Download Upper bounds on twenty-first-century Antarctic ice loss assessed

LETTERS
PUBLISHED ONLINE: 17 MARCH 2013 | DOI: 10.1038/NCLIMATE1845
Upper bounds on twenty-first-century Antarctic
ice loss assessed using a probabilistic framework
Christopher M. Little1 *, Michael Oppenheimer1,2 and Nathan M. Urban1,3
Climate adaptation and flood risk assessments1,2 have
incorporated sea-level rise (SLR) projections developed using semi-empirical methods3–5 (SEMs) and expert-informed
mass-balance scenarios2,6 . These techniques, which do not
explicitly model ice dynamics, generate upper bounds on
twenty-first century SLR that are up to three times higher
than Intergovernmental Panel on Climate Change estimates7 .
However, the physical basis underlying these projections,
and their likelihood of occurrence, remain unclear8–10 . Here,
we develop mass-balance projections for the Antarctic ice
sheet within a Bayesian probabilistic framework10 , integrating
numerical model output11 and updating projections with an
observational synthesis12 . Without abrupt, sustained, changes
in ice discharge (collapse), we project a 95th percentile mass
loss equivalent to ⇠13 cm SLR by 2100, lower than previous
upper-bound projections. Substantially higher mass loss
requires regional collapse, invoking dynamics that are likely
to be inconsistent with the underlying assumptions of SEMs.
In this probabilistic framework, the pronounced sensitivity of
upper-bound SLR projections to the poorly known likelihood
of collapse is lessened with constraints on the persistence
and magnitude of subsequent discharge. More realistic, fully
probabilistic, estimates of the ice-sheet contribution to SLR
may thus be obtained by assimilating additional observations
and numerical models11,13 .
In the Intergovernmental Panel on Climate Change’s Fourth
Assessment Report, century-timescale changes in ice-sheet dynamics were deemed too uncertain to quantify7 . Subsequent critiques
highlighted the need for low-probability upper bounds on SLR
that include a more comprehensive estimate of the ice-sheet
contribution14 . Non-process-based SLR projections, developed
using SEMs (refs 3–5,15) and expert-informed mass balance (or
kinematic) scenarios2,6 (EISs), have been proposed to complement
ongoing numerical modelling efforts16 .
SEMs do not require process-based ice-sheet models to generate
upper bounds: they project the aggregate contribution of all SLR
sources17 using differential equations calibrated against historical
sea-level and climate observations. SEMs propagate error in their
calibration and are presented probabilistically; upper bounds
and best estimates are quantitatively defined and self-consistent.
However, it is unclear what proportion of SEM SLR projections
is derived from each source (for example, changes in Antarctic
ice dynamics)15,18 , and thus whether their upper bounds imply
physically plausible twenty-first century rates of ice loss8,9 . The
importance of independent physical constraints on implied ice-loss
rates is underscored by: the sensitivity of SEM upper bounds to the
choice of calibration data set4,5 ; and evidence that ice-sheet-derived
SLR exhibits a history- and insolation-dependent19,20 response to a
warming climate that cannot be captured by SEM calibrations.
EISs specify regional ice discharge using physical constraints
and expert judgement, but, so far, they have accounted for only
a few possible scenarios over small sectors of the ice sheet. More
robust upper bounds that may be compared to those generated
by SEMs require accounting for the likelihoods of many possible
changes in ice-sheet dynamics8,10 . Furthermore, although SEM and
EIS upper-bound projections are in rough agreement, less attention
has been paid to their contrasting physical implications. SEMs
assume smooth, temperature-dependent, growth in global mean
sea level, whereas upper-bound EISs assume a marked, sustained,
step change in discharge.
Here, we develop probabilistic projections of Antarctica’s mass
balance (and thus its contribution to global mean sea level)
in a Bayesian framework (schematically illustrated in Fig. 1)
that combines expert-informed mass-balance assumptions with
observational and model-based constraints. This methodology
sheds insight into the physical implications of twenty-first-century
SEM projections, and allows a probabilistic interpretation of EIS
upper bounds. In the future, improved SLR projections can be
developed by assimilating more observations and models of the
Antarctic ice sheet, and by including other sources of sea-level
change (grey arrows in Fig. 1).
