Projected distributions of novel and disappearing
climates by 2100 AD
John W. Williams*†‡, Stephen T. Jackson§, and John E. Kutzbach†¶
*Department of Geography, 550 North Park Street, University of Wisconsin, Madison, WI 53706;†Center for Climatic Research and¶Department of
Atmospheric and Oceanic Sciences, 1225 West Dayton Street, University of Wisconsin, Madison, WI 53706; and§Department of Botany, 1000 East University
Avenue, University of Wyoming, Laramie, WY 82071
Edited by Stephen H. Schneider, Stanford University, Stanford, CA, and approved January 30, 2007 (received for review July 24, 2006)
Key risks associated with projected climate trends for the 21st
century include the prospects of future climate states with no
current analog and the disappearance of some extant climates.
Because climate is a primary control on species distributions and
ecosystem processes, novel 21st-century climates may promote
formation of novel species associations and other ecological sur-
prises, whereas the disappearance of some extant climates in-
creases risk of extinction for species with narrow geographic or
climatic distributions and disruption of existing communities. Here
we analyze multimodel ensembles for the A2 and B1 emission
scenarios produced for the fourth assessment report of the Inter-
governmental Panel on Climate Change, with the goal of identi-
fying regions projected to experience (i) high magnitudes of local
climate change, (ii) development of novel 21st-century climates,
projected to develop primarily in the tropics and subtropics,
whereas disappearing climates are concentrated in tropical mon-
tane regions and the poleward portions of continents. Under the
high-end A2 scenario, 12–39% and 10–48% of the Earth’s terres-
trial surface may respectively experience novel and disappearing
climates by 2100 AD. Corresponding projections for the low-end B1
scenario are 4–20% and 4–20%. Dispersal limitations increase the
risk that species will experience the loss of extant climates or the
occurrence of novel climates. There is a close correspondence
between regions with globally disappearing climates and previ-
ously identified biodiversity hotspots; for these regions, standard
conservation solutions (e.g., assisted migration and networked
reserves) may be insufficient to preserve biodiversity.
biodiversity hotspots ? climate change ? dispersal limitations ?
global-change ecology ? ecological surprises
some 20th-century climates may disappear. The combination of
high CO2concentrations, still-extensive ice sheets in Greenland
and Antarctica, and current orbital and land–ocean configura-
tions are geologically unprecedented (1). Already, CO2concen-
trations exceed any recorded for the last 650,000 years (2) and,
without a substantive intervention, are projected to increase to
Global mean temperatures are projected to increase by 1.4–
5.8°C by 2100 AD (3), with decreases in diurnal and seasonal
temperature ranges (4) and spatially variable changes in precip-
itation. It is increasingly likely that some end-21st-century
climates will include conditions not experienced at present
(‘‘novel’’ climates) and that some present climates may disap-
pear. Here we statistically analyze 21st-century climate scenarios
disappearing climates and discuss likely ecological impacts.
Climate is a primary constraint on species distributions and
ecosystem function, and ecologists are faced with the challenge
of forecasting species range shifts, extinction risks, biome shifts,
altered disturbance regimes, biogeochemical cycling, and other
ecological responses to climate change (5–7). Such forecasts are
y the end of the 21st century, large portions of the Earth’s
surface may experience climates not found at present, and
impeded by the difficulty of predicting ecological responses to
environmental conditions outside the range of current experi-
ence. Niche theory predicts that multivariate changes in climate
should cause shifts in species distributions, disruption of extant
communities, and formation of novel species associations (8–
10), because each species responds individualistically (Fig. 1).
This conceptual framework is reinforced by observed ecological
responses to the last deglaciation, which were characterized by
large changes in species ranges, and, in places where past
climates apparently lacked modern analogs, the development of
species associations and biomes with no modern counterpart
(11–13). Metaanalyses indicate already detectable responses to
20th-century temperature rises, with range shifts averaging 6.1
km per decade toward the poles (14, 15). Dispersal limitations
may cause species responses to lag rapid climate change, pro-
moting the formation of disequilibrial relationships between
species distributions and climate. Others have argued that future
novel climates may cause a reshuffling of communities (10, 12);
however, our study attempts to move beyond generalities by
explicitly mapping the future distribution of novel climates.
