Information Analysis
Center (CDIAC) during December 2012 (http://cdiac.ornl.gov/trends/emis/tre_coun.html). These
data represent total emissions from fossil-fuel burning, cement manufacture and gas flaring and are
expressed in metric tons of carbon converted here into units of CO2 by multiplying by 3.667. Details
of how these estimates were calculated can be found at the above website and in (Boden et al.
(2012)). National data were compiled annually for the years 1980 – 2010.
National population data were downloaded from the U.S. Energy Information Administration
(EIA) during December 2012 (http://www.eia.gov) and compiled for year 2010.
National per capita emissions used in this study were calculated by dividing the total 1980 –
2010 emissions by the population in 2010 (Table S3). Therefore, our estimate captures both
historical and present emissions but is expressed in reference to the current (2010) population. We
reasoned that the present population has benefited from past emissions (standard of living,
economic opportunities, etc), but will also experience the negative effects of these emissions on
their reef resources in the near future (2010 – 2030).
The total global carbon dioxide emissions for 1980 – 2010 were estimated to be 720,737.88
million metric tons. The global population in 2010 was estimated to be 6,853.02 million people.
Therefore, the global mean per capita cumulative emissions was estimated to be 105.17 metric tons
of CO2.
Pollution equity index
Equity in coral reef climate stress was calculated for each country's Exclusive Economic Zone
(EEZ) as follows,
Equitycountry 
Stress E , country
Stress P , country
where the predicted stress in a country, StressP, country, is given by the ecological model as
Stress P , country  100  C year , country ,
and the expected stress in a country, StressE, country, is given by
Stress E ,country 
Emissions country
Emissions global
 Stress global
where Cyear is the predicted mean cover of coral for the years 2010 - 2030, Emissionscountry is the per
capita GHG emissions of that country from 1980-2010, Emissionsglobal are the global average per
capita GHG emissions from 1980-2010 (105.17 metric tons), and Stressglobal is the global average of
predicted mean stress from 2010 – 2030 (estimated to be 60.3 based on all 115 EEZs examined in
this study).
For visual display purposes, equity values < 1 were transformed,
1
 1
Equity
so that ‘losers’ were expressed on a negative scale but with a comparable range in values as
‘winners', (i.e. an equity of 0.5, such that predicted stress is double that of expected stress,
transforms to -2 which contrasts with an equity of +2 where predicted stress is half that
expected).
So, using Barbados as an example with results from this study:
Stress E , Barbados 
144.92 Barbados
 60.3( Stress global )  83.09
105.17 global
Equity Barbados 
83.09 E , Barbados
38.58 P , Barbados
 2.15
Using Fiji as another example:
Stress E , Fiji 
49.97 Fiji
105.17 global
Equity Fiji 
 60.3( Stress global )  28.65
28.65 E , Fiji
72.49 P , Fiji
 0.40
Because < 1, transformed:
1
 1  2.50
0.40
In summary, the pollution equity index for Barbados is calculated to be 2.15 and for Fiji, -2.50.
Pollution equity indices for all the 92 EEZs with sufficient emissions and population data were
calculated.
Additional results and discussion
Global variability in the physiological responses (relative calcification from Eq. 5) of corals to
climate change is much lower than when the ecological impacts of bleaching mortality and recovery
are included in calculations of stress (Figure S6; Figures S4-S5). Stress variability had a bell curve
response through time. For the first 75 to 100 years, when modeled reefs are healthy, they have low
variability; then, due to different rates of decline, reefs go through 30 years of high variability (2000
– 2030); finally, variability rapidly decreases as reefs became uniformly stressed (2030 – 2050). In
contrast, calcification variability remains low and stable through time. Also, the magnitude of
change from the years 2000 to 2050 is far less severe with calcification than with stress and timing of
this change is later in the time series (compare Figures S4-S5). Notably, the Northwest Atlantic does
not lag behind the rest of the globe with changes in calcification as it does with stress and the northsouth differences in this region are far less extreme. Non-linear ecological processes mediate
climate change impacts on reefs during much of the time series, but then accentuate these impacts
by the end of the time series serving as a reminder to view the ever increasing number of climate
projections with an ecological filter; for it is the changes to ecosystems that will ultimately
determine the nature of the world we will inhabit in the future (Schmitz et al., 2003, Walther et al.,
2002).
Not surprisingly, the greatest inequity will occur on the reefs of the world’s poorer nations,
particularly those in the western Indian Ocean. The mean equity index of the 16 UN listed Least
Developed Countries (LDC) included here is far lower (-22.8) than the 67 non-LDC nations (0.34).
Also not surprising, the biggest winners in the Indo- Pacific are the United States (Hawaii) and
Australia. More unexpected are the results for the Northwest Atlantic: the wealthier islands of the
Caribbean have the highest positive equity indices found in this study. These results can partly be
explained by the relatively low levels of climate stress found here, particularly on the northern reefs,
and partly by the exceptionally high per capita emissions from some of these islands.
The principle of equal per capita emission entitlements has been argued as an equitable
foundation for climate treaty negotiations (Baer et al., 2000, Chakravarty et al., 2009) and is the
reason that per capita emissions were used in our equity index. However, our results show this
approach will present some particular challenges for small island countries. Small Island Developing
States (SIDS) contributed only 1.2% of the total 1980 – 2010 CO2 emissions of all countries included
here, yet had nearly triple the equity index (-2.2) of non-SIDS (-6.1; supplemental information, Table
S3); even when comparing just LDC nations, SIDs had almost double the equity index (-17.4) of nonSIDs (-28.2). Energy production on islands, particularly SIDS, is constrained by the challenges of scale
and remoteness, resulting in higher per capita emissions than their narrow, resource based, often
volatile economies would suggest (Weisser, 2004).
Comparison of empirical versus GCMs on climate stress and pollution equity results
During the analysis we debated the relative merits of using empirical (e.g. HadISST or NOAA
OISST/ERSST ) versus modelled GCM SST and thermal stress (bleaching) data (described above)for
the historical time frame (1870 – 2010). In the end, we chose to use historical model data for the
following reasons:
1. Our pollution equity results are based on the impacts of mostly predicted future
climate stress (years 2010 - 2030). Mixing known, deterministic bleaching events
(e.g. 1998, 2005, 2010) from empirical data with modelled probabilistic bleaching
predictions for the future poses some interpretation issues. The results of our coral
trajectory modelling would be effected by the magnitude and distribution these
recent events partially masking the effects of future predictions. In other words, we
were concerned that the relatively few bleaching events that have occurred would
be weighted too heavily relative to the more frequent future events predicted to
occur. In summary, although the use of empirical data will provide more accurate
estimates of climate stress to date, its inclusion may have inconsistent influences on
the future predictions we're trying to capture.
