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The projected timing of climate departure from recent variability

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Abstract

Ecological and societal disruptions by modern climate change are critically determined by the time frame over which climates shift beyond historical analogues. Here we present a new index of the year when the projected mean climate of a given location moves to a state continuously outside the bounds of historical variability under alternative greenhouse gas emissions scenarios. Using 1860 to 2005 as the historical period, this index has a global mean of 2069 (±18 years s.d.) for near-surface air temperature under an emissions stabilization scenario and 2047 (±14 years s.d.) under a 'business-as-usual' scenario. Unprecedented climates will occur earliest in the tropics and among low-income countries, highlighting the vulnerability of global biodiversity and the limited governmental capacity to respond to the impacts of climate change. Our findings shed light on the urgency of mitigating greenhouse gas emissions if climates potentially harmful to biodiversity and society are to be prevented.
ARTICLE
doi:10.1038/nature12540
; The projected timing of climate departure
from recent variability
Camilo Mora
1
, Abby G. Frazier
1
, Ryan J. Longman
1
, Rachel S. Dacks
2
, Maya M. Walton
2,3
, Eric J. Tong
3,4
, Joseph J. Sanchez
1
,
Lauren R. Kaiser
1
, Yuko O. Stender
1,3
, James M. Anderson
2,3
, Christine M. Ambrosino
2,3
, Iria Fernandez-Silva
3,5
,
Louise M. Giuseffi
1
& Thomas W. Giambelluca
1
Ecological and societal disruptions by modern climate change are critically determined by the time frame over which
climates shift beyond historical analogues. Here we present a new index of the year when the projected mean climate of
a given location moves to a state continuously outside the bounds of historical variability under alternative greenhouse gas
emissions scenarios. Using 1860 to 2005 as the historical period, this index has a global mean of 2069 (618 years s.d.) for
near-surfaceairtemperature underanemissionsstabilizationscenarioand2047(614yearss.d.) underabusiness-as-usual’
scenario. Unprecedented climates will occur earliest in the tropics and among low-income countries, highlighting the
vulner ability of global biodiversity and the limited governmental capacity to respond to the impacts of c limate change.
Our findings shed light on the urgency of mitigating greenhouse gas emissions if climates potentially harmful to biodiversity
and society are to be prevented.
Climate is a primary driver of biological processes, operating from
individuals to ecosystems, and affects several aspects of human life.
Therefore, climates without modern precedents could cause large and
potentially serious impacts on ecological and social systems
1–5
. For
instance, species whose persistence is shaped by the climate can
respond by shifting their geographical ranges
4–7
, remaining in place
and adapting
5,8
, or becoming extinct
8–11
. Shifts in species distributions
and abundances can increase the risk of extinction
12
, alter community
structure
3
and disrupt ecological interactions and the functioning of
ecosystems. Changing climates could also affect the following: human
welfare, through changes in the supply of food
13
and water
14,15
; human
health
16
, through wider spread of infectious vector-borne diseases
17,18
,
through heat stress
19
and through mental illness
20
; the economy, through
changes in goods and services
21,22
; and national security as a result of
population shifts, heightened competition for natural resources, viol-
ent conflict and geopolitical instability
23
. Although most ecological and
social systems have the ability to adapt to a changing climate, the
magnitude of disruption in both ecosystems and societies will be
strongly determined by the time frames in which the climate will reach
unprecedented states
1,2
. Although several studies have documented the
areas on Earth where unprecedented climates is likely to occur in
response to ongoing greenhouse gas emissions
24,25
, our understanding
of climate change still lacks a precise indication of the time at which the
climate of a given location will shift wholly outside the range of his-
torical precedents.
To provide an indication of the projected timing of climate depar-
ture under alternative greenhouse gas emissions scenarios, we have
developed an index that determines the year when the values of a
given climatic variable exceed the bounds of historical variability
for a particular location (Fig. 1a). We emphasize that although our
index commonly identifies future dates, this does not imply that
climate change is not already occurring. In fact, our index projects
when ongoing climate change signals the start of a radically different
climate. For this analysis we used the projections of 39 Earth System
Models developed for the Coupled Model Intercomparison Project
phase 5 (CMIP5). The bounds of climate variability were quantified as
the minimum and maximum values yielded by the Earth System
Models with the CMIP5 ‘historical’ experiment, which for all models
included the period from 1860 to 2005. This experiment included
observed changes in atmospheric composition (reflecting both anthro-
pogenic and natural sources) and was designed to model the climate’s
recent past and allow the validation of model outputs against available
climate observations
26
. The year at which a climate variable moves out
of the historical bounds was estimated independently with data from
the Representative Concentration Pathways 4.5 (RCP45) and 8.5
(RCP85), which included the period from 2006 to 2100. These path-
ways or scenarios represent contrasting mitigation efforts between
a concerted rapid CO
2
mitigation and a ‘business-as-usual’ scenario
(CO
2
concentrations could increase to 538 and 936 p.p.m. by 2100,
according to RCP45 and RCP85, respectively
27,28
). A more aggressive
mitigation scenario (RCP 2.6) was not analysed, because it was not
consistently used among models, and the implicit mitigation effort is
considered currently unfeasible
29
.