Conversely, species endemic to certain climates are at risk of
extinction if those climates disappear, and communities in those
regions may disaggregate or disappear (Fig. 1) (8). High-elevation
and high-latitude species, for example, may go extinct as temper-
ature or moisture changes drive vegetation zones upward and
increasing the rate of climate change relative to the capacity of
species to adjust by migration and colonization (8, 20).
Using model ensembles drawn from Intergovernmental Panel
on Climate Change (IPCC) Assessment Report 4, we here
calculate three indices of climatic risk: (i) local standardized
climatic change, (ii) the climatic distance between the end-21st-
century simulation for each gridpoint and its closest analog from
the global pool of 20th-century climates (an index of the novelty
of future climates), and (iii) the climatic distance between the
20th-century realization for each gridpoint and its closest 21st-
century climatic analog (an index of the disappearance of extant
climates). All three indices are based on the same metric of
multivariate dissimilarity, and temporal differences are given
context by comparing them against the multivariate differences
in climate among modern potential biomes (see Materials and
Methods). We use two nonmitigation emission scenarios, IPCC
Special Report on Emissions Scenarios (SRES) A2 and B1, to
span the range of IPCC emission scenarios (3). Finally, we
Author contributions: J.W.W. and S.T.J. designed research; J.W.W. performed research;
J.W.W. and J.E.K. analyzed data; and J.W.W. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Abbreviations: IPCC, Intergovernmental Panel on Climate Change; SED, standardized
‡To whom correspondence should be addressed. E-mail: firstname.lastname@example.org.
This article contains supporting information online at www.pnas.org/cgi/content/full/
© 2007 by The National Academy of Sciences of the USA
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analyze the effects of species dispersal limitations on these
climatic risk indices.
Local climate changes are large everywhere in the A2 scenario
(Fig. 2A) but are largest in tropical and subtropical regions, with
high values over most of Africa, South America, and southern
Eurasia, as well as portions of eastern North America, the Arctic,
and eastern Eurasia. In the A2 scenario, 56–100% of global land
area is projected to experience biome-scale climate changes,
versus 17–98% in the B1 scenario. This pattern (Fig. 2 A and B)
differs from the fingerprint commonly associated with global
warming [i.e., large increases in annual mean temperatures
projected for circum-Arctic regions and northern hemisphere
continental interiors (3)], because changes in all variables are
standardized against their interannual variability (see Materials
and Methods). Interannual temperature variability is smaller in
low-latitude regions than in high-latitude regions, upweighting
the statistical significance of tropical warming trends [2–4°C
(A2) and 1–2.5°C (B1) mean annual warming by 2100 AD for the
models used here]. Weighting by interannual variability also
upweights the significance of temperature over precipitation
changes because interannual variability for temperature is rel-
reflect standardized temperature changes.
The distribution of novel climates (Fig. 2 C and D) occupies
a spatially cohesive subset of areas experiencing high local
climate change. Novel climates are strongly concentrated in
tropical and subtropical regions, with the highest dissimilarities
over the Amazonian and Indonesian rainforests. Novel 21st-
century climates are also projected for the western Sahara,
low-lying portions of east Africa, eastern Arabian Peninsula,
southeastern U.S., eastern India, southeast Asia, and northwest-
ern Australia. The percentages of global land area with novel
climates are 12–39% (A2) and 4–20% (B1). The clustering of
novel climates in the tropics and subtropics and their scarcity in
high latitudes suggest that a key determinant of the development
of novel climates is the projected poleward shift of thermal
zones. Tropical and subtropical areas also are projected to
experience large changes in precipitation, caused by an intensi-
fied hydrological cycle and shifted moisture advection (21), but
these changes are secondary in the standardized indices used
radius, representing an upper limit to unassisted rates of plant
migration by 2100 AD (see Materials and Methods), the extent of
novel climates approximately doubles (Fig. 3 A and B) to
39–84% (A2) and 14–52% (B1).
Disappearing climates are primarily concentrated in tropical
mountains and the poleward sides of continents (Fig. 2 E and F).
Specific areas include the Columbian and Peruvian Andes,
Central America, African Rift Mountains, the Zambian and
Angolan Highlands, the Cape Province of South Africa, south-
east Australia, portions of the Himalayas, the Indonesian and
Philippine Archipelagos, and some circum-Arctic regions. There
is generally little overlap between areas projected to develop
The percentages of land area with disappearing climates are
10–48% (A2) and 4–20% (B1). Including a 500-km constraint
increases the extent of disappearing climates to 37–85% (A2)
and 14–50% (B1) (Fig. 3 C and D).