2. We were concerned that if we used models for the future only, some readers may
wrongly assume that we were disguising the model's overall performance to capture
historical conditions. In other words, we believe it is instructive for the reader to
interpret the future ramping of climate stress in the context of overall model
performance which demonstrates the model isn't biased. We believe the relative
severity of modelled future stress is more convincing and interpretable when
juxtaposed to the relative mild historical stress demonstrated by the same models.
3. The aragonite saturation state model used in this research is based in part on the
same GCM temperature data we use in our analysis. Combining model aragonite
saturation state with empirical SST would inappropriately decouple this interaction
to some degree. Unfortunately, there are no available global empirical datasets for
aragonite saturation state (though NOAA are working on this).
The advantage of using empirical data is they more accurately capture the timing and
magnitude of known bleaching events. Despite our arguments above, we recognize that some
readers might be uncomfortable with our purely modelling approach and could be concerned that
our climate stress results and thus the pollution equity indices would be quite different had we used
available empirical data.
To address these concerns, we ran a separate analysis using Hadley Centre Sea Ice and Sea
Surface Temperature data set (HadISST1), a widely vetted and trusted empirical global SST time
series (Rayner et al., 2003) that has been used for coral bleaching research (Donner, 2011). Monthly
HadISST has a spatial resolution of 1° x 1° degree and spans the years 1870 – 2010. The SST obtained
at this resolution was linearly interpolated to produce monthly results at 0.5° x 0.5° degree
resolution for use by the coral reef trajectory model and to match the resolution of the GCM
modelled SST and aragonite saturation state (described above). Bleaching stress was estimated
from the accumulation of degree heating months (DHM) using a similar method described above for
the model. The sole difference was that maximum DHM in each year was calculated as a definitive
value based on the accumulations for that year (instead of expressed as a probability based on a tenyear running period surrounding a given year from all available model runs in the modelling case).
The HadISST SST and DHM data were used instead of the model data for the years 1870 –
2010, but all other data remained the same as in the purely model run described above and in the
paper (aragonite saturation state, GCM model SST and DHM for 2011 – 2050). The coral reef
trajectory model was run identically, using all the same parameters (described above), and climate
stress and equity index values were calculated in the same way for each EEZ (Figure S7, Tables S4 &
S5).
The overall geographic patterns between the two approaches were similar (compare Figure
S4 with S7) with consistently high stress found in the eastern and portions of the central Pacific and
overall lower stress in the Northwest Atlantic. Also, both approaches demonstrated a general
increase in stress with time during the comparable 1870 – 2010 period. The HadISST data
demonstrated more frequent high stress events than the model, which is expected given that the
model expresses DHM probabilistically while HadISST DHM is deterministic. Also expected, the
HadISST data (Figure S7) captures the timing of known bleaching events such as 1982 (eastern
Pacific), 1998, 2005 and 2010, which the model does not (Figure S4). However, substituting model
GCMS with HadISST had a generally minor impact on the mean 2010-2030 EEZ climate stress results
used in our pollution equity index (Table S4 & S5). The climate stress values (for all 115 EEZs)
calculated using the two approaches were very highly correlated (Pearson’s r = 0.968; p <
0.0001)(Table S4). The correlation of the pollution equity indices (for the 92 EEZs with sufficient
data to calculate equity) using the two approaches was even higher (Pearson’s r = 0.999; p <
0.0001)(Table S5).
The HadISST approach resulted in a slightly higher overall 2010 – 2030 mean climate stress
(63.4) than the GCMs approach (60.3). When HadISST stress results are expressed as percent
change relative to GCMs results, the mean change is an increase of 6.4%; 80 of the 115 EEZs
increased, 32 decreased and 3 showed no change (Table S4). Although all ocean basins
demonstrated a mean increase with HadISST, it ranged from only 1.6% for the eastern Pacific to
10.5% for the Northwest Atlantic (Table S4). Most of the changes, either increases or decreases, for
individual EEZs were minor: 57 changed by less than 5%, 80 by less than 10%, and 96 by less than
15% (Table S4).
The HadISST approach had even less impact on the equity indices than it did on the climate
stress estimates (Table S5). The rank order of the 92 EEZs examined barely changed using the two
approaches (Spearman’s rho = 0.998; p < 0.0001), and the overall mean index change of the HadISST
approach relative to the GCMs approach was only a decrease of 1.7%. The Northwest Atlantic
demonstrated the largest change, with a mean equity index decrease of 4.4% (2.98 for GCMs versus
2.64 with HadISST) while the eastern Pacific had the largest mean increase of 2.3% (-4.72 for GCMs
versus -4.60 for HadISST) (Table S5).
In conclusion, swapping GCMs with HadISST demonstrated only minor differences with our
study results; further, these small differences would have no impact on our paper’s analytical
interpretations and conclusions.
Figure S1: Logistic growth functions used to model coral reef trajectories in the Indo-Pacific. Two
taxa were included in the Indo-Pacific model, the fast growing, ubiquitous, branching Pocillipora and
the slower growing, massive, Porites.
Figure S2: Gaussian response of coral calcification to changing sea surface temperature. Optimum
sea surface temperature (SST) was assumed to be the pre-industrial climatology (years 1865 – 1875).
Delta SST is calculated as the difference between the optimum SST and the SST for each year
examined. Individual responses for both taxa (Pocillipora and Porites) in the Indo-Pacific were used
in the model. In the Caribbean, the response of Montastraea faveolata was used for all three taxa
modeled there.
Figure S3: Label, geographic location and ocean basin group for each of the 115 EEZs used in this
analysis. These labels refer to the matrix row numbers used in Figures 2 and S4-S5. Also see lookup
table (Table S2) for identification and additional information for each EEZ. a, shows all EEZs and b,
shows a detailed view of the Northwest Atlantic.
a
b
Climate stress
Figure S4: Annual climate stress per EEZ. Same data and sorting as in Figure 2, but without the row
height scaling. Stress is calculated as shown in Eq. 4 in the supplemental information. EEZ Labels on
right y-axis refer to EEZ ID (row) numbers for information listed in Table S2 and labels shown in
Figure S3. Ocean basins are as follows: Indian = Indian Ocean, SCS = South China and Eastern
Archipelagic Seas, W Pac = Western Pacific, C Pac = Central Pacific, E Pac = Eastern Pacific, NWA =
Northwest Atlantic / Caribbean. See Figure S3 for ocean basin delineation.