We analysed five climate variables for the atmosphere and two for
the oceans (Extended Data Table 1). However, we report results with
mean annual near-surface air temperature as our indicator of the
climate, unless otherwise specified. Simulated and actual measure-
ments of temperature for the period 1986–2005 were remarkably
similar (Extended Data Fig. 1). In comparison with any individual
model’s results, the multi-model average best fitted the actual data
(Extended Data Fig. 1). We therefore describe our results on the basis
of multi-model averages. We show standard deviations to report the
spatial variability in our results. Multi-model uncertainty (that is, the
variability between models in the predicted years) was measured as
the standard error of the mean, which for near-surface air temper-
ature had a global value of 4.2 years for RCP45 and 2.7 years for
RCP85 (Extended Data Fig. 2a). Multi-model uncertainty for all vari-
ables is shown in Extended Data Fig. 2.
1
Department of Geography, University of Hawai‘i at Ma
¯
noa, Honolulu, Hawai‘i 96822, USA.
2
Department of Biology, University of Hawai‘i at Ma
¯
noa, Honolulu, Hawai‘i 96822, USA.
3
Hawai‘i Institute of
Marine Biology, University of Hawai‘i at Ma
¯
noa, Ka
¯
ne‘ohe, Hawai‘i 96744, USA.
4
Department of Oceanography, University of Hawai‘i at Ma
¯
noa, Honolulu, Hawai‘i 96822, USA.
5
Trans-disciplinary
Organization for Subtropical Island Studies (TRO-SIS), University of the Ryukyus, Senbaru, Nishihara, Okinawa 903-0213, Japan.
00 MONTH 2013 | VOL 000 | NATURE | 1
Sensitivity of the index
Three factors could affect the result of our index: first, the number of
years used as the historical reference period (a longer historical refer-
ence period could yield broader bounds of climate variability); second,
the number of consecutive years out of historical bounds in order to
declare the timing of climate departure (for example, one single year
out of historical bounds will probably occur sooner than several con-
secutive years), and third, the extent to which the historical reference
period has been affected by anthropogenic greenhouse gases (the use
of a period that already contains anthropogenic effects would yield
broader bounds of climate variability).
To address the first two concerns we calculated the year when the
climate exceeded the bounds of historical variability, using different
historical reference periods and varying numbers of consecutive years
out of the historical bounds. We found that the year in which the
climate exceeded the bounds of historical variability changed mini-
mally when using historical time bins ranging from 20 to 140 years
(Fig. 1b). Despite the fact that our analysis was constrained by the
140 years of available model data, the observed relationship between
historical time bins (X) and the year exceeding bounds of historical
climate variability (Y) showed a strong correlation for both RCPs
(Y 5 1.2773 ln X 1 2063.2 for RCP45; Y 5 1.0865 ln X 1 2042.2 for
RCP85 (dashed line in Fig. 1b); r
2
5 0.99 and P , 0.001 in both cases).
Under both RCPs, extrapolating these equations from a 140-year time
bin to a 1,000-year time bin increased the estimated year exceeding
the bounds of historical climate variability by only about 2 years. In
contrast, the year at which the climate exceeded the bounds of his-
torical variability was sensitive to the number of consecutive years out
of historical bounds considered (Fig. 1c). As illustrated for the loca-
tion in Fig. 1a, the climate will experience three consecutive years out
of historical bounds by 2012, 11 consecutive years by 2023, and all
consecutive years after 2036. To ensure robustness in our results we
used the minimum and maximum values in the entire time series of
the historical experiment from 39 Earth System Models available,
suggesting that our results included the broadest historical climate
bounds possible, given available data. Similarly, we used the first year
after which all values would continuously exceed the bounds of his-
torical climate variability.
To address the third concern we compared our results from the
historical experiment with those obtained from an additional CMIP5
experiment, ‘historicalNat’, which simulated the same time span as
the historical experiment but with only natural forcing (for example
volcanoes and solar variability), while excluding anthropogenic green-
house gas emissions
26
. The results of this analysis (Fig. 1d) indicated
that the climate surpasses the bounds of historical variability about
18.5 years earlier under RCP45, and 11.5 years earlier under RCP85
when using historical simulations that excluded anthropogenic green-
house-gas forcing (historicalNat experiment) compared with those that
included it (historical experiment). We did not use the historicalNat
experiment in our main results because it was available for only 17 out
of 39 Earth System Models in CMIP5 (Extended Data Table 1) and this
would have sacrificed the robustness obtained by using all available
models. However, the sensitivity test above provides a quantitative
measure of the likely adjustment in the projected timing of climate
departure if historic periods without human effects were used to quan-
tify past climate variability, and suggests that the reported values using
the historical experiment are highly conservative.
