ISMIP6 future projections for the Antarctic ice sheet with the model SICOPOLIS

ISMIP6 future projections for the Antarctic ice sheet with the model SICOPOLIS Ralf GREVE1,2, Reinhard CALOV3, Takashi OBASE,4 Fuyuki SAITO5, Shun TSUTAKI6, Ayako ABE-OUCHI4 1Institute of Low Temperature Science, Hokkaido University, Sapporo, Japan 2Arctic Research Center, Hokkaido University, Sapporo, Japan 3Potsdam Institute for Climate Impact Research, Potsdam, Germany 4Atmosphere and Ocean Research Institute, University of Tokyo, Kashiwa, Japan 5Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan 6National Institute of Polar Research, Tokyo, Japan Contact: R. Greve (grev[email protected]) September 17, 2020 Abstract Technical Report, Zenodo, doi: 10.5281/zenodo.3971232 (2020) The Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6) brings together a consortium of international ice-sheet and climate modellers to simulate the contribution from the Greenland and Antarctic ice sheets to future sea-level rise. In this document (supplementary to Seroussi et al. 2020, doi: 10.5194/tc-14-3033-2020), we describe the ISMIP6 Antarctica Tier-1 and Tier-2 experiments carried out with the ice-sheet model SICOPOLIS. First, we conduct a paleoclimatic spin-up over the last glacial-interglacial cycle until the year 1990. In this spin-up, we employ a nudging technique for the topography and aim at optimizing the match between simulated and observed surface velocities by adjusting the amount of basal sliding for individual drainage systems. Then, we carry out a historical run to bridge the gap between 1990 and 2015. The future climate projections run from the beginning of 2015 until the end of 2100. Results reveal a non-uniform response of the Antarctic ice sheet: for both employed future climate scenarios (RCP8.5/SSP5-8.5, RCP2.6/SSP1-2.6), mass losses and gains occur, depending on the specific forcing (provided by CMIP5 and CMIP6 global climate models). This is due to the counteracting effects of increasing ocean temperature (leading to a loss) and increasing precipitation (leading to a gain). For RCP8.5/SSP5-8.5, the ensemble mean is a mass loss of 18.5 mm SLE (sea-level equivalent) by 2100, and for RCP2.6/SSP1-2.6 it is 8.4 mm SLE. However, the uncertainty range is quite large, including the possibility of a mass loss of more than 100 mm SLE under RCP8.5/SSP5-8.5. 1 1 Introduction The Coupled Model Intercomparison Project Phase 6 (CMIP6) is a major international climate modelling initiative (Eyring et al. 2016). As a part of this project, the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6) was devised to assess the likely sealevel-rise contribution from the Greenland and Antarctic ice sheets until the end of this century (Nowicki et al. 2016, 2020). Seroussi et al. (2020) describe the set-up and results of the ISMIP6 projections for the Antarctic ice sheet. The ensemble of simulations is based on 21 sets of ice-flow simulations submitted by 13 international institutions. The design of the experiments is sketched in Fig. 1. An initial state of the ice sheet is produced by either assimilation or spinup techniques or a combination of both (Goelzer et al. 2018, Seroussi et al. 2019). The initialization date varies between the different models (here 1990 CE). A single historical run with a forcing that can be chosen freely bridges the gap between the initialization and the start date of the projections in January 2015. All projections run from this date until December 2100. The atmospheric forcing consists of anomalies for the surface mass balance (SMB) and surface temperature (ST), derived from selected CMIP5 and CMIP6 global climate models (Barthel et al. 2020). The oceanic forcing is a parameterization for ice-shelf basal melting that depends on the thermal forcing at the ice–ocean interface, which, in turn, is derived by extrapolating the oceanic fields simulated by the climate models into the ice-shelf cavities (Jourdain et al. 2020). A parameterization for ice-shelf fracture triggered by surface melting (Trusel et al. 2015) is also tested. Figure 1: ISMIP6 experimental design. A model-specific initialization is followed by a historical simulation from 1990 until 2015. The several projections run from 2015 until the end of 2100. (Credit: Martin R¨uckamp, AWI Bremerhaven, Germany.) Here, we describe the ISMIP6 future projections for the Antarctic ice sheet carried out with the ice-sheet model SICOPOLIS (SImulation COde for POLythermal Ice Sheets). The emphasis is on the model-specific features that are not covered in detail in the community papers (Seroussi et al. 2020, Payne et al. 2020). For the ISMIP6 future projections for the Greenland ice sheet, see the companion document by Greve et al. (2020). 