Figure S5: Annual relative calcification per EEZ. Sorting and labeling as in Figure S4. Calcification is
calculated as shown in Eq. 7 in the supplemental information, which includes both effects of
aragonite saturation state and taxa dependent temperature responses. Relative calcification is
multiplied by 100 for this figure.
Figure S6: Comparison of annual global variability for climate stress and calcification. Summarizes
data presented in Figures S4 & S5, showing coefficient of variation for both stress and calcification
through time. N = 102 (the 13 ENSO effected EEZs have been removed).
Climate stress
Figure S7: Annual climate stress per EEZ using HadISST for 1875 - 2010. Same sorting as in Figure
S4, but SST and bleaching (DHM) values from GCM models (described above and used in the
manuscript) were replaced for the years 1875 – 2010 with HadISST data. All other data remained the
same and the coral reef trajectory was run identically. Stress is calculated as shown in Eq. 4 in the
supplemental information. EEZ Labels on right y-axis refer to EEZ ID (row) numbers for information
listed in Table S2 and labels shown in Figure S3. Ocean basins are as follows: Indian = Indian Ocean,
SCS = South China and Eastern Archipelagic Seas, W Pac = Western Pacific, C Pac = Central Pacific, E
Pac = Eastern Pacific, NWA = Northwest Atlantic / Caribbean. See Figure S3 for ocean basin
delineation.
Table S1: Taxa specific bleaching mortality estimates used in the coral trajectory model. Mortality
increases with thermal stress severity (DHM), but at different rates for each taxa.
Degree heating months (DHM)
2
3
4
5
6
7
8
Caribbean
Porites asteroides
Montastrea annularis
Agaricia spp.
0
0.046
0.172
0.046
0.301
0.428
0.301
0.550
0.664
0.550
0.768
0.856
0.768
0.926
0.975
0.926
0.999
0.994
0.999
0.999
0.999
0.5
0
0.95
0
0.99
0.25
0.99
0.75
0.99
0.95
0.99
0.95
0.99
0.95
Indo-Pacific
Pocillopora
Porites spp.
Table S2: Detailed climate stress information for each EEZ. The EEZ ID corresponds to the row
numbers used in Figures 2 & S4-S5 and to the labels used in Figure S3. Also provided are reef area
(km2), whether the EEZ belongs to UN designated Small Island Developing States (SIDS (S)) and/or
Least Developed Countries (LDC (L)), whether the EEZ is heavily influenced by ENSO events in this
analysis, the mean 1875 – 2050 climate stress, the maximum observed rate of climate stress
increase (using 5 year moving linear regression) and the last year of climate stress recovery; after
this year, climate stress continuously increased. Reef area per EEZ was estimated from UNEP World
Conservation Monitoring Centre Global Distribution of Coral Reefs (2010) (http://data.unepwcmc.org/datasets/1).
EEZ ID
Country
1
2
3
4
5
6
7
8
Indian Ocean
Oman
Yemen
Somalia
Myanmar
South Africa
Kenya
India
Sri Lanka
9
10
11
12
13
14
15
16
17
18
19
20
21
22
India
Djibouti
Maldives
France
France
Timor-Leste
France
Mozambique
Madagascar
France
France
Seychelles
France
Tanzania
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
EEZ
Reef Area
(km2)
500
1,054
647
1,631
46
562
1,611
591
United Kingdom
Mauritius
Comoros Islands
France
Australia
Australia
Oman
Yemen
Somalia
Myanmar
South Africa
Kenya
India
Sri Lanka
Andaman and
Nicobar
Djibouti
Maldives
Ile Europa
Bassas da India
Timor-Leste
Juan de Nova Island
Mozambique
Madagascar
Réunion
Mayotte
Seychelles
Ile Tromelin
Tanzania
British Indian Ocean
Territory
Mauritius
Comoros Islands
Glorioso Islands
Christmas Island
Cocos Islands
South China and
Eastern Archipelagic
Seas
Taiwan
China
Disputed
Malaysia
Philippines
Cambodia
Thailand
Indonesia
Disputed
Vietnam
Taiwan
China
Paracel Islands
Malaysia
Philippines
Cambodia
Thailand
Indonesia
Spratly Islands
Vietnam
925
706
571
3,619
26,465
44
1,907
46,464
5,184
1,113
West Pacific
2,800
450
8,496
5
62
502
33
1,668
1,973
41
539
1,104
4
3,275
3,551
1,229
397
183
92
65
SIDS
/LDC
L
L
L
L
S
S,L
L
L
S
L
S
S,L
L
ENSO
Mean Stress
Maximum Rate
Last Year of
recovery
30.7
32.1
33.1
33.2
34.5
34.8
35.4
35.9
3.5
2.8
4.5
2.8
4.7
3.0
3.4
3.3
2024
2007
2024
2011
2027
2028
2023
2023
36.5
37.5
38.1
38.6
38.8
39
39.1
39.2
39.8
39.9
40.7
40.7
40.8
42.1
3.3
5.7
3.7
1.3
1.4
4.1
1.8
1.7
3.8
1.0
1.6
2.6
4.3
3.4
2006
2039
2024
2025
2026
2036
2024
2024
2039
2043
2028
2024
2009
2028
42.2
42.5
42.9
43.4
46
46.3
3.4
3.1
2.6
2.4
2.6
2.3
2050
2006
2028
2006
1990
1990
35.1
36.5
38
38.7
39
41.4
43.2
44
45.8
49
4.5
2.7