The timing of climate departure
We found that the year at which the climate exceeds the bounds of
historical variability depended on the modelled scenario. Under RCP45,
the projected near-surface air temperature of the average location on
Earth will move beyond historical variability by 2069 (618 years s.d.,
56 years in the future; solid blue line in Fig. 2d) and two decades earlier
under RCP85 (that is, 2047 (614 years s.d.),34 years in the future; solid
red line in Fig. 2d). These results are sobering indicators of the pace of
Nature nature12540.3d 23/8/13 11:06:28
1900
a
RCP45
Historical experiment
First 3 consecutive
years out
First 11 consecutive
years out
All consecutive years out
Temperature (°C)
1860
23.5
22.5
21.5
20.5
19.5
18.5
All consecutive years out
3 consecutive years out
First year out
Year of climate departure
20 6040 10080 140120
Number of years in historical time bin
2020
2040
2030
2050
2010
b
20 years in
historical time bin
Number of consecutive years out of
climate bounds
13
5
7
91113
15
140 years in
historical time bin
c
Year
1980 20201940
20 40 60 80 100 120 140
Number of years in historical time bin
Historical experiment
HistoricalNat experiment
2100
2060
d
Figure 1
|
Estimating the projected timing of climate departure from recent
variability. a, Mean annual temperatures of an example grid cell (small square
on map) exceed historical climate bounds (grey area) for three consecutive
years starting in 2012 (blue arrow) and for 11 consecutive years after 2023
(green arrow); after 2036 (red arrow) all subsequent years remained outside the
bounds (data from the Geophysical Fluid Dynamics Laboratory Earth System
Model 2G). b, c, Effect of using different historical reference periods (b) and
different numbers of consecutive years exceeding historical bounds (c)onthe
projected timing of climate departure from recent variability for global multi-model
averages under RCP85. d, Comparison of the projected timing of climate departure
from recent variability under RCP85, using the ‘historical’ and the ‘historicalNat’
experiments as reference to set the bounds of historical climate variability.
RESEARCH ARTICLE
2 | NATURE | VOL 000 | 00 MONTH 2013
climate change if one considers that the timing of climate departure will
occur sooner if ‘pristine’ climate conditions (that is, the historicalNat
experiment) are used to set historical climate bounds: 37.5 years in the
future under RCP45, or 22.5 years under RCP85. For the ocean, the
historical bounds of sea surface temperature will be surpassed on aver-
ageby2072(617 years s.d.) under RCP45 and by 2051 (616 years s.d.)
under RCP85 (Extended Data Fig. 3). When the index is calculated by
using monthly values (see Methods), all consecutive months will be out
of the monthly historical bounds later in the century (Fig. 2b, dotted
lines in Fig. 2d; see also Extended Data Fig. 3b, d). The estimated year
when the climate exceeds historical variability is delayed when using
monthly instead of annual averages, because one anomalous year is
not necessarily caused by all months being extreme; thus, an anomal-
ous year on average is likely to occur earlier than the year for which
all months fall outside the monthly climate bounds. It is remarkable,
however, that after 2050 most tropical regions will have every subsequent
month outside of their historical rangeofvariability(Fig.2b).Although
this is later than the yearly averages, we stress that this is an extreme
situation, in which every month will be an extreme climatic record.
We used mean annual near-surface air temperature as the main
proxy for the climate. However, other climate variables can change
earlier or later than temperature in response to greenhouse gas emis-
sions. To assess this effect, we analysed additional climate variables
under both emissions scenarios and provided an overall climate
assessment by estimating the year at which the first climate variable
exceeded its historical bounds of variability. These variables included
evaporation, transpiration, sensible heat flux and precipitation for the
Earth’s atmosphere, and surface pH for the ocean. For the atmo-
sphere, the projected timing of climate departure did not change when
considering other climate variables along with air temperature (Extended
Data Fig. 4). This occurred because air temperature will experience the
earliest and most sustained changes outside historical climate bounds
in comparison with other atmospheric variables (that is, other atmo-
spheric variables will continuously surpass their historical variability
later than temperature; Extended Data Fig. 4). However, the projected
timing of the ocean’s climate departure was pushed forward to this
decade when pH was considered alongside sea surface temperature.
Global mean ocean pH moved outside its historical variability by 2008
(63 years s.d.), regardless of the emissions scenario analysed (Extended
Data Fig. 4). This result, which is consistent with recent studies
30
,is
explained by the fact that ocean pH has a narrow range of historical
variability and that a considerable fraction of anthropogenic CO
2
emis-
sions has been absorbed by the ocean
30,31
.
Timing and absolute changes in climate
Absolute changes in the climate are often the means of detecting or
assessing climate change and are expected to be considerably larger at
higher latitudes (Fig. 2c; see also ref. 25). Measures of absolute changes
in the climate have also dominated the dialogue on climate change
(for example, avoiding 2 uC warming is a broadly recognized goal
among scientists, policy makers and the public, because such change
is forecast to generate deleterious consequences for society and
the environment
25,32
). However, we found poor spatial correlation
(Fig. 2e) between the absolute change in the climate expected by the
year 2100 (Fig. 2c) and the year at which the climate would surpass
historical precedents (Fig. 2a); this pattern was consistent among
other climate variables (Extended Data Fig. 4). This result suggests
that some aspects of climate change, which may be detrimental to
biodiversity, are poorly accounted for by metrics of absolute changes
in the climate; and implies that global biodiversity could face a climate
change ‘double jeopardy’ from either large absolute changes or the
early arrival of unprecedented climates.
We also found that the tropics will experience the earliest emer-
gence of historically unprecedented climates (Fig. 2a, b). This prob-
ably occurs because the relatively small natural climate variability in
this region of the world generates narrow climate bounds that can be
easily surpassed by relati vely small climate changes. However, small
but fast changes in the climate could induce considerable biological
responses in the tropics, because species there are probably adapted to
narrow climate bounds
5,33–35
. This is a prime explanation for the decline
in the range sizes of species towards lower latitudes (Rapoport’s rule):
having narrower tolerances, tropical species are largely restricted to the
tropics; in comparison, the broader physiological tolerances of temper-
ate species allow them to survive across a broader latitudinal span
33
.
