Evolving understanding of Antarctic ice-sheet physics and ambiguity in probabilistic sea-level projections

TL;DR

This paper integrates physical Antarctic ice-sheet models into probabilistic sea-level projections, revealing significant potential increases in sea-level rise and population impact under high emissions, emphasizing the importance of adaptive strategies.

Contribution

It introduces a physical model-based approach to sea-level projections, improving sensitivity analysis and highlighting uncertainties in future ice-sheet contributions.

Findings

Median GMSL rise under RCP 8.5 increases from 79 to 146 cm.

Projected land inundation by 2100 could affect 197 million more people.

Future projections show over 10 m GMSL rise by 2300 under high emissions.

Abstract

Mechanisms such as ice-shelf hydrofracturing and ice-cliff collapse may rapidly increase discharge from marine-based ice sheets. Here, we link a probabilistic framework for sea-level projections to a small ensemble of Antarctic ice-sheet (AIS) simulations incorporating these physical processes to explore their influence on global-mean sea-level (GMSL) and relative sea-level (RSL). We compare the new projections to past results using expert assessment and structured expert elicitation about AIS changes. Under high greenhouse gas emissions (Representative Concentration Pathway [RCP] 8.5), median projected 21st century GMSL rise increases from 79 to 146 cm. Without protective measures, revised median RSL projections would by 2100 submerge land currently home to 153 million people, an increase of 44 million. The use of a physical model, rather than simple parameterizations assuming constant acceleration of ice loss, increases forcing sensitivity: overlap between the central 90% of simulations for 2100 for RCP 8.5 (93-243 cm) and RCP 2.6 (26-98 cm) is minimal. By 2300, the gap between median GMSL estimates for RCP 8.5 and RCP 2.6 reaches >10 m, with median RSL projections for RCP 8.5 jeopardizing land now occupied by 950 million people (vs. 167 million for RCP 2.6). The minimal correlation between the contribution of AIS to GMSL by 2050 and that in 2100 and beyond implies current sea-level observations cannot exclude future extreme outcomes. The sensitivity of post-2050 projections to deeply uncertain physics highlights the need for robust decision and adaptive management frameworks.

Tables

Table 1. Table 1: Projections of GMSL rise (cm)

	50	17–83	5–95	1–99	99.9
K14
RCP 8.5
2050	29	24–34	21–39	17–46	59
2100	79	62–101	51–123	40–159	232
2200	195	131–284	94–380	64–552	886
2300	318	175–516	98–737	37–1093	1929
RCP 4.5
2050	26	21–31	18–35	15–41	55
2100	59	44–77	35–95	26–128	205
2200	126	70–197	36–278	8–433	780
2300	192	70–349	0–531	-55–900	1717
RCP 2.6
2050	24	20–29	18–33	15–40	55
2100	49	36–66	28–84	20–120	203
2200	97	48–163	23–242	6–406	803
2300	142	32–288	-22–470	-57–847	1773
DP16
RCP 8.5
2050	31	22–40	17–48	13–54	59
2100	146	109–209	93–243	80–267	297
2200	719	595–896	558–962	525–1049	1193
2300	1169	980–1409	913–1552	861–1751	2006
RCP 4.5
2050	26	18–36	14–43	10–52	57
2100	91	66–125	50–158	39–180	197
2200	266	176–396	133–455	102–510	594
2300	421	275–595	211–696	163–806	995
RCP 2.6
2050	23	16–33	12–41	9–50	54
2100	56	37–78	26–98	18–111	122
2200	110	70–161	47–206	30–250	314
2300	142	83–230	50–300	22–404	552
Columns indicate percentiles of simulation frequency distributions.

Table 2. Table 2: Population exposure (millions of people)

Current population occupying land exposed to inundation under 2100 RSL projections
Region	Total Pop.	RCP 2.6/K14	RCP 2.6/DP16	RCP 8.5/K14	RCP 8.5/DP16
World	6,836	94.3 (73.3 - 127.6)	97.4 (75.0 - 131.1)	108.2 (82.3 - 153.5)	152.5 (106.2 - 235.5)
China	1,330	26.3 (19.1 - 37.5)	26.9 (19.8 - 38.3)	30.2 (21.8 - 45.0)	42.9 (28.3 - 67.0)
Bangladesh	156	7.6 (5.5 - 10.6)	8.0 (5.7 - 11.1)	8.9 (6.5 - 14.0)	14.0 (8.9 - 23.5)
India	1,173	6.8 (5.2 - 9.1)	7.1 (5.3 - 9.4)	8.0 (5.8 - 11.4)	11.5 (8.0 - 18.5)
Indonesia	243	5.4 (3.8 - 8.0)	5.6 (4.0 - 8.4)	6.3 (4.6 - 9.9)	9.8 (6.1 - 16.6)
Vietnam	90	11.5 (9.1 - 15.1)	11.7 (9.3 - 15.5)	12.9 (10.1 - 17.0)	17.0 (12.7 - 23.9)
Current population occupying land exposed to inundation under 2300 RSL projections
Region	Total Pop.	RCP 2.6/K14	RCP 2.6/DP16	RCP 8.5/K14	RCP 8.5/DP16
World	6,836	165.9 (55.4 - 495.9)	167.0 (82.8 - 357.9)	306.4 (102.1 - 704.0)	950.4 (765.6 - 1,162.6)
China	1,330	41.5 (11.2 - 125.6)	41.6 (17.3 - 97.8)	79.9 (20.7 - 171.1)	207.4 (171.1 - 251.9)
Bangladesh	156	29.7 (11.2 - 70.7)	29.9 (16.8 - 54.5)	49.8 (20.1 - 95.1)	117.0 (101.5 - 129.2)
India	1,173	12.7 (4.3 - 42.8)	12.8 (6.5 - 28.3)	25.1 (8.0 - 71.1)	105.3 (78.1 - 132.5)
Indonesia	243	9.1 (1.7 - 35.7)	9.0 (2.9 - 24.8)	19.6 (4.3 - 50.8)	65.7 (52.9 - 81.0)
Vietnam	90	16.2 (5.4 - 38.7)	16.2 (8.0 - 31.4)	27.4 (9.6 - 49.5)	57.0 (50.7 - 61.4)
Population currently living in land at risk of permanent inundation based on median (5th–95th percentile) RSL projections.
Population densities based on 2010 estimates.
The top five countries with the most exposure in 2300 under median RCP 8.5/DP16 are included in this table.