3.3
3.0
2.9
2.6
2.5
2.2
2.7
2.5
2022
2020
2021
2006
2007
2036
2020
2006
2009
2007
39
40
41
42
43
Disputed
Japan
Australia
France
Solomon Islands
44
45
46
47
48
United States
United States
Palau
Papua New Guinea
Micronesia
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
Central Pacific
United States
Samoa
United States
United States
Vanuatu
France
Fiji
New Zealand
Tuvalu
New Zealand
Tonga
France
United States
New Zealand
Marshall Islands
United States
Kiribati
Kiribati
Nauru
Kiribati
United States
United States
71
72
73
74
75
76
77
78
79
80
East Pacific
United Kingdom
Mexico
Guatemala
France
Ecuador
Ecuador
Colombia
Panama
Nicaragua
Costa Rica
81
82
83
84
85
Northwest Atlantic
United Kingdom
Mexico
United States
Bahamas
Cuba
86
87
88
89
United Kingdom
United Kingdom
United Kingdom
Trinidad and Tobago
Conflict Zone
Japan
Australia
New Caledonia
Solomon Islands
Northern Mariana
Islands
Guam
Palau
Papua New Guinea
Micronesia
9
2,521
43,893
5,473
5,443
31
201
1,072
14,097
4,028
Hawaii
Samoa
Wake Island
American Samoa
Vanuatu
Wallis and Futuna
Fiji
Cook Islands
Tuvalu
Niue
Tonga
French Polynesia
Johnston Atoll
Tokelau
Marshall Islands
Palmyra Atoll
Line Group
Phoenix Group
Nauru
Kiribati
Jarvis Island
Howland Island and
Baker Island
1,024
459
30
192
3,718
397
8,674
1,075
818
163
1,419
5,270
87
209
5,544
124
460
324
29
1,758
10
Pitcairn
Mexico (Pacific)
Guatemala (Pacific)
Clipperton Island
Ecuador
Galapagos Islands
Colombia (Pacific)
Panama (Pacific)
Nicaragua (Pacific)
Costa Rica (Pacific)
42
342
Bermuda
Mexico (Caribbean)
United States
Bahamas
Cuba
Turks and Caicos
Islands
Cayman Islands
British Virgin Islands
Trinidad and Tobago
337
1,215
1,122
2,777
2,818
S
S,L
31.3
35.8
37.4
38.5
39.2
3.8
2.7
2.0
1.0
2.8
2023
1980
2002
2040
2026
S
S
S
S
39.7
40.9
41.7
44.4
47.5
3.1
3.4
3.6
2.2
2.3
1983
1990
1975
1983
1980
x
x
x
x
x
x
37.4
37.4
37.5
37.8
38.5
38.7
40.2
40.5
40.7
40.7
41.1
42
42.4
43
44.2
61.5
67.6
72.4
76.3
78.7
86.8
2.7
2.8
4.0
2.2
3.8
2.7
3.4
2.5
2.2
2.9
2.4
2.5
4.4
1.1
4.6
0.5
0.4
0.1
0.1
0.1
0.0
2017
1990
2032
1997
2013
1981
2002
2006
2029
1997
2018
2006
2005
2028
2029
2031
2050
2050
2050
2050
2050
x
88.4
0.0
2049
x
x
x
x
x
x
37.8
43.6
45.1
49.9
62.3
64.3
64.8
65.1
66.1
66.3
1.1
3.0
2.1
3.0
0.5
0.3
1.7
1.6
1.7
1.8
2041
2004
1999
2036
2039
2039
2038
2047
2050
2035
S
S
40
42
42
42
42.9
3.2
2.2
2.6
2.5
2.7
2003
1997
1989
2032
2014
S
S
42.9
44
45.4
45.6
1.9
2.2
2.8
2.6
2045
2017
2014
2037
S,L
S
S,L
S
S
S,L
S
S
S
S,L
S,L
S
S,L
82
17
29
40
152
844
2,014
228
479
88
90
91
92
93
94
95
96
Dominica
France
Barbados
Dominican Republic
United States
Saint Lucia
United Kingdom
97
98
99
100
101
102
Antigua and Barbuda
France
Belize
United States
Haiti
Honduras
103
104
Netherlands
United Kingdom
105
106
107
108
109
110
France
Saint Kitts and Nevis
Jamaica
Saint Vincent and the
Grenadines
Panama
Joint Regime
111
112
113
114
Nicaragua
Netherlands
Venezuela
Grenada
115
Colombia
Dominica
Martinique
Barbados
Dominican Republic
Puerto Rico
Saint Lucia
Anguilla
Antigua and
Barbuda
Guadeloupe
Belize
US Virgin Islands
Haiti
Honduras
Southern SaintMartin
Montserrat
Northern SaintMartin
Saint Kitts and Nevis
Jamaica
Saint Vincent and
the Grenadines
Panama (Caribbean)
Colombia - Jamaica
Nicaragua
(Caribbean)
Netherlands Antilles
Venezuela
Grenada
Colombia
(Caribbean)
52
609
104
579
2,234
101
20
S
224
350
1,140
305
483
825
S
S
S
S
S
S
S
S
S,L
45.9
46
46
46.1
46.1
46.3
46.3
3.4
3.4
3.7
3.4
2.9
3.4
3.2
2014
2014
2036
2017
2014
2036
2014
46.3
46.4
46.4
46.5
46.7
47
3.2
3.4
4.3
3.3
3.6
4.1
2014
2014
2014
2014
2017
2017
117
37
S
S
47
47.1
3.2
3.5
2014
2015
77
145
1,191
S
S
47.1
47.2
47.4
3.2
3.8
3.8
2017
2016
2017
47.7
48
49.2
3.9
3.8
4.5
2036
2017
2045
50.5
51
51.8
51.8
3.8
3.8
3.2
3.9
2014
2022
2017
2048
51.9
3.4
2017
148
611
141
528
358
492
140
2,466
S
S
Table S3: Detailed emissions and climate stress equity information for each EEZ. Data are sorted in
the same order as displayed in Figure 3a. Also shown: whether the EEZ belongs to Small Island
Developing States (SIDS); the equity index; mean climate stress for 2010 – 2030; total CO2 emissions
for the years 1980 – 2010 (millions of metric tons); 2010 population (millions) and per capita
emissions (total 1980-2010 emissions / 2010 population). See methods for details of how index was
calculated. 92 of the 115 EEZs examined had sufficient emissions and population data to calculate
equity indices.