Furthermore, empirical and theoretical studies in corals
5,36,37
, terrestrial
ectotherms
34
and plants and insects
35
show that tropical species live in
areas with climates near their physiological tolerances and are therefore
vulnerable to relatively small climate changes.
2020 2040 2060
a
Year of climate departure
21002080
e
cb
Year of climate departure
Absolute temperature change (°C)
20802040 2060 2100
2,000
1,500
1,000
500
0
15
10
5
0
2020
Number of points
Absolute temperature
change (°C)
RCP45 annual mean
RCP45 monthly mean
RCP85 annual mean
RCP85 monthly mean
Year of climate departure
d
0
0.5
1.0
Proportion of
100 km × 100 km cells
20802040 2060 21002020
151050
Figure 2
|
The projected timing of climate departure from recent variability.
a, b, Projected year when annual (a)ormonthly(b) air temperature means
move to a state continuously outside annual or monthly historical bounds,
respectively. c, Absolute change in mean annual air temperature. (Results in
ac are based on RCP85.) d, Cumulative frequency of 100-km grid cells
according to the projected timing of climate departure from recent variability
for air temperature under two emissions scenarios (vertical lines indicate the
median year). e, Scatter plot relating the grid cells from the map of absolute
change (c) to the same grid cells from the map of projected timing of climate
departure (a).
ARTICLE RESEARCH
00 MONTH 2013 | VOL 000 | NATURE | 3
The earliest emergence of unprecedented climates in the tropics
and the limited tolerance of tropical species to climate change are
troublesome results, because most of the world’s biodiversity is con-
centrated in the tropics (Extended Data Fig. 5; see also ref. 33). We
found that, on average, the projected timing of climate departure in
marine and terrestrial biodiversity hotspots (sensu ref. 38, the top 10%
most species-rich areas on Earth where a given taxon is found) will
occur one decade earlier than the global average under either emis-
sions scenario (Fig. 3). Coral hotspots will experience the earliest
arrival of unprecedented climates: 2050 under RCP45 (about 23 years
earlier than the global average), or 2034 under RCP85 (about 17 years
earlier than the global average) (Fig. 3). With the exception of marine
birds, whose hotspots are located at high latitudes (Extended Data
Fig. 5d; see also ref. 39), unprecedented climates will occur at the latest
by 2063 (RCP45) or 2042 (RCP85) in the hotspots of all other taxa
considered (Fig. 3). Overall these results suggest that the overarching
effect of climate change on biodiversity may occur not only as a result
of the largest absolute changes in climate at high latitudes but also
perhaps more seriously from small but prompt changes in the tropics.
In short, the tropics will be highly vulnerable to climate change for at
least three reasons: first, the earliest emergence of unprecedented cli-
mates will be there; second, tropical speciesare more vulnerable to small
climate changes; and third, this region holds most of the Earth’s species.
Discussion
The biological responses expected from the rapid emergence of histori-
cally unprecedented climates are likely to be idiosyncratic
40
and will
depend on attributes such as species adaptive capacity, current genetic
diversity, ability to migrate, current availability of habitats, disruption
of ecological interactions, and ecological releases
40–44
. Although the
extent of these responses in the future has been a topic of debate
45,46
,
considerable changes in community structure
3
and extinction
10
have
been shown to have coincided with the emergence of unprecedented
climates in the past. In addition, recent short-term extreme climatic
events have been associated with die-offs in terrestrial
47–49
and marine
37
ecosystems, highlighting the potentially serious consequences of
reaching historically unprecedented climates. Unfortunately, key con-
servation strategies such as protected areas, which may ameliorate the
extent of several anthropogenic stressors, are unlikely to provide refuge
from the expected effects of climate change, because protected areas
within biodiversity hotspots will experience unprecedented climates at
the same time as non-protected hotspot areas (Fig. 4a; see also ref. 50).
The expansion and/or effectiveness of protected areas and other con-
servation strategies could be further impaired by limited governmental
capacity,because the earliest emergence of unprecedentedclimates will
occur among hotspots predominantly located in low-income countries
(Fig. 4b and Extended Data Fig. 6).
The emergence of unprecedented climates could also induce res-
ponses in human societies
1,2,13–16,19–22
, and the resulting adjustments
could be considerable because according to RCP45 roughly 1 billion
people (about 5 billion people under RCP85) currently live in areas
where climate will exceed historical bounds of variability by 2050
(Fig. 5a). The fact that the earliest climate departures occur in low-income
Nature nature12540.3d 23/8/13 11:06:32
RCP85
Coral reefs
Mangroves
Seagrasses
Marine reptiles
Cephalopods
Marine sh
Plants
Terrestrial mammals
Terrestrial birds
Marine mammals
Amphibians
Terrestrial reptiles
Marine birds
2051
Average year of climate departure for hotspots
relative to global average
2047
–30
–15
15
RCP45
2072
2069
–30 –15 15
Figure 3
|
The projected timing of climate departure from recent variability
in global biodiversity hotspots. These plots indicate the difference between the
average year in which the climate exceeds bounds of historical variability for each
hotspot and the estimated global averages. The analysis was run independently
for each hotspot, using mean annual air temperature for terrestrial taxa (green
bars) or sea surface temperature for marine taxa (blue bars). Plots are centred at
the respective global mean year for atmospheric (green numbers) and marine
(blue numbers) environments. Horizontal bars rank the hotspots chronologically
according to the mean year of unprecedented climates under RCP85. Horizontal
black lines indicate the standard deviation among cells in the hotspots.