Table 3. Table S1: CMIP5 models used for thermal expansion and oceanographic processes

Model	Oceanographic			GIC
	RCP 8.5	RCP 4.5	RCP 2.6	RCP 8.5	RCP 4.5	RCP 2.6
access1-0	21	21
access1-3	21	21
bcc-csm1-1	23	23	23	23	23	23
bcc-csm1-1-m	21	21	21
canesm2	21	23	23	21	23	23
ccsm4	21	21	21	21	21	21
cmcc-cesm	21
cmcc-cm	21	21
cmcc-cms	21	21
cnrm-cm5	23	23	21	23	23	21
csiro-mk3-6-0	21	21	21	23	23	21
gfdl-cm3	21	23	21	21	21	21
gfdl-esm2g	21	21	21
gfdl-esm2m	21	21	21
giss-e2-r	23	23	23	23	23
giss-e2-r-cc	21	21
hadgem2-cc	21
hadgem2-es	21		23	23	23	23
inmcm4	21	21		21	21
ipsl-cm5a-lr	23	23	23	23	23	23
ipsl-cm5a-mr	21	23	21
miroc-esm	21	23	21	21	21	21
miroc-esm-chem	21	21	21
miroc5				21	21	21
mpi-esm-lr	23	23	23	23	23	23
mpi-esm-mr	21	21	21
mri-cgcm3	21		21	21	21	21
noresm1-m	21	23	21	21	23	21
noresm1-me	21	21	21
21 = to 2100, 23 = to 2300.

Table 4. Table S2: Projections of GMSL rise without bias correction (cm)

	50	17–83	5–95	1–99	99.9
RCP 8.5
2050	26	20–33	16–39	12–45	51
2100	134	89–202	76–224	64–244	277
2200	713	579–892	530–959	495–1047	1193
2300	1169	981–1410	913–1553	861–1752	2007
RCP 4.5
2050	22	16–28	12–33	9–38	42
2100	74	51–115	41–134	33–149	168
2200	241	134–386	104–439	79–489	571
2300	397	224–582	164–680	118–789	983
RCP 2.6
2050	20	15–25	11–29	8–33	37
2100	44	31–62	23–75	15–87	101
2200	86	58–123	40–153	25–194	267
2300	109	66–172	39–238	16–336	492

Table 5. Table S3: Projections of GMSL rise under different assumptions regarding ice-cliff collapse (cm)

	50	17–83	5–95	1–99	99.9
No Ice Cliff Collapse (VCLIF = 0 km/yr)
RCP 8.5
2050	35	31–39	28–43	25–47	54
2100	124	109–139	97–154	87–172	213
2200	603	560–661	532–722	507–810	986
2300	994	916–1119	874–1262	840–1474	1622
RCP 4.5
2050	33	29–37	26–40	24–43	46
2100	84	74–96	66–106	58–116	137
2200	196	164–235	142–273	122–320	420
2300	295	232–374	194–454	161–569	800
RCP 2.6
2050	32	29–35	26–38	24–41	43
2100	66	58–75	52–83	46–92	108
2200	129	111–156	101–186	93–225	301
2300	170	138–227	123–292	111–388	554
Slow Ice Cliff Collapse (VCLIF = 1 km/yr)
RCP 8.5
2050	23	18–30	14–37	11–43	48
2100	109	92–126	82–140	72–159	199
2200	621	580–679	551–742	521–831	988
2300	1032	953–1157	909–1302	865–1514	1655
RCP 4.5
2050	19	14–27	11–36	7–41	46
2100	64	49–82	40–94	31–108	123
2200	180	133–228	105–266	81–315	405
2300	297	222–384	174–468	130–582	791
RCP 2.6
2050	17	13–24	10–34	6–38	43
2100	38	27–55	19–75	12–87	100
2200	72	46–117	32–151	21–191	263
2300	96	50–173	27–240	9–332	534
Fast Ice Cliff Collapse (VCLIF = 5 km/yr)
RCP 8.5
2050	38	31–46	26–52	23–57	62
2100	213	185–242	144–259	127–277	307
2200	898	847–957	605–1021	559–1111	1265
2300	1398	1303–1525	972–1668	882–1880	2031
RCP 4.5
2050	31	25–39	20–50	17–55	59
2100	127	109–156	98–174	89–187	206
2200	400	342–451	246–491	207–545	627
2300	592	495–684	360–767	284–888	1070
RCP 2.6
2050	28	22–36	17–48	14–53	57
2100	71	56–94	46–106	39–116	127
2200	135	100–196	84–226	70–266	324
2300	174	111–269	87–333	67–427	559
Columns indicate percentiles of simulation frequency distributions.

Table 6. Table S4: Projections of GMSL rise under different assumptions regarding ice-shelf hydrofracturing (cm)

	50	17–83	5–95	1–99	99.9
No Hydrofracturing (CREVLIQ = 0 m per (m/yr)-2)
RCP 8.5
2050	39	33–49	29–54	26–59	66
2100	125	107–147	96–163	86–183	226
2200	588	545–646	520–709	499–802	985
2300	980	902–1105	860–1248	826–1458	1612
RCP 4.5
2050	38	32–48	29–53	25–57	61
2100	94	78–114	68–126	59–139	159
2200	216	174–263	148–304	125–354	462
2300	320	248–407	204–489	167–610	830
RCP 2.6
2050	36	31–47	28–51	25–55	58
2100	79	65–99	57–110	49–120	135
2200	157	123–193	108–224	97–267	344
2300	213	161–275	136–340	118–441	613
Strong Hydrofracturing (CREVLIQ = 150 m per (m/yr)-2)
RCP 8.5
2050	29	21–37	16–42	12–46	53
2100	142	103–209	87–231	75–249	280
2200	715	605–892	571–959	548–1047	1189
2300	1166	995–1404	931–1551	886–1734	1982
RCP 4.5
2050	24	17–32	12–37	8–40	44
2100	86	58–124	45–142	33–155	170
2200	260	160–395	118–448	85–495	584
2300	414	259–595	191–689	135–798	1002
RCP 2.6
2050	22	15–30	11–34	7–38	42
2100	52	33–71	23–80	14–90	105
2200	100	61–136	38–166	23–205	287
2300	124	72–186	36–251	11–346	529
Columns indicate percentiles of simulation frequency distributions.

Full text

\journalname

Earth’s Future

Department of Earth & Planetary Sciences and Institute of Earth, Ocean & Atmospheric Sciences, Rutgers University, New Brunswick, NJ, USA Department of Geosciences, University of Massachusetts–Amherst, Amherst, MA, USA Center for Climate Systems Research, Columbia University, New York, NY, USA Department of Earth & Planetary Sciences, Harvard University, Cambridge, MA, USA Climate Central, Princeton, NJ, USA Woodrow Wilson School of Public & International Affairs and Department of Geosciences, Princeton University, Princeton, NJ, USA Earth and Environmental Systems Institute, Pennsylvania State University, University Park, PA, USA Current address: Boston College, Boston, MA, USA

\correspondingauthor

R. E. Kopprobert.kopp@rutgers.edu

{keypoints}

Incorporating ice-shelf hydrofracturing and ice-cliff collapse mechanisms highlights ambiguity in post-2050 sea-level projections.

These mechanisms make post-2050 sea level more heavily emissions dependent and can significantly revise high-emissions projections upwards.

Current Antarctic retreat by different processes than and exhibits little correlation with late-century changes.