Country
Indian Ocean
Tanzania
Madagascar
Mozambique
Comoro Islands
Somalia
East Timor
Myanmar
Kenya
Sri Lanka
Yemen
India
Maldives
Mauritius
France
Djibouti
Seychelles
South Africa
Oman
South China and Eastern
Archipelagic Seas
Cambodia
Vietnam
Philippines
Indonesia
Thailand
China
Malaysia
Taiwan
EEZ
Tanzania
Madagascar
Mozambique
Comoro Islands
Somalia
East Timor
Myanmar
Kenya
Sri Lanka
Yemen
India
Maldives
Mauritius
Réunion
Djibouti
Seychelles
South Africa
Oman
SIDS
y
y
y
y
y
Cambodia
Vietnam
Philippines
Indonesia
Thailand
China
Malaysia
Taiwan
Equity
Index
Mean Stress
Total CO2
Emissions
Population
2010
Per Capita
Emissions
-53.0
-51.4
-48.4
-41.9
-23.5
-20.6
-14.0
-13.9
-7.1
-3.7
-3.7
-3.4
-2.1
-1.7
-1.6
1.9
2.7
3.8
74.2
70.5
68.7
76.7
36.0
65.0
36.6
47.4
49.3
37.1
46.8
57.9
72.8
71.8
60.1
63.6
47.8
30.2
102.42
50.92
54.57
2.47
27.05
6.38
243.01
237.71
259.11
405.88
25,800.99
11.73
78.90
56.15
48.53
18.49
11,064.60
595.73
41.8929
21.2818
22.0615
0.7734
10.1125
1.1546
53.4144
40.0466
21.5140
23.4954
1,173.1080
0.3957
1.2941
0.7662
0.7405
0.0883
49.1091
2.9677
2.44
2.39
2.47
3.19
2.68
5.52
4.55
5.94
12.04
17.27
21.99
29.65
60.97
73.29
65.54
209.29
225.31
200.74
-30.0
-9.1
-6.2
-4.5
-1.8
-1.0
1.0
3.6
61.6
74.7
60.5
73.5
66.4
44.8
59.0
39.6
51.77
1,289.94
1,698.19
6,901.31
4,263.87
103,600.77
2,912.06
5,665.79
14.4537
89.5711
99.9002
242.9683
67.0895
1,330.1413
28.2747
23.0250
3.58
14.40
17.00
28.40
63.55
77.89
102.99
246.07
West Pacific Ocean
Solomon Islands
Micronesia
Papua New Guinea
Palau
France
United States
Japan
Australia
Solomon Islands
Micronesia
Papua New Guinea
Palau
New Caledonia
Guam
Japan
Australia
y
y
y
y
y
y
-12.2
-10.1
-9.1
1.8
2.1
3.1
3.1
4.5
68.5
84.0
78.3
79.2
64.3
79.5
49.0
56.8
5.50
1.61
91.19
5.19
59.91
76.58
33,607.28
9,588.42
0.5592
0.1111
6.0645
0.0205
0.2524
0.1809
126.8044
21.5158
9.84
14.50
15.04
253.28
237.41
423.41
265.03
445.65
Central Pacific Ocean
Kiribati
Kiribati
Kiribati
Tuvalu
Vanuatu
Samoa
Tonga
Kiribati
Phoenix Group
Line Group
Tuvalu
Vanuatu
Samoa
Tonga
y
y
y
y
y
y
y
-19.8
-19.6
-18.7
-14.8
-8.2
-4.8
-4.6
96.8
95.7
91.2
69.3
64.9
59.3
71.4
0.85
0.85
0.85
0.09
3.05
4.17
3.30
0.0995
0.0995
0.0995
0.0105
0.2216
0.1920
0.1226
8.52
8.52
8.52
8.19
13.78
21.73
26.93
France
Marshall Islands
Fiji
New Zealand
France
New Zealand
United States
Nauru
United States
East Pacific Ocean
Nicaragua
Guatemala
Costa Rica
Colombia
Ecuador
Ecuador
Panama
Mexico
Northwest Atlantic
Ocean
Haiti
Nicaragua
Honduras
Colombia
Grenada
Saint Vincent and the
Grenadines
Dominica
Dominican Republic
United Kingdom
Saint Lucia
Belize
Saint Kitts and Nevis
United Kingdom
Panama
United Kingdom
Jamaica
Venezuela
Cuba
France
Mexico
France
Barbados
Antigua and Barbuda
United Kingdom
United States
United Kingdom
United Kingdom
Bahamas
Trinidad and Tobago
United States
Netherlands
Netherlands
France
United States
Wallis and Futuna
Marshall Islands
Fiji
Niue
French Polynesia
Cook Islands
American Samoa
Nauru
Hawaii
y
y
y
y
y
y
y
Nicaragua (Pacific)
Guatemala (Pacific)
Costa Rica (Pacific)
Colombia (Pacific)
Ecuador
Galapagos Islands
Panama (Pacific)
Mexico (Pacific)
Haiti
Nicaragua (Caribbean)
Honduras
Colombia (Caribbean)
Grenada
Saint Vincent and the
Grenadines
Dominica
Dominican Republic
Turks and Caicos
Islands
Saint Lucia
Belize
Saint Kitts and Nevis
Anguilla
Panama (Caribbean)
British Virgin Islands
Jamaica
Venezuela
Cuba
Guadeloupe
Mexico (Caribbean)
Martinique
Barbados
Antigua and Barbuda
Cayman Islands
Puerto Rico
Montserrat
Bermuda
Bahamas
Trinidad and Tobago
United States
Netherlands Antilles
Southern Saint-Martin
Northern Saint-Martin
US Virgin Islands
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
-3.8
-3.7
-2.5
-2.5
-1.3
1.5
2.4
3.2
5.7
65.5
78.2
72.5
71.6
64.2
66.8
63.4
96.6
53.0
0.46
2.00
43.78
0.11
24.79
2.03
17.36
4.95
164,196.21
0.0153
0.0540
0.8760
0.0022
0.2910
0.0115
0.0664
0.0093
310.2329
30.10
36.93
49.97
50.19
85.19
176.98
261.34
533.64
529.27
-10.1
-7.6
-5.5
-4.3
-3.7
-3.6
-1.6
-1.4
91.1
69.6
91.0
92.7
83.2
83.0
93.7
73.4
94.63
216.59
131.12
1,656.58
587.27
587.27
357.81
10,563.98
5.9959
13.5504
4.5162
44.2053
14.7906
14.7906
3.4107
112.4689
15.78
15.98
29.03
37.47
39.71
39.71