To generate prior probability distributions of ice-sheet mass
balance, we sample uncertain assumptions in each of 19 Antarctic
drainage basins using a Monte Carlo method10 (see Methods and
Supplementary Discussion and Fig. S1). In each Monte Carlo
realization, each drainage basin employs a surface mass balance
(SMB, principally snow accumulation in Antarctica) and icedischarge baseline that is drawn from one of two recent analyses21,22
and assumed to represent 1996 values. For Pine Island Glacier
(PIG) and the remainder of its drainage basin (B15R), 30-year
linear trends in discharge23 are extrapolated with their associated
uncertainty10 . After 2008, PIG’s annual net mass balance is held
constant at a value sampled from a normal distribution, constructed
from the output of an ensemble of regional model simulations11
(see Supplementary Discussion). For all other Antarctic drainage
basins, where historical discharge data are limited, linear growth
rates, sampled from a multivariate normal distribution, are applied
to SMB and ice-discharge baselines. These growth rates may be
spatially correlated, reflecting either dynamic linkages or common
climate forcing10 .
Observations indicate that abrupt changes in discharge may
be initiated by stress perturbations at ice-sheet margins (for
1 Woodrow
Wilson School of Public and International Affairs, Princeton University, Princeton, New Jersey 08544, USA, 2 Department of Geosciences,
Princeton University, Princeton, New Jersey 08544, USA, 3 Computational Physics and Methods (CCS-2), Los Alamos National Laboratory, Los Alamos,
New Mexico 87544, USA. *e-mail: [email protected]
654
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LETTERS
NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE1845
SEMs
Other SLR sources:
thermosteric,
Greenland, others
Data constraint
Expert judgment
Physical
model
Statistical
model
Other techniques
Monte Carlo sampling
Weighting
Posterior
prediction
2100 global mean
sea level
Antartic mass balance
1990¬2099
Annual SLR
contribution
Collapse probability
and flux
Monte Carlo
sampling
PIG and B15R 2009¬2099
PIG 2009¬2099
Continental masschange observations
1992¬2010
Covariance function
Historical discharge
growth rate
Historical discharge
growth rate
Discharge growth rate
distribution
B15R 1990¬2099
Other basins 1990¬2099
PIG 2090¬2008
1996 SMB and
discharge baseline
Figure 1 | A schematic illustration of the methodology used to generate mass-balance projections for Antarctica. Rectangles indicate prior mass-balance
assumptions, diamonds indicate physical or statistical models used to convert priors into posterior SLR projections, and ovals indicate projections of
sea-level components. More details are presented in Methods. Grey arrows indicate the relationship between the Antarctic mass-balance projections
developed in this paper and SEM projections of SLR.
Base-case cumulative Antarctic
mass loss (cm SLR equivalent)
a
15
0.8
10
0.0
5
0
¬0.8
1990
b
Base case
Unweighted base case
Weighted base case
EISs
Weighted
2010
SEMs
Extreme cases
¬5
¬10
¬15
Fully correlated (ρ a = 1)
High growth rate (µ = µ )
Immediate collapse (Pc = 1)
Unweighted
2000
2040
MB
2080
Year
PIG
¬20
0
20
40
60
1990¬2099 Antarctic mass loss (cm SLR equivalent)
Figure 2 | Mass-balance projections, and comparison with previous work. a, 1990–2099 projections of the cumulative Antarctic ice-sheet mass loss,
using the base-case assumptions described in Table 1. Grey shading indicates the 5–95th percentile range of unweighted projections. Blue shading indicates
the 5–95th percentile range when individual Monte Carlo realizations are weighted by their agreement with observations (see Methods). b, Projections of
cumulative ice loss by 2100 for the base case and, as described in text, three extreme changes in key assumptions. In all box plots, circles are median
projections, and bars show 5–95th ranges. Also included are estimates of the Antarctic contribution to SLR from recent EIS and SEM projections. For EIS
projections, triangles correspond to ref. 2 and squares correspond to ref. 6; red symbols indicate high-end or severe scenarios, and green symbols represent
more moderate ice-loss scenarios. For SEM analyses, triangles correspond to ref. 5, diamonds correspond to ref. 4 and squares corresponds to ref. 3. Red
bars represent the range of 95th percentile values for all calibrations and Greenland/Antarctica ice-loss partitions ranging from 1:1 to 2:1 (see Methods).