The risk of novel and disappearing climates scales linearly with
the magnitude of mean global warming (Fig. 4), suggesting that
there is no obvious threshold beyond which a ‘‘dangerous’’ level
of climate change exists (22). However, ecological systems are
likely to exhibit strongly nonlinear responses to climatic forcing
(6), so that forecasting ecological responses to novel and disap-
pearing climates will be critical.
Different ecological risks are associated with the prospect of
novel versus disappearing climates. Novel temperature regimes,
combined with changes in precipitation, may lead to novel
species associations and other unexpected ecological responses,
as has occurred in the past (11–13). Because the pre-Industrial
Revolution climate system was already in a warm state, further
increases in temperatures are likely to be novel not just relative
years (20). Tropical species may be particularly sensitive to
21st-century warming because (i) tropical temperatures vary less
than high-latitude temperatures at daily, seasonal, orbital, and
tectonic timescales (23), and (ii) range size tends to decrease
toward the equator (Rapoport’s Rule) (24), so that tropical
species are more narrowly endemic in both geographic and
is at particular risk for increased fire frequency and loss of forest
cover (7). The potential for ecological surprises in the tropics
adds urgency to current conservation efforts.
Forecasting species-level responses to novel climates is a
serious challenge, because they force an extrapolation of eco-
logical niche models beyond the observed correlations among
current species distributions and climates (8). Paleoecological
data clearly indicate that some past plant communities and
biomes had no modern analog (11–13) and likely grew in
response to climates also lacking modern analogs (8, 11). More-
over, the lack of current analogs for future climates limits our
ability to validate ecological model predictions. One partial
solution is to use the past as a testing ground for ecological
models (27). Although past climates also are not a good analog
for future climates (1), paleoecological and paleoclimatic data
can be used to evaluate the robustness of ecological models to
environmental conditions outside the modern envelope.
Disappearing climates increase the likelihood of species ex-
tinctions and community disruption for species endemic to
poleward and tropical montane regions. Many have warned that
climate change may drive certain species and ecosystems to
extinction, e.g., in high latitudes (18), the South African Fynbos
Climate Variable 1
Climate Variable 2
ical 20th- and 21st-century climates (black-bordered ellipses) (8). Novel climates
are the portions of the 21st-century envelope that do not overlap 20th-century
climates, and disappearing climates are the portions of the 20th-century enve-
lope that do not overlap 21st-century climates. Species cooccur only if their
fundamental niches simultaneously intersect with each other and the current
including shifts in species distributions (species 1–3), community disaggregation
(species 1 and 3), new communities forming (species 2 and 3), and extinction
(species 4). This conceptual model assumes fixed niches, i.e., that climate change
will outpace evolutionary adaptation (8).
Williams et al. PNAS ?
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(19), and neotropical cloud montane forests (16, 17). Our
analysis places these regional alarms in a global context and goes
further by showing that, in many cases, these climates may
disappear entirely from the global set of end-21st-century cli-
mates. The areas of disappearing climates closely overlay regions
identified as critical hotspots of biological diversity and ende-
mism, including the Andes, Mesoamerica, southern and eastern
Africa, Himalayas, Philippines, and Wallacea (28). In these
areas, elevated risks of extinction are likely, as is the disruption
and disaggregation of extant communities (8).
Even with a conservative estimate of dispersal constraints, dis-
persal limitations greatly increase the risk that species will experi-
ence the loss of extant climates or the occurrence of novel climates
(Figs. 2 and 3). Efforts to conserve biological diversity in the face
local climate change (A and B), novel 21st-century
climates (C and D), and disappearing 20th-century cli-
mates (E and F). (A) Local climatic change for the A2
scenario, represented by the SED between the 20th-
and 21st-century climate realizations for each grid-
point. The color bar is scaled so that SED3SEDt(see
Materials and Methods) are yellow to red. (B) As in A
but for the B1 scenario. Locally high values over the
Sahara, Arabian Peninsula, and southwestern Asia are
an artifact of zero precipitation and precipitation vari-
ance simulated by the MRI–CGCM2.3.2 and CCSM3
models. In other models, the SED scores for these lo-
cations are similar to those of neighboring gridpoints.
(C) Maps of the SEDminbetween the 21st-century real-
ization for each gridpoint and the set of 20th-century
climate realizations (A2 scenario). High dissimilarities
scenario. (E) Maps of the SEDminbetween the 20th-
century realization for each gridpoint and the set of
21st-century climate realizations (A2 scenario). High
As in E but for the B1 scenario. (C–F) The pool of
potential climatic analogs is global.