104.91
93.93
-18.0
-5.9
-4.3
-2.8
-2.1
-2.0
41.2
53.8
39.7
60.8
56.0
38.57
94.63
127.97
1,656.58
5.04
9.6489
5.9959
7.9894
44.2053
0.1078
4.00
15.78
16.02
37.47
46.74
42.6
36.8
39.2
3.89
2.39
377.80
0.1042
0.0728
9.8238
37.33
32.81
38.46
34.4
38.0
38.5
41.8
39.7
49.7
37.5
43.7
58.1
31.9
39.6
31.3
37.0
38.6
40.3
33.9
39.1
41.1
28.9
31.1
41.0
31.6
58.6
40.9
42.0
39.8
0.81
7.35
15.61
3.49
1.01
357.81
1.99
279.98
3,872.99
983.62
48.52
10,563.98
51.93
41.40
14.96
8.93
865.33
1.56
18.33
109.52
798.47
164,196.21
315.53
50.51
45.46
334.17
0.0235
0.1609
0.3145
0.0499
0.0151
3.4107
0.0249
2.8472
27.2232
11.4775
0.4445
112.4689
0.4260
0.2857
0.0868
0.0502
3.9787
0.0051
0.0683
0.3104
1.2287
310.2329
0.2627
0.0421
0.0368
0.1098
34.51
45.65
49.63
70.03
66.68
104.91
79.87
98.33
142.27
85.70
109.16
93.93
121.91
144.92
172.48
177.83
217.49
305.19
268.52
352.80
649.86
529.27
1201.09
1201.09
1234.45
3044.79
-2.0
-1.8
-1.7
-1.5
-1.4
-1.0
-1.0
1.2
1.2
1.3
1.4
1.5
1.6
1.7
1.9
2.2
2.5
3.0
3.2
4.3
5.3
6.5
9.1
9.6
11.8
16.8
16.8
43.9
Table S4: Comparison of climate stress results using GCMs versus HadISST. As described above, a
separate analysis was conducted that replaced the GCMs derived SST and DHM data with HadISST
SST and DHM for the historical years 1870 – 2010. Coral trajectory models were run again, with
these replaced data, for the full 1870 – 2050 time series. GCMs stress represents the mean EEZ
climate stress for the years 2010 – 2030 used in the equity index calculations for the paper and
described in Table S3. HadISST stress represents the mean EEZ climate stress for 2010 – 2030 for the
HadISST model runs. The results of both outputs are ranked from lowest to highest stress. Also
shown is the percent change of HadISST climate stress values relative to the original GCMs values
used in the paper. Climate stress from the two approaches were very highly correlated (Pearson’s r
= 0.968; p < 0.0001). The EEZ ID corresponds to the row numbers used in Figures 2, S4, S7 and to the
labels used in Figure S3
EEZ ID
Country
EEZ
GCMs Stress
GCMs Rank HadISST Stress
HadISST Rank
Percent
Change
Indian Ocean
1
Oman
Oman
30.2
3
38.9
11
28.7
2
Yemen
Yemen
37.1
14
44.7
21
20.5
3
Somalia
Somalia
36.0
10
38.3
10
6.4
4
Myanmar
Myanmar
36.6
11
38.0
9
3.9
5
South Africa
South Africa
47.8
38
46.4
25
-2.8
6
Kenya
Kenya
47.4
37
48.1
32
1.5
7
India
India
46.8
36
52.0
39
11.2
8
Sri Lanka
Sri Lanka
49.3
42
47.9
31
-2.7
9
India
Andaman and Nicobar
48.2
39
48.6
34
0.7
10
Djibouti
Djibouti
60.1
55
76.9
89
28.0
11
Maldives
Maldives
57.9
50
56.4
46
-2.6
12
France
Ile Europa
69.1
73
67.0
64
-3.1
13
France
Bassas da India
69.9
76
69.5
68
-0.6
14
East Timor
East Timor
65.0
64
63.7
55
-2.0
15
France
Juan de Nova Island
67.1
69
66.0
61
-1.6
16
Mozambique
Mozambique
68.7
72
68.6
67
-0.1
17
Madagascar
Madagascar
70.5
77
71.5
71
1.4
18
France
Réunion
71.8
82
77.0
90
7.3
19
France
Mayotte
71.5
79
67.1
65
-6.1
20
Seychelles
Seychelles
63.6
60
63.8
56
0.3
21
France
Ile Tromelin
67.7
70
78.0
93
15.3
22
Tanzania
Tanzania
74.2
89
70.1
70
-5.5
23
United Kingdom
British Indian Ocean
Territory
73.0
85
73.4
76
0.5
24
Mauritius
Mauritius
72.8
84
81.6
97
12.2
25
Comoros Islands
Comoros Islands
76.7
92
74.3
82
-3.1
26
France
Glorioso Islands
76.2
91
74.7
85
-2.0
27
Australia
Christmas Island
82.1
101
79.5
94
-3.2
28
Australia
Cocos Islands
79.2
96
75.0
86
-5.3
Taiwan
39.6
21
55.0
44
38.9
South China and Eastern
Archipelagic Seas
29
Taiwan
30
China
China
44.8
35
54.7
42
21.9
31
Disputed
Paracel Islands
54.7
47
61.9
52
13.2
32
Malaysia
Malaysia
59.0
53
59.1
48
0.2
33
Philippines
Philippines
60.5
56
66.2
63
9.3
34
Cambodia
Cambodia
61.6
58
59.1
47
-4.0
35
Thailand
Thailand
66.4
67
64.2
57
-3.4
36
Indonesia
Indonesia
73.5
88
72.1
72
-1.9
37
Disputed
Spratly Islands
71.6
81
70.1
69
-2.1
38
Vietnam
Vietnam
74.7
90
73.0
74
-2.3
West Pacific
39
Disputed
Conflict Zone
29.9
2
33.0
6
10.1
40
Japan
Japan
49.0
41
54.9
43
12.1
41
Australia
Australia
56.8
49
60.1
51
5.8
42
France
New Caledonia
64.3
62
66.2
62
2.9
43
Solomon Islands
Solomon Islands
68.5
71
77.9
92
13.7
44
United States
Northern Mariana Islands
73.0
86
79.6
95
9.0
45
United States
Guam
79.5
99
85.8
101
7.9
46
Palau
Palau
79.2