example, rapid disintegration of an ice shelf and/or ungrounding
from bedrock16,24,25 ) that are difficult to predict and inadequately
modelled at present16 . Although abrupt changes are not included
in our base-case projection, we examine the sensitivity of our
projections to a sustained step change in discharge (collapse) by
assigning a constant annual collapse probability in PIG and B15R
until (and if) it occurs (see Supplementary Discussion). We then use
a selection of published scenarios to bound the resulting discharge
(2–8⇥ present-day SMB)2,6,26 , which is held constant to 2100
(Supplementary Fig. S4).
When propagated through the Monte Carlo mass-balance model
(blue lines in Fig. 1), the basin-specific distributions of discharge
growth presented in Table 1 give a base-case 5th–95th percentile
Antarctic mass change of 14.7–12.6 cm SLR equivalent by 2100
(grey shading in Fig. 2a). The range widens considerably with time,
underscoring the role of discharge uncertainty in century-long
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LETTERS
NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE1845
b
8x
5x
PIG and B15R collapse discharge
Annual collapse probability in PIG and B15R
27 cm upper bound (2:1 partition)
100
Reaches upper
bound without
collapse
10¬2
Does not reach
upper bound
10¬4
0.0
2x
1.0
µPIG
Mean growth rate (µ , % yr¬1) in
East and West Antarctic marine-based basins
40 cm upper bound (1:1 partition)
Annual collapse probability in PIG and B15R
a
100
5x
10¬2
Does not reach
upper bound
10¬4
0.0
1.0
µPIG
Mean growth rate (µ, % yr¬1)
in West Antarctic marine-based basins
Figure 3 | Sensitivity analysis. a,b, Discharge assumptions that are compatible with a 95th percentile Antarctic ice-sheet mass loss of 27 cm (a) and
40 cm (b). Shading indicates the collapse discharge (c) in PIG and B15R at which each upper bound is reached for a given annual collapse probability (pc )
and positive shift in the growth rate distribution in a set of marine-based basins. In a, the set of marine-based basins includes both East and West
Antarctica (1, 8–11, 13–14 and 17 in Table 1); in b, the set of basins includes only West Antarctica (1, 13–14 and 17 in Table 1). Warmer colours indicate
increasingly high discharge associated with collapse; these rates of ice loss should be viewed as progressively less plausible. Symbols in a are discussed in
the text; blue symbols invoke a sea-level contribution from collapse in PIG and B15R.
projections. The median SLR of 1.1 cm is in agreement with
projections of a modest mass gain by Antarctica if changes in
discharge are not expected7 .
This prior probability distribution is updated by applying an observational constraint12 on the 1992–2010 cumulative continental
mass balance (orange lines in Fig. 1; see Methods). The updating
process weights mass-balance baselines and discharge growth rates
(see Supplementary Discussion, Figs S2 and S6), resulting in a
narrowed range of projections; the median and 95th percentile
projections of ice loss increase to 2.4 and 13.3 cm, respectively (blue
shading in Fig. 2a). This weighting has only a weak influence on
upper bounds, but the likelihood of a sea-level fall decreases to
less than 15%, primarily because negative continental mass-balance
baselines are strongly favoured.