2050
ab
2030
2040
2040
2070
2060
2080
2080 0 30,00010,000 20,000
Average GDP per person of
countries with hotspots (US$)
Year of climate departure in
hotspot protected areas
Year of climate departure
in hotspots
RCP45
RCP85
2060
Figure 4
|
Biodiversity hotspots: exposure to climate departures, and
economic capacity to respond. Each point in these plots represents one of the
13 taxonomic biodiversity hotspots considered. a, Comparison of the year at
which the climate exceeds bounds of historical variability between hotspots and
protected areas in those hotspots. The dotted line shows the 1:1 relationship.
b, Comparison of the year at which the climate exceeds bounds of historical
variability in hotspots against the average Gross Domestic Product (GDP) per
personfor the countries encompassing the hotspots. The trend line for RCP45 is
modelled with y 5 0.001x 1 2045.2 (r
2
5 0.75, P , 0.05; n 5 13 hotspots) and
for RCP85 with y 5 0.0007x 1 2030.4 (r
2
5 0.75, P , 0.05; n 5 13 hotspots).
0
a
Year of climate departure
b
2020
2020
2040
2040
2060
2060 2080
2080
2100
2100
GDP per person (US$)
7
20,000 40,000 6,0000
Year of climate departure
Number of people (×10
9
)
RCP85
RCP85RCP45RCP45
6
5
4
3
2
1
0
Figure 5
|
Susceptibility of societies to climate departures, and economic
capacity to respond. a, Plot of cumulative number of people against the years
at which the climate of their current living areas will exceed historical climate
bounds (dotted lines highlight the results for 2050). b, Relation between
GDP per person and the average year of climate departure from recent
variability. The trend line for RCP45 is modelled with y 5 0.0005x 1 2051.9
(r
2
5 0.19, P , 0.05; n 5 200 countries or territories) and for RCP85 with
y 5 0.0004x 1 2034.5 (r
2
5 0.25, P , 0.05; n 5 200 countries or territories).
RESEARCH ARTICLE
4 | NATURE | VOL 000 | 00 MONTH 2013
countries (Fig. 5b) further highlights an obvious disparity between those
who benefit economically from the processes leading to climate change
and those who will have to pay for most of the environmental and social
costs. This suggests that any progress to decrease the rate of ongoing
climate change will require a bigger commitment from developed coun-
tries to decrease their emissions but will also require more extensive
funding of social and conservation programmes in developing countries
to minimize the impacts of climate change. Our results on the projected
timing of climate departure from recent variability shed light on the
urgency of mitigating greenhouse gas emissions if widespread changes
in global biodiversity and human societies are to be prevented.
METHODS SUMMARY
We used the projections of seven climate variables (near-surface air temperature,
sea surface temperature, precipitation, evaporation, transpiration, surface sens-
ible heat flux, and ocean surface pH) from Earth System Models developed for
CMIP5. As of March 2013 there were 39 Earth System Models from 21 climate
centres in 12 countries that modelled at least one of the variables analysed
(Extended Data Table 1). For each model and variable, we used the period
1860–2005 from the historical experiment (the longest time span common to
all models), to establish the historical bounds of climate variability. The projec-
tions from RCP45 and RCP85, which simulate the period 2006–2100, were used
to identify the year at which mean annual values of a given variable would exceed
historical bounds. We also independently calculated the year at which the climate
would exceed historical monthly variability by identifying the year containing the
month after which all consecutive months, until 2100, exceeded monthly histor-
ical bounds (Fig. 2b). In total, for all variables and experiments, we processed
89,712 years of data comprising 1,076,544 monthly global maps, interpolated to
an equal-area grid with a resolution of 100 km. Absolute climate change (Fig. 2c
and Extended Data Figs 3c and 4) was calculated by subtracting contemporary
averages (1996 to 2005) from future averages (2091 to 2100). Decadal averages
were chosen to minimize aliasing by inter-annual variability. The projected tim-
ing of climate departure in biodiversity hotspots (Fig. 3), protected areas (Fig. 4),
populated areas (Fig. 5a) and the economic capacity of countries (Fig. 5b) were
estimated by sampling these areas from global map results on projected climate
departure (based on near-surface air or sea surface temperature). All data sources
are indicated in Extended Data Table 2.
Online Content Any additional Methods, Extended Data display items and
Source Data are available in the online version of the paper; references unique
to these sections appear only in the online paper.
Received 25 April; accepted 6 August 2013.
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Acknowledgements We thank D. Beilman for commenting on the paper; E. Wingert for
help on the figures; D. Olsen for technical support; H. Kreft and the International Union
for Conservation of Nature, BirdLife International, the Food and Agriculture
Organization of the United Nations, the World Bank Database, the National Centers for
Environmental Prediction, the World Database of Protected Areas, and the Gridded
Human Population of the World Database for making their data openly available. We
acknowledge the World Climate Research Programme’s Working Group on Coupled
Modelling, which is responsible for CMIP5, and we thank the climate modelling groups
(listed in Extended Data Table 1) for producing and making available their model
outputs. This work was made possible by funding from the University of Hawai‘i Sea
Grant to C.M. The paper was developed as part of the graduate course on ‘Methods for
Large Scale Analyses’ in the Department of Geography, University of Hawai‘i at Ma
¯
noa.