Mapped indices of climate change risk for
the 21st-century realization for each gridpoint and the set of 20th-century climate realizations (A2 scenario). High dissimilarities indicate risk of regionally novel
21st-century climates. (B) As in A but for the B1 scenario. (C) SEDminbetween the 20th-century realization for each gridpoint and the set of 21st-century climate
realizations (A2 scenario). High dissimilarities indicate risk of regionally disappearing 20th-century climates. (D) As in C but for the B1 scenario.
www.pnas.org?cgi?doi?10.1073?pnas.0606292104Williams et al.
of climate change, e.g., by establishing dynamic networks of con-
nected reserves that can facilitate species migrations (19) or ‘‘re-
wilding’’ or otherwise assisting species migrations (29), may help
overcome dispersal limitations. However, for those regions, com-
insufficient. Furthermore, because of the spatial segregation be-
tween novel and disappearing climates (Fig. 2), species at risk of
extinction due to disappearing climates are unlikely to be well
positioned to take advantage of new climatic regimes.
No single analysis can capture all ecological risks associated
with climate change, e.g., shifts in ecosystem distributions,
changes in carbon sequestration, altered hydrological function-
is needed to capture the multidimensional responses of species
and ecosystems to multidimensional climate change. Moreover,
climate change is just one of many current ecological stressors.
Interspecific differences in response time to the rapid pace of
projected 21st-century climate change will also promote the
formation of novel species assemblages and extinction risk (9).
These factors, together with the projected development of novel
climates and the threat that the climates particular to some
biodiversity hotspots may disappear globally, create the strong
likelihood that many future species associations and landscapes
will lack modern analogs and that many current species and
associations will be disrupted or disappear entirely.
Materials and Methods
We use a nine-model ensemble for the A2 scenario (856 ppm
pCO2by 2100 AD) (3) and an eight-model ensemble for the B1
scenario (549 ppm pCO2) [supporting information (SI) Table 1].
Each global climate model is represented by a single realization
per scenario, chosen at random when multiple realizations were
available. The equilibrium climate sensitivity of these models to
CO2doubling is 2.1–4.4°C, with seven models between 2.7°C and
ipcc?model?documentation.php). All simulations were bilinearly
interpolated to a common T42 grid (?2.8° ? 2.8° resolution), the
median resolution for the models used here.
After interpolation, we quantified dissimilarities between
20th- and 21st-century climates by using the standardized Eu-
clidean distance (SED) (31):
k ? 1
where akiand bkjare the 1980–1999 and 2080–2099 means for
climate variable k at gridpoints i and j and skjis the standard
deviation of the interannual variability for 1980–1999. Effects of
climate model bias are minimized by comparing simulated
i ? j. The SED equally weights all variables and emphasizes
21st-century trends that are large relative to 1980–1999 inter-
annual variability. SED scores are first calculated for each global
climate model and then averaged into the ensembles reported
Four climate variables are used: mean surface air temperature
and precipitation for June–August (JJA) and December–
February (DJF). These variables were chosen because (i) they
represent controls of seasonal temperature and moisture avail-
ability on plant distributions and abundance (32), (ii) seasonal
means are robust features of model simulations, and (iii) they are
compatible with prior work linking the occurrence of past
no-analog plant communities (12) to no-analog climates (11).
These variables correlate well with other proposed bioclimatic
controls on species distributions (e.g., growing degree days,
actual and potential transpiration, and minimum and maximum
annual temperature) (33, 34). For example, linear regression
models using 1961–1990 mean monthly data from the Climate
Research Unit TS2.1 data set (35) and the explanatory variables
JJA temperature (TJJA) and DJF temperature (TDJF) explain
93% of the global variance growing degree days. Similarly, linear
models using TJJA, TDJF, JJA precipitation (PJJA), and DJF
precipitation (PDJF) explain 45% of the global variance in the
moisture index ?, calculated as the ratio of actual to potential
evapotranspiration (32). We opted not to include metrics of
21st-century variability or the frequency of extreme events
because projected trends in 21st-century climate variability are
more uncertain than changes in means.
Local climate change is measured as the SED between 20th-
and 21st-century realizations for each gridpoint (Fig. 2 A and B).