In Fig. 2b, we compare our base-case projections with EISs
and SEMs. Although the probability of individual EISs is unclear,
their low to moderate Antarctic ice-loss projections fall within
or near our weighted base-case range. The upper bounds of EIS
analyses are substantially higher than our projections, driven by
their underlying assumption of collapse in PIG and/or B15R.
Comparison with SEM projections remains clouded by the
uncertain partition between SLR sources over their historical
calibration, and the widely varying upper bounds obtained from
different studies and when alternative data sets are used for
calibration. However, after we apply a partition to their 95th
percentile projections (see Methods), SEMs imply a higher upperbound Antarctic ice loss (⇠14–65 cm, denoted by red bars in
Fig. 2b) than our base case. With a 2:1 Greenland/Antarctica
partition, the mean upper bound across the analyses included in
Fig. 2 is approximately 27 cm. With a 1:1 partition, the mean upper
bound is approximately 40 cm.
At the coarse scale examined here, changes in three assumptions
lead to 95th percentile ice-loss projections that are compatible
with SEM and EIS upper bounds: positive shifts in discharge
growth rate distributions (including their form and range, and
the set of basins to which they apply)10 ; increased inter-basin
spatial correlation10 ; and abrupt, persistent, collapse. Each of
these assumptions embodies different prior beliefs about plausible
changes in ice dynamics and/or underlying physical processes, and
has substantially different implications on regional and continental
ice discharge (Supplementary Figs S4 and S5).
656
In Fig. 2b, we present probability distributions of Antarctica’s
mass balance associated with extreme changes in these assumptions.
First, we increase the correlation coefficient of discharge growth
rates across all ice-sheet basins to 1. Next, the discharge growth
rate distribution for all marine-based drainage basins in East and
West Antarctica (where sustained increases in discharge are more
physically justifiable)16,27,28 is shifted upwards by the historical
trend in PIG discharge (µPIG = 1.85% yr 1 ). We then increase
the probability of an abrupt change in PIG and B15R discharge
to 8⇥ SMB to 1: an immediate collapse. Although spatially
correlated discharge growth increases the spread of SLR projections,
its influence on upper bounds is limited relative to increases
in the likelihood of higher discharge in many drainage basins
and/or abrupt collapse.
To more clearly assess the dynamic implications of higher upper
bounds, we use this probabilistic framework to work backwards
towards sets of discharge assumptions that reach 27 and 40 cm SLR
equivalent ice loss with a 5% chance of exceedance (Fig. 3). Reaching either upper bound without collapse requires high discharge
growth across many Antarctic drainage basins. To reach a 27 cm
upper bound (Fig. 3a), the prior distribution of discharge growth
rates in all marine-based basins must be increased by approximately
0.9% yr 1 (black circle), reflecting an expectation of discharge
growth half of that observed for PIG over more than 40% of the icesheet area (with much higher growth rates possible in every basin).
Reducing the spatial extent of enhanced discharge implies higher
growth rates. For example, if discharge in East Antarctic basins is
assumed to be encompassed by our base-case assumptions, a 27 cm
upper bound requires a positive shift of 1.5% yr 1 in the growth
rate distribution of every West Antarctic basin, giving a 95–99.5%
chance of an increase in discharge (Supplementary Fig. S7).
Collapse in PIG and/or B15R decreases the requirement for
widespread discharge growth. Without changes in our base-case
assumptions in marine-based basins, a 27 cm upper bound for
Antarctica is achieved with a certain, instantaneous, increase of PIG
and B15R discharge to ⇠5⇥ SMB (blue circle), or a 1% annual
chance of collapse (equivalent to a 60% cumulative probability
before 2100) with a discharge ⇠8⇥ SMB (blue triangle). With a
1% annual collapse probability and discharge 5⇥ baseline SMB, this
upper bound is reached with an increase in discharge growth rates
of 0.4% yr 1 (blue star).