A.G.F. and T.W.G were supported by Pacific Islands Climate Change Cooperative
(PICCC) award F10AC00077 and National Science Foundation Hawai‘i EPSCoR grant
no. EPS-0903833. R.J.L. was supported by the Pacific Islands Climate Science Center
and PICCC award F10A00079. R.S.D. and E.J.T. were supported by National Science
ARTICLE RESEARCH
00 MONTH 2013 | VOL 000 | NATURE | 5
Foundation Graduate Fellowships, and I.F.-S. by a postdoctoral fellowship from the
Japanese Society for the Promotion of Science.
Author Contributions All authors contributed equally to conceive the study, compile
the data, conduct analyses, and write the manuscript.
Author Information Reprints and permissions information is available at
www.nature.com/reprints. The authors declare no competing financial interests.
Readers are welcome to comment on the online version of the paper.
Correspondence and requests for materials should be addressed to C.M.
(cmora@hawaii.edu).
Nature nature12540.3d 23/8/13 11:06:36
RESEARCH ARTICLE
6 | NATURE | VOL 000 | 00 MONTH 2013
METHODS
Approach. We used the projections of seven climatic variables (near-surface air
temperature, sea surface temperature, precipitation, evaporation, transpiration,
surface sensible heat flux, and ocean surface pH) from Earth System Models
developed for CMIP5 (link to data source is provided in Extended Data Table 2).
As of March 2013, there were 39 Earth System Models from 21 climate centres in
12 countries that modelled at least one of the variables analysed (Extended Data
Table 1). We used the period 1860–2005 from the historical experiment (the
longest time span common to all models) to establish the historical bounds of
climate variability. The projections from RCP45 and RCP85, which simulate the
period 2006–2100, were used to identify the year at which mean annual values of a
given variable would exceed historical bounds (Fig. 1a). We also independently
calculated the year at which the climate would exceed historical monthly vari-
ability by identifying the year containing the month after which all consecutive
months, until 2100, exceeded monthly historical bounds. We used models that
provided the complete data sets for the historical, RCP45 and RCP85 experi-
ments. In total, for all variables and projections, we processed 89,712 years of data
comprising 1,076,544 monthly global maps. Given that the CMIP5 models use
different spatial grids to provide their data, we interpolated data from all models
to an equal-area grid with a resolution of 100 km, using an area-weighted average
interpolation method in which the data from the models’ grid were transferred to
our grid proportionally to the area that they occupied on our grid cells.
Sensitivity tests. We assessed three factors that could affect the result of our
index: first, the number of years used as the historical reference period; second,
the number of consecutive years out of historical bounds in order to declare the
departure of climate; and third, the extent to which the historical reference period
has been affected by anthropogenic greenhouse gases. To address these concerns
we calculated the year when the climate exceeded the bounds of historical vari-
ability by using historical time bins varying from 20 to 140 years, by varying the
number of consecutive years out of the historical bounds, and by comparing our
results from the historical experiment with those obtained from an additional
CMIP5 experiment, ‘historicalNat’, which simulated the same time span as the
historical experiment, but with only natural forcing. On the basis of the results
from these tests we decided to use, first, the minimum and maximum values of the
entire historical period; second, the year after which all values were outside
climate bounds, as our index; and third, the historical experiment as our reference
period of climate variability, because it was available for all models in CMIP5 as
opposed to the historicalNat experiment, which was available for only 17 out of 39
models. The test of our third concern, however, provides a measure of the differ-
ence in the results of our index using the historical reference period with human
effects (historical experiment) compared with a historical reference period without
human effects.
Determining the exposure of biodiversity and human societies. Biodiversity
hotspots were outlined for 13 different marine (birds, cephalopods, corals, mam-
mals, mangroves, marine fishes, reptiles and sea grasses) and terrestrial (amphi-
bians, birds, mammals, reptiles and plants) taxonomic groups from global patterns
of species richness (data sources are provided in Extended Data Table 2).
Biodiversity hotspots were defined as the top 10% most species-rich areas on
Earth where a given taxon is found (sensu ref. 33); their spatial distributions are
outlined in Extended Data Fig. 5. We determined the exposure of global biodiver-
sity hotspots to the projected timing of climate departure by intersecting biodi-
versity maps with maps of our index, and extracted the values in the overlapping
cells. To assess the conservation potential of protected areas in biodiversity hot-
spots, we obtained data on protected areas, extracted all those in biodiversity hot-
spots and overlapped them with maps of our index to determine their timing of
climate departure. We followed the same procedure for maps of population esti-
mates and for GDP per person by country to assess the exposure of human societies
and nations’ economic capacities to the timing of climate departure (data sources
are provided in Extended Data Table 2).