97
81.8
98
3.2
47
Papua New Guinea
Papua New Guinea
78.3
95
83.1
99
6.1
48
Micronesia
Micronesia
84.0
104
86.5
102
3.0
Central Pacific
49
United States
Hawaii
53.0
44
52.6
40
-0.7
50
Samoa
Samoa
59.3
54
74.4
83
25.5
51
United States
Wake Island
65.2
65
65.9
60
1.0
52
United States
American Samoa
63.4
59
74.4
84
17.5
53
Vanuatu
Vanuatu
64.9
63
73.5
77
13.3
54
France
Wallis and Futuna
65.5
66
77.6
91
18.6
55
Fiji
Fiji
72.5
83
76.7
87
5.9
56
New Zealand
Cook Islands
66.8
68
72.4
73
8.3
57
Tuvalu
Tuvalu
69.3
74
76.9
88
10.9
58
New Zealand
Niue
71.6
80
73.7
78
3.0
59
Tonga
Tonga
71.4
78
74.1
81
3.8
60
France
French Polynesia
64.2
61
64.7
58
0.9
61
United States
Johnston Atoll
78.0
93
73.8
79
-5.3
62
New Zealand
Tokelau
79.5
100
84.6
100
6.4
63
Marshall Islands
Marshall Islands
78.2
94
80.6
96
3.1
64
United States
Palmyra Atoll
87.9
105
89.9
105
2.3
65
Kiribati
Line Group
91.2
108
89.4
104
-2.0
66
Kiribati
Phoenix Group
95.7
111
95.7
109
0.0
67
Nauru
Nauru
96.6
112
95.8
110
-0.9
68
Kiribati
Kiribati
96.8
113
96.2
112
-0.5
69
United States
Jarvis Island
97.3
114
97.4
114
0.0
United States
Howland Island and Baker
Island
97.5
115
97.5
115
0.0
70
East Pacific
71
United Kingdom
Pitcairn
54.6
46
53.5
41
-2.1
72
Mexico
Mexico (Pacific)
73.4
87
73.1
75
-0.4
73
Guatemala
Guatemala (Pacific)
69.6
75
63.4
54
-9.0
74
France
Clipperton Island
79.3
98
74.0
80
-6.6
75
Ecuador
Ecuador
83.2
103
94.7
107
13.8
76
Ecuador
Galapagos Islands
83.0
102
88.9
103
7.1
77
Colombia
Colombia (Pacific)
92.7
109
96.8
113
4.4
78
Panama
Panama (Pacific)
93.7
110
95.2
108
1.5
79
Nicaragua
Nicaragua (Pacific)
91.1
107
95.8
111
5.2
80
Costa Rica
Costa Rica (Pacific)
91.0
106
93.1
106
2.3
Northwest Atlantic
81
United Kingdom
Bermuda
28.9
1
28.9
1
0.1
82
Mexico
Mexico (Caribbean)
31.3
5
32.8
5
4.8
83
United States
United States
31.6
6
32.4
4
2.6
84
Bahamas
Bahamas
31.1
4
30.8
2
-1.0
85
Cuba
Cuba
31.9
7
31.8
3
-0.2
86
United Kingdom
Turks and Caicos Islands
34.4
9
34.7
8
1.0
87
United Kingdom
Cayman Islands
33.9
8
33.8
7
-0.5
88
United Kingdom
British Virgin Islands
37.5
15
40.0
12
6.8
89
Trinidad and Tobago
Trinidad and Tobago
41.0
28
43.8
17
6.8
90
Dominica
Dominica
36.8
12
44.7
20
21.4
91
France
Martinique
37.0
13
46.7
26
26.3
92
Barbados
Barbados
38.6
18
49.6
36
28.5
93
Dominican Republic
Dominican Republic
39.2
20
41.1
13
4.8
94
United States
Puerto Rico
39.1
19
42.2
15
8.0
95
Saint Lucia
Saint Lucia
38.0
16
47.4
28
24.6
96
United Kingdom
Anguilla
39.7
24
44.1
19
11.2
97
Antigua and Barbuda
Antigua and Barbuda
40.3
26
46.1
24
14.6
98
France
Guadeloupe
39.6
22
47.8
30
20.6
99
Belize
Belize
38.5
17
43.0
16
11.6
100
United States
US Virgin Islands
39.8
25
46.1
23
15.9
101
Haiti
Haiti
41.2
30
43.8
18
6.3
102
Honduras
Honduras
39.7
23
41.4
14
4.2
103
Netherlands
Southern Saint-Martin
40.9
27
47.8
29
16.7
104
United Kingdom
Montserrat
41.1
29
46.7
27
13.6
105
France
Northern Saint-Martin
42.0
32
49.9
37
18.8
106
Saint Kitts and Nevis
Saint Kitts and Nevis
41.8
31
49.0
35
17.1
107
Jamaica
Jamaica
43.7
34
45.2
22
3.4
108
Saint Vincent and the
Grenadin
Saint Vincent and the
Grenadines
42.6
33
48.5
33
14.0
109
Panama
Panama (Caribbean)
49.7
43
65.5
59
31.9
110
Joint Regime
Colombia - Jamaica
48.5
40
50.3
38
3.9
111
Nicaragua
Nicaragua (Caribbean)
53.8
45
59.6
50
10.9
112
Netherlands
Netherlands Antilles
58.6
52
59.6
49
1.8
113
Venezuela
Venezuela
58.1
51
61.9
53
6.7
114
Grenada
Grenada
56.0
48
55.3
45
-1.3
115
Colombia
Colombia (Caribbean)
60.8
57
68.0
66
11.9
Table S5: Comparison of pollution equity indices using GCMs versus HadISST. The HadISST model
results were used to calculate equity indices for the 92 EEZs using the same method described above
and in the paper. These indices are compared with the original (GCM) indices used in our paper
analyses. Both indices are ranked from lowest (‘losers’) to highest (‘winners’) and results are sorted
as in Table S3. Also shown is the percent change of HadISST climate stress values relative to the
original GCMs values used in the paper. Equity indices from the two approaches were very highly
correlated (Pearson’s r = 0.999; p < 0.0001). Data are sorted in the same order as displayed in
Figures 4a and Table S3.