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Cumulative collapse probability
0.01
35
0.60
1.00
95th percentile PIG + B15R ice loss
(cm SLR equivalent)
8x
25
8x (PIG only)
4x
15
4x (PIG only)
5
10¬4
10¬2
100
Annual collapse probability
Figure 4 | Sensitivity of upper-bound SLR projections to localized
collapse. The 95th percentile, cumulative mass loss by 2100 of PIG and
B15R for a given annual (bottom axis) and cumulative (top axis) collapse
probability and collapse discharge. Black lines show Monte Carlo
simulations in which PIG and B15R are subject to collapse; grey lines show
simulations in which only PIG is subject to collapse (linear discharge
growth is assumed in B15R).
Although our base-case projections suggest that SEM upper
bounds are, in general, biased high, the wide range of SEM projections, and the uncertain fraction derived from Antarctica, prohibit an absolute statement regarding their validity. However, we
judge that the unique bedrock morphology25,27 and oceanographic
environment29 of regions undergoing rapid ice loss, projections
of gradual changes in climate around the margins of Antarctica16 ,
and the sensitivity of ice loss to climate observed in other modelling studies11,16,30 do not support a sustained and widespread
increase in discharge. This judgement, in turn, implies that the
additional Antarctic SLR implied by SEM upper bounds is derived
from regional collapse.
In this analysis, we do not attempt to assign a likelihood to
collapse; however, we suggest that any abrupt, sustained changes in
discharge implied by SEMs are: inconsistent with their linearized,
temperature-dependent, model; unlikely to be captured by their
historical calibration; and unlikely to be well represented by their
smooth representation of future SLR. Higher SEM upper bounds
imply a larger amount of ice loss through regional collapse and are
therefore more likely to be inconsistent. This conclusion applies
to more SEM analyses if a larger fraction of SEM upper bounds is
derived from Antarctica (as in Fig. 3b).
A complete reconciliation with SEMs requires the inclusion of
all SLR sources (for example, Greenland ice-sheet mass changes and
thermosteric SLR). In the short term, the importance of the unique
fingerprints of each SLR source to local sea-level change8 suggest
that research should prioritize projections of individual sources. As
these sources are included in this framework, improved SEMs may
be introduced as a constraint on global mean SLR.
Unlike SEMs, EISs explicitly consider a localized collapse,
however, they do not specify its likelihood. The pronounced
sensitivity of upper bounds to annual collapse probabilities below
1% (Fig. 4 and Supplementary Fig. S7) underscores the need
to quantify very low-probability events if increases in discharge
are high and sustained; otherwise, upper bounds (such as those
presented in earlier EISs) remain relatively uninformative.
In the absence of robust data or appropriate models, collapse
probabilities may be assessed using formalized expert elicitation28
based on physically based criteria (for example, climate model
simulations, bedrock morphology or palaeoclimate evidence).
Table 1 | Base-case discharge assumptions, and resulting (weighted) sea-level contribution by basin.
Basin name
Basin ID
Growth rate (µb ±
(⇥1.85% yr 1 )
b)
15
1990–2008: 1.0 ± 0.68
2009–2099: see Methods
0.69 ± 0.31
0 ± 0.25
0 ± 0.50
Median
SLR (cm)
95th percentile
SLR (cm)
1.5
2.4
3.2
0.9
2.5
5.5
2.0
6.0
WAIS
Amundsen Sea (⇢ = 0.2)
PIG (Pine Island)
B15R (Basin 15)
ABT (Abbot)
GTZ (Getz)
Ross Sea (⇢ = 0.2)
East Antarctic
West Antarctic
Weddell Sea (⇢ = 0.2)
East Antarctic
West Antarctic
Antarctic Peninsula
AP
16
17
14
12
13
0 ± 0.25
0 ± 0.50
1.0
1.4
0.1
0.7
2
1
0 ± 0.25
0 ± 0.50
0.9
0.2
0.1
3.5
18–19
0.25 ± 0.25
3–6
8–11
7
0 ± 0.25
0 ± 0.50
0 ± 0.25
EAIS (⇢ = 0.1)
Non-marine-based
Marine-based
Amery
2.4
1.2
2.3
0.2
5.7
1.2
2.5
0.9
The probability of collapse (pc ) is zero. In all basins, a SMB growth rate of 0.189 ± 0.011% yr 1 is applied; ⇢ = regional correlation coefficient. WAIS, West Antarctic Ice Sheet; EIAS,
East Antarctic Ice Sheet.