CMIP5 model robustness. We tested the robustness of CMIP5 Earth System
Models by relating the simulated values and the multi-model average of near-
surface air temperature and sea surface temperature from each model’s output to
20 years of temperature observations (data sources are provided in Extended Data
Table 2). We compared only air and sea surface temperature because of the broad
availability and reliability of temperature observations. This comparison was
restricted to the period 1986–2005, given the availability of actual data. The
resulting metrics of fit include the correlation, the ratio of the standard deviations
and the root mean squared error (all shown in Taylor diagrams in Extended Data
Fig. 1a, d). Given our interest in historical climate bounds set by minimum and
maximum climate values, we also compared the multi-model minimum and
maximum projected values of temperature against actual minimum and max-
imum temperatures for the period 1986–2005 (Extended Data Fig. 1b, c, e, f).
51. Kier, G. et al. A global assessment of endemism and species richness
across island and mainland regions. Proc. Natl Acad. Sci. USA 106, 9322–9327
(2009).
ARTICLE RESEARCH
Nature nature12540.3d 23/8/13 11:06:37
Extended Data Figure 1
|
Evaluating robustness of Earth System Models.
ac, Analysis of near-surface air temperature. df, Analysis of sea surface
temperature. a, d, Normalized Taylor diagrams. The Taylor diagrams compare
actual observations with CMIP5 model simulations, and summarize three
different metrics of similitude: the correlation (curved axis), the ratio of the
standard deviations (x and y axes) and the root mean squared error (blue arcs).
Blue points indicate perfect fit, red points the multi-model average, and black
points the comparison of each Earth System Model to actual observations. The
closer a red or black point is to the blue point, the better the fit between actual
and simulated data. b, c, e, f, Comparison between actual and multi-model
minimum (b, e) and maximum (c, f) temperatures for the 20-year period
1986–2005 (the time frame for which actual observations were mostly
available). Dashed lines indicate the 1:1 relationship.
RESEARCH ARTICLE
Extended Data Figure 2
|
Multi-model uncertainty in the projected timing
of climate departure. Results are shown for near-surface air temperature
(a), sea surface temperature (b), evaporation (c), sensible heat flux (d), ocean
surface pH (e), precipitation (f) and transpiration (g). Maps on the left show the
mean year of climate departure under RCP85, and maps on the right illustrate
the spatial patterns of inter-model standard error of the mean for RCP85. The
histograms on the right indicate the frequency of grid cells by multi-model
standard error of the mean according to each emissions scenario (blue, RCP45;
red, RCP85).
ARTICLE RESEARCH
Nature nature12540.3d 23/8/13 11:07:09
Extended Data Figure 3
|
Sea surface temperature and projected timing of
climate departure. a, b, Projected year when annual (a)ormonthly(b) sea
surface temperature means move to a state continuously outside annual or
monthly historical bounds, respectively. c, Absolute change in mean annual sea
surface temperature. (Results in ac are based on RCP85.) d, Cumulative
frequency of 100-km resolution grid cells according to the year of climate
departure under the two emissions scenarios and for mean annual and monthly
sea surface temperature. e, Scatter plot relating the grid cells from the absolute
change map (c) to the same grid cells from the projected timing of climate
departure map (a).
RESEARCH ARTICLE
Extended Data Figure 4
|
Projected timing of climate departure for
different climate variables. We calculated the year of climate departure for five
variables in addition to temperature. We considered the year of climate
departure as the year at which the first variable exceeded its historical bounds of
variability. The plots show the year of climate departure (left), the absolute
change (middle) and the relation between the departure year and absolute
change (right) under RCP85. The plot at the bottom right compares the global
average year using temperature alone with the year when considering
additional climate variables. Vertical lines indicate s.d.
ARTICLE RESEARCH
Nature nature12540.3d 23/8/13 11:07:17
Extended Data Figure 5
|
Biodiversity hotspots. Global patterns of species
richness were mapped for 13 marine and terrestrial taxa. For each taxon, we
outlined biodiversity hotspots as the top 10% most species-rich places on Earth
where the given taxon occurred (bold black lines). For mammals, birds, reptiles,
amphibians, marine fishes, cephalopods, corals, mangroves and seagrasses
(ah, jm), we used expert-verified geographical ranges to map patterns of
species richness by counting the number of species whose ranges overlapped
with an equal-area grid with a resolution of 100 km. i, For terrestrial vascular
plants we used the number of species in different regions (data from ref. 51) and
calculated species richness as the highest number of species occurring in the
regions intersecting each 100-km resolution grid cell. The number of species or
species richness used for each taxonomic group is indicated in parentheses.
RESEARCH ARTICLE
Extended Data Figure 6
|
Average gross domestic product (US$) per person
for countries where the world’s biodiversity hotspots are located. Horizontal
bars represent the average GDP per person for the countries containing the
hotspots for the 13 taxa examined.
ARTICLE RESEARCH
Nature nature12540.3d 23/8/13 11:07:26
Extended Data Table 1
|
Earth System Models analysed
The table shows the list of models used for each variable analysed. We considered only models that provided the complete series of data from 1860 to 2100 under the historical, RCP45 and RCP85 experiments;
asterisks indicate that the model also provided results for the ‘historicalNat’ experiment (data source shown in Extended Data Table 2). The variables analysed included near-surface air temperature (CMIP5
variable name ‘tas’, in K), precipitation (‘pr’, kg m
22
s
21
), evaporation (‘evspsbl’, kg m
22
s
21
), transpiration (‘tran’, kg m
22
s
21
), surface upward sensible heat flux (‘hfss’, W m
22
), surface sea water potential
temperature (‘thetao’, K) and pH (‘ph’, mol H kg
21
).