1 Introduction

Probabilistic sea-level rise projections aim to characterize plausible Bayesian probability distributions – usually conditional upon greenhouse gas emissions scenario – of future global-mean sea-level (GMSL) and local relative sea-level (RSL) changes over time (e.g., Kopp et al., 2014, 2016; Mengel et al., 2016; Slangen et al., 2014; Jackson and Jevrejeva, 2016; Wong et al., 2017; Nauels et al., 2017). Those projections explicitly labeled ‘probabilistic’ generally aim to include estimates not just of central or ‘likely’ ranges (e.g., Church et al., 2013a), but also estimates of the tails of probability distributions; others are conceptually similar but do not attempt to estimate low-probability hazards (e.g., Slangen et al., 2014). Many probabilistic projections are developed through frameworks that incorporate regional contributions to sea-level change, allowing them to be combined (for example) with estimated distributions of local flood frequencies to estimate an expected amplification of local flood frequencies over time (e.g. Buchanan et al., 2017) and vertical elevation allowances needed to maintain expected flood frequency at a constant level (e.g., Buchanan et al., 2016; Slangen et al., 2017). There is a good degree of agreement on future GMSL among many of the studies producing probabilistic projections, as well as between these studies and the IPCC’s Fifth Assessment Report (AR5: Church et al., 2013a). In some cases, this is by construction; for example, Kopp et al. (2014) used ice-sheet projections based on a reconciliation of the structure expert elicitation study of Bamber and Aspinall (2013) with AR5. In other cases, this agreement represents independent lines of evidence leading to similar conclusions: for example, the agreement between AR5 and recent semi-empirical models relating GMSL change to global-mean temperature (Kopp et al., 2016; Mengel et al., 2016).

This agreement is not universal, however (e.g., Jackson and Jevrejeva, 2016), and may be misleading. The response of polar ice sheets to forcing remains an area of ‘ambiguity’ and ‘deep uncertainty’ (Kasperson, 2008; Heal and Millner, 2014), for which it is currently impossible to identify a uniquely ‘correct’ probability distribution. Approaches beyond historically calibrated statistical models (Kopp et al., 2016; Mengel et al., 2016) and consensus-based expert assessment (Church et al., 2013a) can provide additional reasonable ways of estimating probability distributions of the ice-sheet response. Notably, structured expert elicitation about the Antarctic ice sheet (AIS) response (Bamber and Aspinall, 2013) yielded a broader range than consensus-based AR5 expert assessment. Direct use of results from Bamber and Aspinall (2013), without reconciliation with AR5, drives the higher projections in Jackson and Jevrejeva (2016). Most physical models of the AIS response have been generally consistent with AR5 and Kopp et al. (2014): for example Ritz et al. (2015)’s physical-statistical model estimated a 95th percentile AIS contribution to GMSL of 30 cm sea-level equivalent between 2000 and 2100, consistent with Kopp et al. (2014)’s 33-35 cm. Golledge et al. (2015)’s deterministic model found a 39 cm contribution under high emissions and their higher, sub-grid interpolation of basal ice melt. However, DeConto and Pollard (2016) (henceforth, DP16) found that the inclusion in a physical model of previously omitted processes such as ice-shelf hydrofracturing and structural collapse of tall, marine-terminating ice cliffs has the potential to drive an order-of-magnitude increase in Antarctic mass-loss rates.

Ideally, the integration of process models into probabilistic frameworks such as those of Kopp et al. (2014) (henceforth, K14) and Jackson and Jevrejeva (2016) would involve the development and use of fast models – or fast statistical emulators of more complex models – in a mode that allows Monte Carlo sampling of key uncertainties and the conditioning of uncertain parameters on multiple observational lines of evidence. The development of such fast models or model emulators is an involved task, however, and the publication of DP16 triggered an increase in stakeholder interest and a demand for more expeditious approaches. For example, regional sea-level assessments for the City of Boston (Douglas et al., 2016) and State of California (Cayan et al., 2016) adopted the K14 framework but substituted, as a set of discrete samples, an ensemble of Antarctic ice-sheet projections from DP16. For the United States’ Fourth National Climate Assessment, a U.S. Interagency Task Force on Sea-Level Rise report (Sweet et al., 2017) semi-quantitatively assessed how DP16’s results might shift the probability distribution of future GMSL change.

Here, we extend the approach of Douglas et al. (2016) and Cayan et al. (2016) to a global scale, substituting DP16’s AIS ensembles for K14’s expert assessment- and expert elicitation-based probability distribution. This substitution allows more complex temporal dynamics than the simple assumptions of constant acceleration that underlie many expert-judgement-based projections (e.g., Bamber and Aspinall, 2013; Little et al., 2013a, b; Kopp et al., 2014). It also allows identification of the importance of different physical assumptions regarding AIS for total GMSL and RSL projections. However, this approach comes with some limitations. DP16 did not originally develop their ensemble to produce a probability distribution. Instead, they sampled their key physical parameters from a discrete, somewhat arbitrary set of values, and they integrated paleo-observations via a simple pass/fail test. A more probabilistic approach would have employed continuous prior probability distributions for key parameters and integrated observations – potentially instrumental observations as well as paleo-constraints – via Bayesian updating. The more limited approach taken means their ensemble may give excessive weight to certain ensemble members, while also having unrealistically thin tails delimited by the discrete values selected for the prior parameter values.

This substitution approach differs from that taken by Le Bars et al. (2017), who sought to integrate DP16 projections into comprehensive projections of GMSL rise between 2000 and 2100. They fit a normal distribution to DP16 ensembles for 2100, while also introducing a linear dependence of AIS mass loss on global mean surface temperature. In contrast to the approach here, their approach neglects the non-normality in the DP16 ensemble and gives significant weight to values represented in the tails of their fitted normal distributions but not within the physically modeled DP16 projection ensemble. The approach in their current paper is thus more conservative with respect to extrapolation beyond the DP16 ensemble values. In addition, by using the DP16 projections directly, this study considers not just a single time point but also the course of sea level over time. Leveraging the K14 projection framework, this study also ties global projections to their regional manifestations.

Because of the limitations associated with using the DP16 projections directly, the resulting GMSL and RSL projections should not be viewed as constituting well-constructed Bayesian probability distributions. Accordingly, we refer to the resulting distributions as simulation frequency distributions, not probability distributions; this terminological choice parallels that of National Academies of Sciences, Engineering, and Medicine (2016) in the context of social cost of carbon dioxide estimates. These simulation frequency distributions can be used in some contexts – e.g., decision frameworks leveraging multiple alternative probability distributions – as if they were probability distributions. Doing so effectively treats the DP16 choices of parameter values as though they constituted a well-constructed, equally weighted prior, and the DP16 paleo-constraints as though they were well-represented by uniform likelihood distributions. Given the weaknesses in these assumptions, we would advise against using these simulation frequency distributions in isolation as new ‘best-estimate’ probability distributions.