High SED scores correspond to larger local climate change and
integrate changes in temperature and precipitation. Novel ter-
restrial climates are identified by comparing the 21st-century
climate realization for each land gridpoint to the 20th-century
climate realizations for all land gridpoints and retaining the
indices of local climate change (Fig. 2, compare A and B with C
and D). The pool of potential climatic analogs is global, so that
high SEDminindicate 21st-century climates with no good analog
anywhere in 20th-century climate space (Fig. 2 C and D).
Conversely, disappearing climates are identified by comparing
each 20th-century gridpoint to all 21st-century climate realiza-
ing and the fractional global area with novel and disappearing climates. Each
point represents an individual model; triangles represent A2 simulations, and
circles represent B1 simulations. Filled symbols and the solid regression line
represent risk indices for novel climates (corresponding to Figs. 2 C and D and
3 A and B); open circles and the dashed line represent risk indices for disap-
pearing climates (corresponding to Figs. 2 E and F and 3 C and D). A2 and B1
scenarios are pooled for the regression model. (A) Search for climatic analogs
is global (no dispersal constraint). (B) Search for analogs restricted to within
500 km (500-km dispersal constraint).
Plots showing the relationship between global mean annual warm-
Williams et al. PNAS ?
April 3, 2007 ?
vol. 104 ?
no. 14 ?
tions and retaining the SEDmin. High SEDmin here indicate
places where 20th-century climates may disappear; i.e., they have
no close counterpart anywhere in the 21st-century simulations
(Fig. 2 E and F).
Although the SED scores usefully summarize multivariate
changes in climate, assessing their ecological significance re-
quires placing them in context against known ecological phe-
nomena. We calculate an SED threshold (SEDt) that determines
when an SED value is large enough to represent a truly novel
climate. A 21st-century gridpoint is defined as novel if its SEDmin
exceeds the threshold. Similarly, a 20th-century climate disap-
pears if its SEDminexceeds the threshold.
SEDtis determined by overlaying a global potential vegetation
map (36) onto 20th-century climatologies and identifying the
SEDt that optimally discriminates between pairs of climate
vectors from the same biome vs. pairs of biomes from different
biomes (31). SEDtis calculated individually for each biome and
then averaged across all biomes (SI Materials and Methods and SI
Table 2); here we use SEDt ? 3.22, and in SI Materials and
Methods, we experiment with a higher threshold (SEDt? 5.33)
(SI Figs. 5–7). Biomes are global-scale ecosystems whose distri-
butions are climatically controlled (32, 37), making them well
suited for determining whether a particular level of climate
SEDtthus represents a temporal climatic change equivalent to
the spatial difference in climates among extant biomes, and is a
conservative estimator of the extent of novel and disappearing
climates. This is because all locations from finer-scale ecological
entities tend to have fairly similar climates, and so SEDtwill be
low, whereas biomes encompass a wider range of climates, and
so SEDt will be high. Thus, the climatic differences among
biomes are large relative to the climatic differences among
finer-scale ecological phenomena, so that a ‘‘biome-scale’’ cli-
mate change likely will be significant for finer-scale ecological
Close climatic similarities between geographically distant lo-
cations are not relevant for species constrained by dispersal
limitations. We therefore conducted a second set of analyses in
which the potential pool of climatic analogs is restricted to
locations within 500 km (Fig. 3). The 500-km radius is inten-
tionally large [exceeding the highest known rates of plant
migration during the last deglaciation (?200 km per century)
(38)], because larger radii produce more conservative identifi-
cations of novel and disappearing climates. This analysis affects
only the predicted distribution of novel and disappearing cli-
mates (Fig. 2 C–F), not local change (Fig. 2 A and B).
We thank the international modeling groups for making simulations
available for analysis; the Program for Climate Model Diagnosis and
Intercomparison for collecting and archiving the model data, the JSC/
CLIVAR Working Group on Coupled Modeling and their Coupled
Model Intercomparison Project and Climate Simulation Panel for
organizing the model data analysis activity; Pat Behling, Mark Marohl,
and Intergovernmental Panel on Climate Change Working Group 1 for
technical support; Bryan Shuman, Josh Tewksbury, and Mick McCarthy
for discussions; and several anonymous reviewers for comments. The
Intergovernmental Panel on Climate Change Data Archive at Lawrence
Livermore National Laboratory is supported by the Office of Science,
U.S. Department of Energy. This work was supported by National
Science Foundation Grant ATM 050-7999.
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