RESEARCH ARTICLE
Extended Data Table 2
|
Data sources
ARTICLE RESEARCH
Author Queries
Journal: Nature
Paper: nature12540
Title: The projected timing of climate departure from recent variability
Query
Reference
Query
1 AUTHOR: Please check that the display items are as follows (doi:10.1038/nature12540): Figs 1a–d, 2a–
e, 3, 4a,b, 5a,b (colour); Tables: None; Boxes: None. Please check all figures very carefully as they have
been relabelled, resized and adjusted to Nature’s style. Please ensure that any error bars in the figures
are defined in the figure legends.
Web
summary
An ensemble of simulations indicates that ongoing climate change will exceed the bounds of historical
climate variability some time in the mid to late twenty-first century and that the burden of rapid climate
adaption will occur earliest in highly biodiverse and often economically challenged tropical areas.
For Nature office use only:
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Nature nature12540.3d 23/8/13 11:07:31
RESEARCH ARTICLE
16|NATURE|VOL000|00MONTH2013
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The continued functioning of tropical forests under climate change depends on their resilience to drought and heat. However, there is little understanding of how tropical forests will respond to combinations of these stresses, and no field studies to date have explicitly evaluated whether sustained drought alters sensitivity to temperature. We measured the temperature response of net photosynthesis, foliar respiration and the maximum quantum efficiency of photosystem II (Fv /Fm ) of eight hyper-dominant Amazonian tree species at the world's longest-running tropical forest drought experiment, to investigate the effect of drought on forest thermal sensitivity. Despite a 0.6 - 2 ˚C increase in canopy air temperatures following long-term drought, no change in overall thermal sensitivity of net photosynthesis or respiration was observed. However, photosystem II tolerance to extreme-heat damage (T50 ) was reduced from 50.0 ± 0.3 ˚C to 48.5 ± 0.3 ˚C under drought. Our results suggest that long-term reductions in precipitation, as projected across much of Amazonia by climate models, are unlikely to greatly alter the response of tropical forests to rising mean temperatures but may increase the risk of leaf thermal-damage during heatwaves. This article is protected by copyright. All rights reserved.
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This original, field-changing collection explores the plasticity and unfinishedness of human subjects and lifeworlds, advancing the conceptual terrain of an anthropology of becoming. People's becomings trouble and exceed ways of knowing and acting, producing new possibilities for research, methodology, and writing. The contributors creatively bridge ethnography and critical theory in a range of worlds on the edge, from war and its aftermath, economic transformation, racial inequality, and gun violence to religiosity, therapeutic markets, animal rights activism, and abrupt environmental change. Defying totalizing analytical schemes, these visionary essays articulate a human science of the uncertain and unknown and restore a sense of movement and possibility to ethics and political practice. Unfinished invites readers to consider the array of affects, ideas, forces, and objects that shape contemporary modes of existence and future horizons, opening new channels for critical thought and creative expression. Contributors. Lucas Bessire, João Biehl, Naisargi N. Dave, Elizabeth A. Davis, Michael M. J. Fischer, Angela Garcia, Peter Locke, Adriana Petryna, Bridget Purcell, Laurence Ralph, Lilia M. Schwarcz
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This original, field-changing collection explores the plasticity and unfinishedness of human subjects and lifeworlds, advancing the conceptual terrain of an anthropology of becoming. People's becomings trouble and exceed ways of knowing and acting, producing new possibilities for research, methodology, and writing. The contributors creatively bridge ethnography and critical theory in a range of worlds on the edge, from war and its aftermath, economic transformation, racial inequality, and gun violence to religiosity, therapeutic markets, animal rights activism, and abrupt environmental change. Defying totalizing analytical schemes, these visionary essays articulate a human science of the uncertain and unknown and restore a sense of movement and possibility to ethics and political practice. Unfinished invites readers to consider the array of affects, ideas, forces, and objects that shape contemporary modes of existence and future horizons, opening new channels for critical thought and creative expression. Contributors. Lucas Bessire, João Biehl, Naisargi N. Dave, Elizabeth A. Davis, Michael M. J. Fischer, Angela Garcia, Peter Locke, Adriana Petryna, Bridget Purcell, Laurence Ralph, Lilia M. Schwarcz
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This book provides a first synthetic view of an emerging area of ecology and biogeography, linking individual- and population-level processes to geographic distributions and biodiversity patterns. Problems in evolutionary ecology, macroecology, and biogeography are illuminated by this integrative view. The book focuses on correlative approaches known as ecological niche modeling, species distribution modeling, or habitat suitability modeling, which use associations between known occurrences of species and environmental variables to identify environmental conditions under which populations can be maintained. The spatial distribution of environments suitable for the species can then be estimated: a potential distribution for the species. This approach has broad applicability to ecology, evolution, biogeography, and conservation biology, as well as to understanding the geographic potential of invasive species and infectious diseases, and the biological implications of climate change. The book lays out conceptual foundations and general principles for understanding and interpreting species distributions with respect to geography and environment. Focus is on development of niche models. While serving as a guide for students and researchers, the book also provides a theoretical framework to support future progress in the field.
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