2 Methods

2.1 Projections Framework

The framework employed to generate GMSL and RSL projections in this analysis is based on that of K14. The K14 framework combines multiple lines of information to construct probability distributions for key contributors to GMSL and RSL change. It employs a joint probability distribution for global mean thermal expansion and regional ocean dynamics derived from the Coupled Model Intercomparison Project Phase 5 (CMIP5) (Taylor et al., 2012) ensemble. Its projections of glacier mass-balance changes are derived from the Marzeion et al. (2012) surface mass-balance model, forced by the CMIP5 ensemble. Following the approach of Rahmstorf et al. (2012), its projections of the global-mean contribution of anthropogenic changes in land-water storage are based upon historical relationships between human population, dam construction, and groundwater withdrawal (Chao et al., 2008; Wada et al., 2012; Konikow, 2011). The regional contributions of non-climatic effects such as glacio-isostatic adjustment, tectonics, and sediment compaction are based upon a spatiotemporal statistical model of tide-gauge observations. Ice sheet contributions are derived from the AR5 expert assessment and the structured expert elicitation of Bamber and Aspinall (2013), as described below. Glacier and ice sheet projections are translated into RSL changes using static-equilibrium fingerprints for eighteen glacier regions, the Greenland Ice Sheet, the West Antarctic Ice Sheet (WAIS), and the East Antarctic Ice Sheet (EAIS) (Mitrovica et al., 2011).

Bamber and Aspinall (2013) elicited from fourteen experts central 90% probability estimates for the rate of GMSL rise in 2100 due to the Greenland ice sheet, WAIS and EAIS. They did not distinguish between surface-mass balance and dynamic contributions, nor did they distinguish between emissions scenarios. In turning these rates into cumulative 21st century GMSL rise contributions, they assumed a linear increase in rates based on the experts’ rate estimates for the last decade and for 2100.

AR5 assessed the likely (central 66% probability; see exegesis by Church et al. (2013b)) range of Greenland and Antarctic contributions in 2080-2099, distinguishing between surface-mass balance and dynamic terms. They did not distinguish between EAIS and WAIS. For the dynamic AIS contribution, they did not distinguish among RCPs.

K14 combined the Bamber and Aspinall (2013) and AR5 approaches in a manner intended to retain consistency with the likely ranges of AR5. In particular, K14: (1) calculated probability distributions for EAIS, WAIS, and Greenland changes over time from Bamber and Aspinall (2013), assuming linear changes in rates; (2) calculated probability distributions for AIS and Greenland over time based on the AR5 likely ranges for 2080–2099, again assuming linear changes in rates to achieve these values; (3) added a time-varying factor to the first set of distributions so the medians of the two sets align; (4) separated the AR5-derived Antarctic distribution into EAIS and WAIS terms by assuming the EAIS/WAIS ratio is the same as in the median projection from the first set; and (5) applied multipliers (separately for values greater than and less than the median) to the difference of the values in the final distribution from the distribution’s median, so that the central 66% probability range matches that of AR5.

2.2 Revised Antarctic projections

In this paper, we compare two sets of projections. The first, which we label K14, follows the original methodology of K14, extended in space and time. The second, which we label DP16, replaces the AIS projections of K14 with projections based on new physical modeling (DeConto and Pollard, 2016). These processes include the influence of surface meltwater, driven by summer temperatures above freezing and the increasing ratio of rain to snow in a warming climate, on the penetration into ice shelves of surface crevasses that can lead to hydrofracturing. Hence, in DP16, buttressing ice shelves can thin or be lost entirely due to sub-ice ocean warming, the extensive spread of surface meltwater, or a combination of the two. In places where thick, marine-terminating grounding lines have lost their buttressing ice shelves, a wastage rate of ice is applied locally at the tidewater grounding line in places where vertical ice cliffs are tall enough to produce stresses that exceed the yield strength of the ice (see DeConto and Pollard (2016) and Pollard et al. (2015) for complete formulation).

Three uncertain but key model parameters relate to (1) the rate of sub-ice shelf melt rates in response to warming ocean temperatures (OCFAC), (2) the sensitivity of crevasse penetration to meltwater input (hydrofracturing) (CREVLIQ), and (3) the maximum rate of cliff collapse (VCLIF). Because, as discussed below, there are no modern analogues to widespread ice-cliff failure, model performance cannot be adequately judged relative to Holocene or recent trends in ice-sheet behavior. Instead, the new model physics were tested relative to past episodes of ice sheet retreat during the Pliocene ($\mathord{\sim}3$ Ma) and the Last Interglacial (LIG, $\mathord{\sim}125$ Ma), when Antarctic ocean and surface air temperatures were warmer than today (Capron et al., 2014; Rovere et al., 2014). The three key parameters were varied systematically. From an initial 64 versions of the ice sheet model, 29 were found to satisfy both Pliocene and LIG sea-level targets, with Antarctic contributions to GMSL ranging between 5 to 15 m (Pliocene) and 3.6 to 7.4 m (LIG). The range of oceanic melt rate model parameters passing the Pliocene and LIG sea-level tests are comparable to those determined from a large, 625-member ensemble of the last deglacial retreat of the West Antarctic Ice Sheet using the same ice sheet model (Pollard et al., 2016); however, the deglacial simulations do not provide guidance on hydrofracturing and ice-cliff physics, because the background climate was too cold to trigger those processes.

One challenge of formulating a parameterization of ice-cliff physics is the lack of observations of marine-terminating ice without buttressing ice shelves and of sufficient thickness ($\mathord{\sim}1000$ m) to allow subaerial ice cliffs tall enough ($\mathord{\sim}100$ m) to drive structural collapse (Bassis and Walker, 2011). The few calving fronts of this scale that exist today (e.g., Helheim and Jakobshavn Glaciers on Greenland, and Crane Glacier on the Antarctic Peninsula) are experiencing rates of calving and structural failure at the terminus, comparable to the seaward flow of the glaciers, on the order of $\mathord{\sim}2$ to $>12$ km/yr (e.g., Howat et al., 2008; Joughin et al., 2014; Wuite et al., 2015). Unlike several major Antarctic outlet glaciers, these Greenland outlet glaciers are in relatively narrow (5–12 km wide), restricted fjords, with substantial mélange (a mix of ice bergs and sea ice that can provide some supporting buttressing/back pressure at the terminus), and supportive, lateral shear along the fjord walls. Hence, using observed rates of cliff collapse to constrain the model physics representing these processes could lead to underestimates.

In Antarctica, there is potential for much wider ice cliffs to form along vast stretches of the coastline if floating ice tongues and shelves are lost. For example, the throat of Thwaites Glacier is about 120 km wide, but at present its grounding line is mostly on bedrock too shallow (about 600 m deep; Millan et al. (2017)) to drive extensive structural failure at the terminus (Bassis and Walker, 2011). In the DP16, the highest maximum allowable rate of cliff collapse (VCLIF) – the maximum horizontal rate of ice loss applied at the marine ”tidewater” calving terminus where ice cliffs are tall enough to generate stresses that exceed the strength of the ice – is 5 km/yr. This rate is about half the rate of mass wastage at the front of Jakobshavn, which currently has a relatively stable terminus position but is flowing seaward at $>12$ km/year (Joughin et al., 2012). To include the potential for even faster rates of ice sheet mass loss than in the existing model formulation, future work should consider a wider range of parameter space.