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However, improved confidence in upper bounds, and their eventual
lowering, may be more easily achieved by lessening the sensitivity
of SLR projections to the probability of abrupt changes at ice-sheet
margins. Figure 4 indicates that regional constraints on the location
and/or rate of discharge—gained through numerical modelling
and/or expert judgements applied at a finer spatial scale—are as
valuable as those on the probability of triggering events.
Continental-scale model simulations are limited in their representation of: small-scale climatic boundary conditions at ice-sheet
margins; physical processes that initiate collapse; and subglacial
boundary conditions and feedbacks that may enhance discharge
following a large stress perturbation11,16 . These processes are often
better represented in smaller-scale regional ice/ocean models31 .
Furthermore, regional model ensembles allow the investigation of a
wider range of uncertainty in boundary conditions and ice physics,
providing a spectrum of possible outcomes in addition to upper
bounds. In this framework, constraints derived from these ensembles may be incorporated either as prior assumptions (as for PIG
in this analysis), or on the posterior distributions of mass change at
finer spatial scales (Supplementary Figs S4 and S5). By integrating
such ensembles with smaller-scale ice-sheet and sea-level observations for calibration13 , and continental-scale model simulations,
this framework can generate a self-consistent presentation of the
complete range of plausible ice-sheet mass-balance projections,
greatly improving the basis for SLR-related decision-making14 .
Assuming that the reported uncertainty in ref. 12 corresponds to 1 standard
deviation, the observed Antarctic mass change M S12 defines a normally distributed
likelihood function, P(M S12 |M 2010 ) ⇠ N ( 1196 M 2010 ,4912 ) GT. The Bayesian
posterior distribution for Antarctic mass change, constrained by the continental
mass-balance estimates of ref. 12, is given by Bayes’s theorem:
Methods
We project only global mean SLR due to changes in Antarctica’s mass balance.
We use Monte Carlo sampling to propagate uncertainty in basin-by-basin
mass-balance projections. The methodology in ref. 10 is modified to include
SMB uncertainty, a probability of collapse in PIG and B15R, a constraint on the
SLR contribution of PIG derived from a numerical model, and an observational
constraint on Antarctic mass change. Each Monte Carlo simulation contains 40,000
realizations (k) that sample from a unique set of mass-balance assumptions (see
Supplementary Discussion).
Mass-balance baseline. For each Monte Carlo realization, we create a hybrid
set of mass-balance observations in which SMB and discharge (changes in mass
flux across the grounding line) in each basin (SMB1996,k
and Q1996,k
, respectively)
b
b
has an equal chance of being drawn from either of two recent input/output
21,22
22
analyses . These analyses are assumed to represent 1996 values . SMB and
discharge in basin 15 of ref. 22 is partitioned according to 2006 discharge in PIG
and B15R (100 and 161 GT yr 1 , respectively)23 . SMB1996 and Q1996 are always
taken from the same analysis.
Mass balance projections. The net mass balance of a basin is:
Mbk (t ) = SMBkb (t )
Qkb (t )
(1)
SMB(t ) and Q(t ) are assumed to be independent. Changes in SMB are
calculated by applying a growth rate k sampled from a normal distribution
⇠ N (0.19,0.11)% yr 1 . For discharge, separate assumptions are specified
for each drainage basin. The discharge of PIG and B15R, justification for these
assumptions, and examples of the annual SLR contributions for four Monte
Carlo simulations are provided in the Supplementary Discussion and Figs S3–S5.
Discharge in the 17 other drainage basins is calculated by applying normally
distributed growth rates ↵ ⇠ N (µb , b2 )% yr 1 to the baseline discharge10 (see
Table 1 and Supplementary Discussion).