EEZ
GCMs Equity
Index
GCMs
Rank
HadISST
Equity Index
HadISST
Rank
Percent
Change
Tanzania
Tanzania
-53.0
1
-47.6
2
11.3
Madagascar
Madagascar
-51.4
2
-49.6
1
3.7
Mozambique
Mozambique
-48.4
3
-46.0
3
5.3
Comoro Islands
Comoro Islands
-41.9
4
-38.6
4
8.5
Somalia
Somalia
-23.5
6
-23.7
6
-1.2
East Timor
East Timor
-20.5
7
-19.1
7
7.3
Myanmar
Myanmar
-14.0
13
-13.9
13
1.2
Kenya
Kenya
-13.9
14
-13.4
14
3.5
Sri Lanka
Sri Lanka
-7.1
22
-6.6
21
8.0
Yemen
Yemen
-3.7
32
-4.3
28
-12.8
India
India
-3.7
33
-3.9
34
-5.4
Maldives
Maldives
-3.4
37
-3.2
37
8.0
Mauritius
Mauritius
-2.1
42
-2.2
42
-6.3
France
Réunion
-1.7
48
-1.7
47
-2.0
Djibouti
Djibouti
-1.6
49
-1.9
45
-17.8
Seychelles
Seychelles
1.9
68
2.0
70
4.8
South Africa
South Africa
2.7
74
2.9
76
8.1
Oman
Oman
3.8
81
3.1
79
-18.3
Cambodia
Cambodia
-30.0
5
-27.4
5
9.6
Vietnam
Vietnam
-9.1
19
-8.4
20
7.6
Philippines
Philippines
-6.2
23
-6.5
23
-3.8
Indonesia
Indonesia
-4.5
28
-4.2
32
7.2
Thailand
Thailand
-1.8
45
-1.7
49
8.8
China
China
-1.0
57
-1.2
55
-13.7
Malaysia
Malaysia
1.0
58
1.1
59
4.9
Taiwan
Taiwan
3.6
80
2.7
74
-24.3
Solomon Islands
-12.1
15
-13.1
15
-7.6
Country
Indian Ocean
South China and Eastern
Archipelagic Seas
West Pacific Ocean
Solomon Islands
Micronesia
Micronesia
-10.1
16
-9.9
17
2.1
Papua New Guinea
Papua New Guinea
-9.1
18
-9.2
18
-0.9
Palau
Palau
1.8
67
1.9
69
1.9
France
New Caledonia
2.1
70
2.2
72
2.2
United States
Guam
3.1
77
2.9
75
-6.2
Japan
Japan
3.1
76
3.0
77
-2.6
Australia
Australia
4.5
83
4.5
83
-0.6
Kiribati
Kiribati
-19.8
8
-18.7
8
5.7
Kiribati
Phoenix Group
-19.6
9
-18.6
9
5.1
Kiribati
Line Group
-18.7
10
-17.4
11
7.3
Tuvalu
Tuvalu
-14.8
12
-15.6
12
-5.2
Vanuatu
Vanuatu
-8.2
20
-8.8
19
-7.2
Samoa
Samoa
-4.8
26
-5.7
25
-16.2
Tonga
Tonga
-4.6
27
-4.6
27
1.3
France
Wallis and Futuna
-3.8
31
-4.3
31
-11.3
Marshall Islands
Marshall Islands
-3.7
34
-3.6
36
2.0
Fiji
Fiji
-2.5
40
-2.4
40
2.1
New Zealand
Niue
-2.5
39
-2.5
39
-0.7
France
French Polynesia
-1.3
54
-1.3
54
4.2
New Zealand
Cook Islands
1.5
63
1.5
64
-2.9
United States
American Samoa
2.4
72
2.1
71
-10.5
Nauru
Nauru
3.2
78
3.4
81
6.1
United States
Hawaii
5.7
85
6.1
85
5.9
Nicaragua
Nicaragua (Pacific)
-10.1
17
-10.1
16
0.0
Guatemala
Guatemala (Pacific)
-7.6
21
-6.6
22
15.5
Costa Rica
Costa Rica (Pacific)
-5.5
25
-5.3
26
2.8
Colombia
Colombia (Pacific)
-4.3
30
-4.3
29
0.7
Ecuador
Ecuador
-3.7
35
-4.0
33
-7.6
Ecuador
Galapagos Islands
-3.6
36
-3.7
35
-1.9
Panama
Panama (Pacific)
-1.6
50
-1.5
51
3.6
Mexico
Mexico (Pacific)
-1.4
52
-1.3
53
5.6
Haiti
Haiti
-18.0
11
-18.2
10
-1.1
Nicaragua
Nicaragua (Caribbean)
-5.9
24
-6.3
24
-5.2
Honduras
Honduras
-4.3
29
-4.3
30
0.9
Colombia
Colombia (Caribbean)
-2.8
38
-3.0
38
-6.0
Grenada
Grenada
-2.1
41
-2.0
44
6.5
Saint Vincent and the
Grenadines
Saint Vincent and the Grenadines
-2.0
43
-2.2
43
-7.8
Central Pacific Ocean
East Pacific Ocean
Northwest Atlantic Ocean
Dominica
Dominica
-2.0
44
-2.3
41
-13.4
Dominican Republic
Dominican Republic
-1.8
46
-1.8
46
0.3
United Kingdom
Turks and Caicos Islands
-1.7
47
-1.7
50
4.1
Saint Lucia
Saint Lucia
-1.5
51
-1.7
48
-15.6
Belize
Belize
-1.4
53
-1.4
52
-5.8
Saint Kitts and Nevis
Saint Kitts and Nevis
-1.0
55
-1.2
56
-10.2
United Kingdom
Anguilla
-1.0
56
-1.1
57
-5.5
Panama
Panama (Caribbean)
1.2
59
-1.0
58
-20.3
United Kingdom
British Virgin Islands
1.2
60
1.2
60
-1.6
Jamaica
Jamaica
1.3
61
1.3
61
1.7
Venezuela
Venezuela
1.4
62
1.4
63
-1.4
Cuba
Cuba
1.5
64
1.6
66
5.4
France
Guadeloupe
1.6
65
1.4
62
-12.8
Mexico
Mexico (Caribbean)
1.7
66
1.7
67
0.3
France
Martinique
1.9
69
1.6
65
-16.7
Barbados
Barbados
2.2
71
1.8
68
-18.2
Antigua and Barbuda
Antigua and Barbuda
2.5
73
2.3
73
-8.3
United Kingdom
Cayman Islands
3.0
75
3.2
80
5.6
United States
Puerto Rico
3.2
79
3.1
78
-2.6
United Kingdom
Montserrat
4.3
82
3.9
82
-7.4
United Kingdom
Bermuda
5.3
84
5.6
84
5.1
Bahamas
Bahamas
6.5
86
6.9
86
6.2
Trinidad and Tobago
Trinidad and Tobago
9.1
87
9.0
87
-1.6
United States
United States
9.6
88
9.9
88
2.5
Netherlands
Netherlands Antilles
11.8
89
12.1
89
3.3
Netherlands
Southern Saint-Martin
16.8
90
15.2
91
-9.9
France
Northern Saint-Martin
16.9
91
14.9
90
-11.5
United States
US Virgin Islands
43.9
92
39.9
92
-9.3
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