We note that the paleo-sea-level targets used to test and calibrate the model physics provide limited guidance regarding potential rates of ice-sheet retreat. While Kopp et al. (2009) do provide an estimate of the rate of sea-level rise contributed by the Antarctic Ice Sheet during LIG retreat, both their temporal resolution and their ability to attribute GMSL changes to AIS are limited. Moreover, given limited Antarctic atmospheric warming during the LIG relative to the Pliocene, initial WAIS retreat was more likely driven by oceanic warming than atmospheric warming (DeConto and Pollard, 2016), and therefore offers little in terms of validating rates of retreat driven by extensive surface melt, hydrofracture, and cliff collapse.

As described in DeConto and Pollard (2016), the 29 versions of the ice sheet model satisfying geological constraints were used to simulate future ice-sheet retreat following RCP 2.6, 4.5, and 8.5 greenhouse gas pathways. In the future simulations, time-evolving oceanic melt rates were driven by NCAR CCSM4 (Gent et al., 2011; Shields and Kiehl, 2016) sub-surface ocean temperatures. Surface mass balance and meltwater production rates were calculated from monthly air temperatures and precipitation provided by the RegCM3 regional climate model (Pal et al., 2007) run offline and bias-corrected relative to a modern climatology (DeConto and Pollard, 2016; Le Brocq et al., 2010).

Coupled atmosphere-ocean models are known to struggle with subsurface ocean temperatures in the circum-Antarctic (Little and Urban, 2016). To minimize the effects of a general cold bias in NCAR CCSM4 Antarctic Shelf Bottom Water in the Amundsen and Bellingshausen Seas, a correction of 3∘C was applied to ocean temperatures at 400 m depth. This bias correction is meant to compensate for the recent warming observed there (Schmidtko et al., 2014). The correction is greater than the actual temperature offset, but given the formulation of the sub-ice melt rate parameterization used in DP16, a 3∘C correction is required to bring modern oceanic sub-ice shelf melt rates closer to observations (Rignot et al., 2013). The effect of not using the ocean temperature/melt-rate correction in future simulations is shown in Supporting Information.

2.3 Detection simulation

To simulate the process by which new observations of GMSL change can help detect whether the world is on a path leading to high or low levels of GMSL rise, we first define GMSL scenarios in a manner similar to Sweet et al. (2017). In particular, we pool the simulations of GMSL rise under RCP 2.6, RCP 4.5, and RCP 8.5, and then filter the pooled set to arrive at sets of simulations consistent with either $50\pm 10$ cm or $200\pm 10$ cm of GMSL rise between 2000 and 2100. We use the 5th-95th percentile range of these filtered sets to define scenario time paths. At each decade from 2000 to 2100, for each scenario, we compute the simulation frequency distribution of GMSL rise in 2100, conditional upon the observed GMSL in the decade being within the bounds of the scenario’s time path. Finally, we compare the resulting conditional distributions to assess the detectability in a given decade of the difference between a pathway leading to about 50 cm of GMSL rise in 2100 and one leading to about 200 cm of GMSL rise.

2.4 Extensions of the spatial and temporal domain

The projections framework in this paper has a more extended spatial domain than the original K14 projections. While the original K14 projections were generated only at the precise location of tide gauges, here we also generate projections at points on a $2^{\circ}\times 2^{\circ}$-resolution global grid that intersect world coastlines. At these points, we use the spatiotemporal statistical model described in K14 to estimate (with larger errors than at the tide-gauge sites) the long-term, non-climatic, background contribution to RSL change. The projection assumes that the background rate of change estimated from tide-gauge data continues unchanged over the duration of the projections.

The projection framework also has a more extended temporal domain than the original K14 projections. Whereas the original K14 projections end in 2200, here we generated projections to 2300. This extension requires no computational modifications to the K14 framework. However, we regard this time frame as more appropriate when considering projections in which Antarctic ice sheet behavior are determined by a physical model rather than by a simple, temporally quadratic projection.

2.5 Assessment of population exposure

As an integrative metric of RSL changes, we assess the population currently occupying land threatened with submergence under different sea level rise projections. To do this, we compare land elevations from NASA’s 1-arcsec SRTM 3.0 digital elevation model (NASA JPL, 2013) against nearest-neighbor water elevations derived from adding the K14 and DP16 projection grids to measured local mean sea surface elevation augmented by a modeled tidal supplement. We intersect the resulting inundation surfaces with contemporary population (Bright et al., 2011) and national boundary (Hijmans et al., 2012) data to estimate current national populations occupying land at risk of permanent submergence. SRTM data are the most practical option and widely used for global coastal exposure research, but bias estimates low (Kulp and Strauss, 2016). For each set of sea-level rise projections, we assess the population exposed assuming each grid cell followed its 50th, 5th, or 95th percentile RSL projection. Further details are provided in the Supporting Information. We emphasize that the resulting values are not projections of the impacts of RSL change, which would require a dynamic model considering both population growth and migration away from inundated regions; rather, population here serves as a convenient integrative metric.

3 Results

The K14 Antarctic projections – like those of Bamber and Aspinall (2013) and Little et al. (2013a, b), among others – assumed that changes in the rates of change in ice-sheet mass balance occurred linearly. For example, Bamber and Aspinall (2013) elicited expert opinion on the rate of Antarctic ice sheet mass change in 2100, and assumed that the elicited rate was achieved following a linear growth rate. The result is a quadratic change in ice volume over time. K14 took the same approach (Figures 1 and 2). By contrast, process modeling as in DP16 shows considerably more complex behavior, with periods of rapid increases in mass loss rate as individual sectors of ice sheet collapse, and other intervals of stable or declining rates of retreat (Figure 1). Sizable non-linearity appears in all simulations under strong forcing (Figure 1, RCP 8.5) and under all forcings in almost all simulations with high maximum rates of ice-cliff collapse (Figure 1, purple and magenta curves).

In the first half of the 21st century, the range spanned by DP16 Antarctic projections is similar to that spanned by K14 (-10 to +23 cm contribution to GMSL in 2050, vs. a 1st–99th percentile range of -2 to +14 cm under K14). Both sets of projections show minimal emissions-scenario dependency in the first half of the 21st century. The central tendency among the DP16 projections is slightly higher, with a median contribution to GMSL of about +5 cm under DP16, compared to a median of +2 cm under K14. These slightly higher values are driven by the ocean-temperature bias correction, which is needed to improve consistency with observed oceanic sub-ice melt rates in the Amundsen and Bellingshausen Sea sectors of West Antarctica (Rignot et al., 2013). Without this correction, there is a tendency toward Antarctic growth in the early decades of the century (in 2050, RCP 8.5: median -3 cm, range of -9 to +12 cm, RCP 2.6: median -2 cm, range of -10 to +6 cm). However, even with the bias correction, Antarctica’s median contribution to GMSL is still  0.1 mm/yr, which is about a factor of 3 less than that currently observed for the early 21st-century (Shepherd et al., 2012; Church et al., 2013a; Harig and Simons, 2015). Overall, the substitution of DP16 has a very limited effect on mid-century GMSL projections (Table 1).