Discharge growth rates are correlated within regions that flow into common
continental shelf seas, following equation (2) in ref. 10, with correlation coefficients
shown in Table 1. If a continent-wide correlation is specified (as in Fig. 2c), it is
implemented according to equation (3) in ref. 10.
Bayesian update of Monte Carlo realizations. The set of Monte Carlo realizations
for each basin constitute samples from the prior distribution P(Mb (t )) that may be
updated with observations at various spatial scales. Here, we weight each Monte
Carlo realization by its agreement with an observational estimate of the 1992–2010
cumulative Antarctic ice-sheet mass change12 .
The cumulative mass change in each realization between 1992 and 2010 is:
M 2010,k =
658
2010 X
19
X
t =1992 b=1
Mbk (t )
(2)
P(M 2010 |M S12 ) / P(M S12 |M 2010 )P(M 2010 )
(3)
2010,k
A smoothed kernel density estimate of the samples M
is constructed, with each
sample k weighted by its likelihood P(M S12 |M 2010,k ). The resulting weighted kernel
density estimate is the data-updated posterior distribution P(M 2010 |M S12 ).
The SLR distribution. The cumulative Antarctic contribution to SLR at time tf is:
SLRk (tf ) =
tf
19
X
1 mm X
M k (t )
360 GT t =1990 b=1
(4)
To construct posterior predictive distributions of sea level, a weighted kernel
density estimate is applied to the corresponding samples SLRk (tf ) of projected
SLR, with the same observational weights as described above. Projections in grey in
Fig. 2 are not weighted by the continental constraint.
The implied SEM upper-bound Antarctic ice loss. Attributing the Antarctic
contribution to SLR in SEM analyses is difficult owing to the aggregation of all
sources of SLR (ref. 17). Here, we assume that the difference between a 95th
percentile SEM projection and the upper bound of the Intergovernmental Panel
on Climate Change Fourth Assessment Report’s SLR projections using the A1B
scenario (50 cm; ref. 7) arises from the dynamic contribution of ice sheets.
We examine two partitions of this implied ice-sheet contribution,
although we caution that they may not constitute endmembers. The first
(2:1 Greenland/Antarctica) assumes that the 1992–2010 partition12 is indicative
of future ice loss. These observations, however, do not cover the entire SEM
calibration period, over which the contribution of both ice sheets is highly
uncertain15,18 . Furthermore, the order of magnitude higher rates of SLR implied
by extrapolations of these trends demand a sustained dynamic contribution,
which is more plausible in Antarctica. This assessment is consistent with a recent
expert elicitation, in which the median contribution of the ice sheets reflects a 2:1
Greenland/Antarctic ice-sheet partition, whereas the 95th percentile contribution
is weighted strongly towards Antarctica28 . Palaeoclimate evidence also suggests a
higher (roughly equal) partition between ice sheets in the last interglacial period32 .
We thus examine a 1:1 partition; larger fractional contributions from Antarctica do
not change the nature of our conclusions.
Received 25 June 2012; accepted 4 February 2013; published online
17 March 2013
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Acknowledgements
C.M.L. is grateful for financial support from the Science, Technology and Environmental
Policy programme in the Woodrow Wilson School of Public and International
Affairs at Princeton University and the Carbon Mitigation Initiative in the Princeton
Environmental Institute. The authors thank K. Keller, O. Sergienko and Y. Liu for many
helpful suggestions. We also thank A. Shepherd and the Ice Sheet Mass Balance Exercise
team for promptly providing data.
Author contributions
C.M.L, N.M.U and M.O. designed the research. C.M.L. conducted the data analysis
and wrote the manuscript. M.O and N.M.U. contributed extensively to the paper
writing, editing and revision.
Additional information
Supplementary information is available in the online version of the paper. Reprints and
permissions information is available online at www.nature.com/reprints. Correspondence
and requests for materials should be addressed to C.M.L.
Competing financial interests
The authors declare no competing financial interests.
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