Under strong forcing, the overall picture changes dramatically by the end of the 21st century, with several of the DP16 simulations leading to AIS contributions to GMSL exceeding +1 m by 2100 under RCP 8.5 (Figure 2). These high projections are driven by high maximum rates of ice-cliff retreat (VCIF = 5 km/yr) in combination with non-zero sensitivity of ice shelves to hydrofracturing (CREVLIQ ¿ 0) (Figures 2, S11). As a consequence, the median DP16 GMSL projections for 2100 under RCP 8.5 reaches 146 cm, the 98th percentile projection under K14. The low tail is curtailed by the incorporation of physical modeling, with a 1st percentile of 80 cm exceeding the median of K14. With a high VCLIF, the median GMSL projection reaches 213 cm (in excess of the 99th percentile of K14); with no cliff collapse mechanism or no hydrofracturing, it is reduced to about 125 cm (96th percentile of K14) (Supporting Table S3, S4).

DP16 RSL projections indicate the risk of significant changes to the global coastline by 2100. Without protective measures, the 5th–95th percentiles of RSL projections under DP16 would inundate land currently home to 106–236 million people. This contrasts with 82–154 million people under the K14 projections (Table 2, full table in Data Set 6).

A significant enhancement of the AIS contribution to GMSL also occurs for 2100 under moderate forcing: the median DP16 total GMSL projection of 91 cm under RCP 4.5 is consistent with the 93rd percentile of K14. The low tail is modestly curtailed: the DP16 1st–99th percentile values for RCP 4.5 (39–180 cm) resemble the K14 9th–99.8th percentile range. Under low forcing (RCP 2.6), there is little effect, with the DP16 1st–99th percentile range (18–111 cm) resembling the K14 0.5th–99th percentile range.

These differences build over the 22nd and 23rd century. By 2300, under RCP 8.5, the median DP16 GMSL projection of 11.7 m exceeds the K14 99th percentile. Although the ice-cliff collapse mechanism contributes to this projection, the median projection remains as high as 10.0 m by 2300 even without it (Supporting Table S3). Without protective measures, median DP16 RSL projections would submerge land currently home to 950 million people worldwide, a roughly three-fold increase relative to K14 (Table 2). The DP16 1st–99th percentile range (8.6–17.5 m) resembles the K14 97th–99.8th percentile range. Under RCP 4.5, the median DP16 GMSL projection of 4.2 m resembles the K14 90th percentile, and the DP16 1st–99th percentile range (1.6–8.1 m) resembles the K14 42nd–98th percentile range. The median is reduced to 3.0 m (75th percentile of K14) without the ice-cliff collapse mechanism, and 3.2 m (79th percentile of K14) without the hydrofracturing mechanism. Under RCP 2.6, by contrast, the median DP16 GMSL projection of 1.4 m matches the K14 median, and the DP16 1st–99th percentile range (0.2–4.0 m) resembles the K14 14th–92nd percentile range.

Taken together, the incorporation of the DP16 AIS ensemble pulls the projections much higher by 2100 and beyond under RCP 4.5 and especially RCP 8.5 (Figure 3). It thus leads to a significant reduction in overlap among projections of GMSL change based upon different emissions scenarios. This is to be expected based on the difference in construction between the K14 Antarctic projections and the DP16 projections. In K14, as in AR5, AIS surface mass balance is scenario-dependent, but the ice-sheet dynamic term is treated as scenario-independent: it is assumed that the uncertainty in physical understanding of ice-sheet behavior swamps the forcing uncertainty. By contrast, the physical model of DP16 yields a strong forcing dependence.

As a consequence of this difference, the proportion of total projection variance attributable to emissions changes significantly with the incorporation of the DP16 ensemble (Figure 4). Under K14, relative to RCP 4.5, thermal expansion is initially the dominant contributor to projection variance (accounting for about 70% of total variance in 2020). By 2060, AIS accounts for one-third of total variance and is the single largest contributor. The AIS share grows over time, accounting for more than 60% of total variance by 2300. Assuming RCP 2.6, 4.5 and 8.5 are all treated as equally likely, scenario uncertainty accounts for only $\mathord{\sim}10\%$ of total variance in 2050, a share that grows to 20–30% by 2070 and stays in this range through 2300.

Under DP16, physical uncertainty in AIS initially dominates total variance (89% in 2020). This share declines over time, predominantly losing out to emissions scenario uncertainty, which grows from 8% of total variance in 2050, to 45% in 2070, to 65% in 2100, and continues to grow to 89% in 2250. This shift reflects the larger sensitivity of the DP16 AIS projections to emissions scenario.

The assumption of a simple linear change in rate of mass loss underlying K14 leads to a perfect correlation between the rate of AIS mass loss observed in the near term and that projected for the long term (Figure 5a,b). If this assumption were correct, knowing that AIS mass loss in the first decades of this century fell in the middle of the estimated distribution would rule out high-end mass loss late in the century or beyond. By contrast, DP16 projections reveal no correlation between the AIS contribution to GMSL in 2020 and that in 2100 ($r=-0.08$, pooling across RCPs and both with and without an ocean temperature adjustment), and only a weak correlation between the AIS contribution in 2050 and in 2100 ($r=0.26$). In the second half of the century, observed AIS behavior becomes more strongly predictive of long-term behavior; the correlation with the AIS contribution to 2300 grows from $r=0.26$ in 2050 to $r=0.82$ in 2100, $r=0.97$ in 2150, and $r=0.997$ in 2200. The general lack of correlation between the AIS contributions in 2020 and 2100, and the rapidly strengthening correlations in the second half of the 21st century, are caused by a transition in the model from an ocean-dominated driver of ice-shelf loss (and reduced buttressing) to an atmosphere-dominated driver via hydrofracturing.

The lack of correlation between early 21st century and subsequent projections has important implications for the ability of GMSL observations to constrain future GMSL rise. In K14, simulations consistent with $50\pm 10$ cm of GMSL rise in 2100 diverge from those consistent with $200\pm 10$ cm of GMSL rise by the 2020s (Figure 5c). The median conditional projections for 2100 under the 200 cm scenario exceed the 95th percentile under the 50 cm scenario by 2030, and the 5th percentile of the 200 cm conditional distribution exceeds the 95th percentile under the 50 cm scenario shortly thereafter. 95% of projections for 2100 under the 200 cm time path exceed 100 cm in the 2030s and 150 cm in the 2040s (Figure 5d). By contrast, the more complex temporal dynamics of the DP16 simulations delays the divergence of the 50 and 200 cm time paths until around 2050 (Figure 5e). The median conditional projection for 2100 under the 200 cm scenario does not exceed the 95th percentile of the 50 cm scenario until the 2050s, with the 5th percentile of the 200 cm conditional distribution exceeding the 95th percentile under the 50 cm scenario in the 2060s. 95% of projections for 2100 under the 200 cm time path exceed 50 cm in the 2040s, 100 cm in the 2060s, and 150 cm in the 2070s (Figure 5f).

The effect of DP16 on RSL projections is as would be expected based on the change in projected WAIS and EAIS contributions and their associated static-equilibrium fingerprints (Figure 6 and Supporting Information). Relative to K14, the effect on RSL projections for 2050 is minimal ($<4$ cm). By 2100, however, the increase in median ASL contribution decreases projected RSL rise in the Antarctic, while enhancing it most strongly in in a geographic swath including North America, the central Pacific, Australia, southeast Asia, and parts of India and Africa same. (Detailed simulation frequency distributions of RSL at tide gauge sites and on the global coastal grid are provided in the Supporting Information.)

4 Discussion and Conclusions

The replacement of the probabilistic, expert-assessment- and expert-elicitation-based AIS projections of K14 with the physical-model-based projections of DP16 leads to a number of significant effects on GMSL and RSL projections.

First, the use of explicit physics including novel ice-shelf-hydrofracturing and ice-cliff-collapse mechanisms in DP16 leads to a significant upward shift in central projections for strong (RCP 8.5) and moderate (RCP 4.5) forcing scenarios. The DP16 simulations provide physically meaningful pathways that can lead to $>2.0$ m of total GMSL rise by 2100 under RCP 8.5 and $>1.5$ m under RCP 4.5 (Oppenheimer and Alley, 2016; Sweet et al., 2017).

Second, in the second half of this century and beyond, sea-level projections incorporating the DP16 ensemble are significantly more forcing-sensitive than the K14 projections. Due primarily to the significant number of simulations involving collapse of parts of AIS under strong forcing, the gap in the median GMSL projection for 2100 between RCP 8.5 and RCP 2.6 grows from 30 cm under K14 to 90 cm under the projections under DP16. Under RCP 2.6 and DP16, the 99th percentile projection remains below 2 m through 2200. If these findings are correct, they point to a significantly larger mitigation benefit than indicated by the AR5 or K14 sea-level projections.

Third, the DP16 projections indicate a much weaker correlation between the near-term behavior of AIS and its contribution to GMSL rise over the course of this century and beyond. Finding the planet on a ‘moderate’ sea-level rise pathway over the first half of the 21st century thus cannot exclude ‘extreme’ outcomes subsequently. For end-users employing discrete scenarios of sea-level rise, such as those constructed by Sweet et al. (2017), this means that ‘extreme’ future scenarios need to be considered even if they overestimate current rates of sea-level rise. Constraining the future behavior of the AIS requires more detailed process-based modeling than the simple relationships used by Bamber and Aspinall (2013), K14, and others would indicate.

That said, end-users of sea-level rise projections should be cognizant of single-study bias: the DP16 simulations should be viewed as expanding scientific understanding of the space of the physically coherent, rather than as offering firm projections of what will be. More robust projections of future Antarctic contributions to sea-level rise require a more through exploration of appropriate values for parameters such as the maximum rate of ice cliff retreat and the sensitivity of ice-shelves to hydrofracturing, as well as of uncertainty in the regional climate response to forcing. They also require more physically based representations of key processes, including structural cliff failure. Currently, the potential for model intercomparisons is hampered by the lack of representations of these processes in most continental-scale ice-sheet models.

Moreover, there remain important physical processes that are not currently in any continental-scale model but could be critical for the timing and pace of major ice-sheet retreat. For example, at present, continental-scale ice-sheet models poorly represent the meltwater-buffering capacity of firn, the transitional layer between newer snow and underlying ice. In the future, as summer air temperatures begin to drive the production of more rain and surface melt, meltwater will be absorbed by the firn layer as long as the layer contains uncompacted pore space, limiting the meltwater’s potential to flow into crevasses and hydrofracture the underlying ice (Munneke et al., 2014).

The breadth of published projections, as well as of remaining structural uncertainties, highlight the fact that future sea-level rise remains an arena of deep uncertainty (Kasperson, 2008; Heal and Millner, 2014). For the foreseeable future, there will not be a single, uniquely valid approach for estimating the probability of different levels of future change. End-users should therefore consider applying robust decision frameworks and/or adaptive management frameworks appropriate for deeply uncertain contexts. Where feasible, they could employ decision criteria that seek to minimize regret or optimize weighted mixtures of expected utilities across multiple possible distributions (e.g., Heal and Millner, 2014), rather than relying on a single distribution.

They could also try to structure decisions in a staged fashion, such that the decisions being made today depend primarily upon mid-century projections while leaving open a variety of options for later in the century (e.g., Ranger et al., 2013). The value of this approach stems from the robustness of mid-century sea-level rise projections relative to those for later in the century. Through 2050, the K14 and DP16 projections overlap substantially, as do the projections within both sets for different RCPs (Table 1).

One simple multiple-probability-distribution approach involves giving special consideration to physically plausible but low-likelihood projections in the high-end tail of projected probability distributions (e.g., Buchanan et al., 2016). K14 suggested that the 99.9th percentile of their projection for RCP 8.5, which yielded $\mathord{\sim}2.5$ m of GMSL rise by 2100, represented a physically plausible ‘worst case.’ The highest values from the DP16 projections are only modestly higher, although high values occur with greater frequency: under RCP 8.5, the DP16 ensemble revises the frequency of a $>2.5$ m GMSL rise upward from $0.1\%$ to $3\%$. However, even higher frequencies for $>2.5$ m outcomes are conceivable; the DP16 ensemble may not cover the full space of plausible outcomes. Notably, rates of ice-cliff collapse faster than the 5 km/yr maximum of DP16 have been observed in parts of Greenland, and faster rates of ice-cliff collapse would yield faster rates of ice-sheet retreat.

Moving forward, the development of probabilistic ice-sheet models that incorporate ice-sheet instabilities in a manner consistent with more detailed ice-sheet models is a key challenge for sea-level risk assessment (e.g., Wong et al., 2017; Nauels et al., 2017). Such probabilistic models will enable estimation of sea-level rise probabilities that reflect emissions sensitivity and the potential for rapid increases in discharge rate more accurately than current approaches. Probabilistic projections are a valuable input into the design of projects and policies intended to manage coastal flood risk (e.g., Buchanan et al., 2016; Lempert et al., 2012; Wong et al., 2017; Oppenheimer and Alley, 2016), as well as assessments of the value of climate change mitigation (e.g., Hinkel et al., 2014; Houser et al., 2015; Diaz, 2016). They will also enable value-of-information analyses, which can inform the design of observation systems intended to reduce the key physical uncertainties underlying future sea-level projections (Cooke et al., 2014).

Probabilistic assessment also requires more research on potential bounds for factors that influence the AIS. For example, it is unclear whether even a full ensemble of GCMs would fully constrain the range of plausible distributions of near surface ocean temperature, sea ice, and storm tracks near the AIS. More research is also needed on interactions and feedbacks across the AIS and between the AIS and the rest of the world, including through poorly understood mechanisms like ocean circulation that could over long time scales influence both ice-sheet retreat and other drivers of coastal flood risk (such as tropical cyclones) around the globe.

Acknowledgements.

REK was supported in part by a grant from Rhodium Group (for whom he has previously worked as a consultant), as part of the Climate Impact Lab consortium, and in part by a grant from the National Science Foundation (ICER-1663807). RMD and DP were supported by grants from the National Science Foundation (OCE-1202632 PlioMAX, AGS 1203910/1203792, and ICER-1664013/1663693). This paper is a contribution to PALSEA2 (Palaeo-Constraints on Sea-Level Rise), which is a working group of Past Global Changes/IMAGES (International Marine Past Global Change Study) and an International Focus Group of the International Union for Quaternary Research. We thank Climate Ready Boston for inspiring this paper. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Supporting Information) for producing and making available their model output. For CMIP, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. Code for generating sea-level projections is available in the ProjectSL (https://github.com/bobkopp/ProjectSL) and LocalizeSL (https://github.com/bobkopp/LocalizeSL) repositories on Github.

Contents

Text S1 2. 2.

Data Set S1 3. 3.

Data Set S2 4. 4.

Data Set S3 5. 5.

Data Set S4 6. 6.

Data Set S5 7. 7.

Data Set S6

Text S1: Population Exposure Assessment Detailed Methods.

To assess topography as required for this analysis, we employ the NASA SRTM digital elevation model, which is based on a radar satellite mission in 2000. This Data Set has nearly global coverage (including latitudes 60N–54S, covering land inhabited by more than 99.9 percent of global population), and is available at a 1 arcsec (SRTM 3.0) horizontal resolution (NASA JPL, 2013). SRTM, as distributed, is referenced to the EGM96 geoid (henceforth denoted by $SRTM_{EGM96}$) at a 1 m vertical resolution, with RMSE less than 10 m (LaLonde et al., 2010; Rodriguez et al., 2005). Derived from radar measurements, $SRTM_{EGM96}$ is an unclassified (surface) elevation model, and significant positive bias is expected in regions of dense urban development and vegetation (Shortridge and Messina, 2011).

To convert elevations to a tidal vertical datum, we use the global mean sea level (MSL) model MSS_CNES_CLS_11 (AVISO, 2011), based on TOPEX/Poseidon satellite measurements, and referenced to the GLAS ellipsoid ($MSL_{GLAS}$). We also employ mean higher-high water ($MHHW_{MSL}$) deviations from MSL provided by Mark Merrifield, University of Hawaii, developed using the model TPX08 (Egbert and Erofeeva, 2002). We convert MSL and SRTM to a common ellipsoidal datum (WGS84) using NOAA’s VDatum tool (Parker et al., 2003) version 3.7, and subtract the MHHW grids from $MSL_{WGS84}$ to find $MHHW_{WGS84}$. We then subtract this tidal grid from $SRTM_{WGS84}$ to produce our final elevation map, $SRTM_{MHHW}$.

We resample a given relative sea-level rise projection grid $X$ using bilinear interpolation to match the horizontal resolution of SRTM, and threshold elevation against these water heights to produce the inundation surface $THRESH_{x}$. Hydrological connectivity to the ocean is typically enforced in such analyses, but we find that high-frequency errors present in SRTM create significant speckle noise in the flood maps, causing some truly connected areas to appear isolated. We instead perform connected components analysis at the 20m water height above MHHW, producing surface $CONTIG_{20m}$, and perform the intersection $HYBRID_{x}=THRESH_{x}\cap CONTIG_{20m}$. Isolated, low-lying land separated from ocean by at least a 20m high ridge is therefore removed from the final surface, $HYBRID_{x}$, while low-lying ocean-side land is included, reducing sensitivity to speckle noise. We compute inundation surfaces given different projection models (K14 and DP16), emissions scenarios (RCP 2.6 and RCP 8.5), percentiles of RSL projections (5th, 50th, and 95th), and years (2100 and 2300).

To assess population exposure, we employ the LandScan 2010 High Resolution global Population Data Set, which provides total estimated populations living in 1 km square cells (Bright et al., 2011). We refine this data using the SRTM Water Body Data Set (SWBD), which defines land cells at 1-arcsecond resolution. We resample Landscan at 1-arcsecond resolutions to align with the SRTM grids, assuming zero population in water cells, while proportionally increasing the population density in land cells to ensure total population in each 1 km square remains unchanged. We integrate exposure under each inundation surface and tabulate according to national boundaries defined by the Global Administrative Areas (GADM) 2.0 Data Set (Hijmans et al., 2012).

Linked to the positive bias in SRTM elevation data, a notable negative bias has been detected in coastal population flood exposure analysis based on SRTM, at least within the United States, where higher quality, LIDAR-based elevation models are freely available for comparison (Kulp and Strauss, 2016). LIDAR-based US national exposure estimates are $\mathord{\sim}45\%$ higher than SRTM-based estimates at 1 m above MHHW; $\mathord{\sim}150\%$ higher at 2 m and 3 m; and monotonically decline to $\mathord{\sim}33\%$ higher at 10 m (Kulp and Strauss, 2016). The exposure values presented here can thus be viewed as likely to significantly underestimate the true hazard. We nevertheless include them to provide some illustration of the ramifications of different projections. SRTM data are widely used in analysis of global exposure to sea level rise and coastal flooding (e.g., Hinkel et al., 2014), and the major available alternative, the Global Land One-kilometer Base Elevation (GLOBE) gridded elevation model, is far coarser in resolution and based on underlying data of varying and unknown quality by region (Kulp and Strauss, 2016).

The LandScan 2010 High Resolution global Population Data Set is copyrighted by UT-Battelle, LLC, operator of Oak Ridge National Laboratory under Contract No. DE-AC05-00OR22725 with the United States Department of Energy. The United States Government has certain rights in this Data Set. Neither UT-Battelle, LLC nor the United States Department of Energy, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of the data set.

Data Set S1

Data Set S1 provides time series of WAIS, EAIS and total AIS contributions to GMSL from 2000 to 2300 from DP16.

Data Set S2

Data Set S2 provides estimated non-climatic background rates and IDs, latitude, and longitude of tide-gauge and grid point locations.

Data Set S3

Data Set S3 provides K14 GMSL and RSL projections at decadal intervals from 2010 to 2300.

Data Set S4

Data Set S4 provides DP16 GMSL and RSL projections at decadal intervals from 2010 to 2300.

Data Set S5

Data Set S5 provides DP16 GMSL and RSL projections at decadal intervals from 2010 to 2300 without bias correction.

Data Set S6

Data Set S6 provides current population by country occupying land exposed to inundation under K14 and DP16 projected 2100 and 2300 RSL change.