2 Ice-sheet model SICOPOLIS v5.1 The three-dimensional, dynamic/thermodynamic model SICOPOLIS was originally created in a version for the Greenland ice sheet (Greve 1995, 1997a,b). Since then, the model has been developed continuously and applied to problems of past, present and future glaciation of Greenland, Antarctica, the entire northern hemisphere, the polar ice caps of 2 the planet Mars and others, resulting in more than 120 publications in the peer-reviewed literature (www.sicopolis.net, last accessed 2020-08-12). Here, we apply SICOPOLIS v5.1 (Greve and SICOPOLIS Developer Team 2019) to the Antarctic ice sheet. The model domain covers the entire area of Antarctica and the surrounding oceans. We use the EPSG:3031 grid, based on a polar stereographic projection with the WGS 84 reference ellipsoid, standard parallel 71◦S and central meridian 0◦E. The stereographic plane is spanned by the Cartesian coordinates xand y, and the coordinate zpoints upward. It is discretized by a regular (structured) grid with ∆x= 8, 16 or 32 km resolution. In the vertical, we use terrain-following coordinates (sigma transformation) with 81 layers in the ice domain and 41 layers in the thermal lithosphere layer below. For the ice rheology, we use the regularized Glen flow law in the formulation by Greve and Blatter (2009, Sect. 4.3.2). Ice dynamics is modelled by the shallow-ice approximation (SIA) for slow-flowing grounded ice, hybrid shallow-ice–shelfy-stream dynamics for fastflowing grounded ice and the shallow-shelf approximation (SSA) for floating ice. The hybrid dynamics is a modified form of Bernales et al.’s (2017) HS-1 scheme (hybrid scheme #1), for which the horizontal-velocity field vis computed as follows: 1. Compute the SIA solution with basal sliding (see below) →velocity vSIA. 2. Slip ratio r=vb,SIA vs,SIA (1) (where subscripts ‘b’ and ‘s’ refer to the base and surface, respectively). 3. Fast-flowing ice (streaming point) if r > rthr (threshold slip ratio chosen as rthr = 0.5), then continue. Otherwise, use the SIA solution, v=vSIA. 4. Compute the solution for the shelfy-stream approximation (SStA) with the same basal-sliding law used for the SIA solution →velocity vSStA. 5. Auxiliary factor ˜r=r−rthr 1−rthr (∈[0,1] for streaming points) .(2) 6. Weight factor ˇr=f5(˜r),(3) where the quintic function f5is defined by f5(x) = x3[10 −x(15 −6x)] (4) (Fig. 2). Thus, for streaming points, ˇr∈[0,1]. 7. Weighting v= (1 −ˇr)vSIA + ˇr vSStA .(5) Due to the staggered grid used by SICOPOLIS (Arakawa C grid, Arakawa and Lamb 1977), this must be done separately for the xand y-components. 3 Figure 2: Quintic function f5(x) according to Eq. (4). Compared to the original HS-1 scheme by Bernales et al. (2017), the new feature is using the quintic function f5in step 6 (SICOPOLIS parameter SSTA SIA WEIGH FCT= 3). Since both the first and second derivatives of this function at the end points x= 0 and x= 1 are equal to zero, this guarantees a very smooth transition between slowand fastflowing grounded ice (ˇr= 0) and between fast-flowing grounded and floating ice (ˇr= 1). In contrast to the SIA, the SStA and SSA require stress or velocity boundary conditions at the lateral margins of the domain. For the SSA, the boundary condition at the calving front states that the stress is equal to the hydrostatic pressure from the ocean water (e.g., Greve and Blatter 2009, Eqs. (6.62)–(6.64)). For the SStA, we distinguish between a marine-terminating and a land-terminating grounded ice front. For a marine-terminating front (in contact with the ocean), the boundary condition is essentially the same as for SSA/calving front; however, the floating condition is not fulfilled (a larger percentage of the ice column is above the water line). For a land-terminating front, we do not prescribe a stress boundary condition, so that the system merely sees the adjacent ice-free grid points with zero velocity (SICOPOLIS parameter BC SSA LTGF = 1). [The alternative, also implemented via BC SSA LTGF = 2, would be to impose a stress-free condition acting on the vertical face of the land-terminating front. However, this sometimes leads to unrealistically high velocities, so that we refrain from it.] At the grounding line (transition between grounded and floating ice), we compute the surface (h) gradients at staggered-grid points by one-sided differences into either grounded or floating ice, depending on whether the staggered-grid points are grounded or floating (SICOPOLIS parameter GL SURF GRAD = 2). For instance, for the x-direction with grid index i, if the point iis grounded and the point i+ 1 is floating, then the gradient at the mid-point i+1 2is discretized as ∂h ∂xi+1/2 ∼         hi+1/2−hi−1/2 ∆x,if point i+1 2grounded, hi+3/2−hi+1/2 ∆x,if point i+1 2floating. (6) This is illustrated in Fig. 3. The decision whether the point i+1 2is grounded or floating is done by linear interpolation of the ice topography between iand i+ 1. For the y-direction (grid index j), the scheme is analogous. 4 Figure 3: One-sided gradients of the discretized ice surface at the grounding line. Depending on whether the staggered-grid point i+1 2is diagnosed as grounded or floating, either the one-sided gradient “GR” (pointing into the grounded ice) or “FL” (pointing into the floating ice) is used for (∂h/∂x)|i+1/2. Ice thermodynamics is modelled by the one-layer melting-CTS enthalpy scheme (CTS: cold-temperate transition surface; Blatter and Greve 2015, Greve and Blatter 2016). The temperature-dependent rate factor for cold ice is by Cuffey and Paterson (2010, Sect. 3.4.6), and the water-content-dependent rate factor for temperate ice is by Lliboutry and Duval (1985). The ice surface is assumed to be traction-free. Basal sliding under grounded ice, vb, is described by a Weertman-Budd-type sliding law accounting for sub-melt sliding (Hindmarsh and Le Meur 2001) and the subglacial water-layer thickness (Kleiner and Humbert 2014, Calov et al. 2018): vb=−Cb τp b Nq b ,(7) with Cb=C0 bexp T0 b γ1 + c1−exp −Hw H0 w.(8) In Eq. (7), Cbis the sliding function, τbthe basal drag (shear stress), Nbthe basal normal stress (ice stress minus water pressure, counted positive for compression), and pand qare the sliding exponents. In Eq. (8), C0 bis the sliding coefficient, T0 bthe basal temperature relative to pressure melting (in ◦C, always ≤0◦C), γthe sub-melt-sliding parameter, cthe coefficient for water-layer-enhanced basal sliding, Hwthe subglacial water-layer thickness and H0 wthe threshold water-layer thickness. Optionally, the simulated ice sheet can be divided into n= 1 . . . N regions, for which individual basal sliding coefficients (C0 b)nare set. We do this for the N= 18 IMBIE-2016 basins (Rignot and Mouginot 2016, The IMBIE Team 2018) (see below, Sect. 3.2.3). The water-layer thickness Hwis computed by a steady-state routing scheme for subglacial water that receives its input from the basal melting rate under grounded ice (Le Brocq et al. 2006, 2009). 5 The sliding law (7) features a singularity for Nb→0 (when grounded ice becomes nearly floating close to the grounding line). To avoid this, we impose a limiter such that Nb≥0.35Pb, where Pb=ρgH is the hydrostatic pressure of the ice column (ice thickness H). However, the limiter is only applied for the SIA. For the SStA, the sliding law appears in its inverse form (solved for τb), so that the singularity does not appear, and the limiter is thus not required (SICOPOLIS parameter SLIDE LAW = 3). Quantity Value Density of ice, ρ910 kg m−3 Density of sea water, ρsw 1028 kg m−3 Gravitational acceleration, g9.81 m s−2 Length of year, 1 a 31 556 926 s Power-law exponent, n3 Residual stress, σ010 kPa Flow enhancement factor, Egrounded ice: 5, floating ice: 1 Melting temperature at low pressure, T0273.16 K Clausius-Clapeyron gradient, β8.7×10−4K m−1 Universal gas constant, R8.314 J mol−1K−1 Heat conductivity of ice, κ9.828 e−0.0057 T[K] W m−1K−1 Specific heat of ice, c(146.3+7.253 T[K]) J kg−1K−1 Latent heat of ice, L3.35 ×105J kg−1 Sliding exponents, (p, q) (3,2) Sub-melt-sliding parameter, γ1◦C Coefficient for water-layer-enhanced basal sliding, c9 Threshold water-layer thickness, H0 w5 mm Density ×specific heat of the lithosphere, ρlcl2000 kJ m−3K−1 Heat conductivity of the lithosphere, κl3 W m−1K−1 Thickness of the thermal upper boundary layer of the lithosphere, Hlt 2 km Flexural stiffness of the lithosphere, Kl1025 N m Asthenosphere density, ρa3300 kg m−3 Time lag for the relaxing asthenosphere, τa3000 a Table 1: Physical parameters used for the simulations of this study. The bed topography is Bedmap2 (Fretwell et al. 2013). Glacial isostatic adjustment (GIA) is included using an elastic-lithosphere–relaxing-asthenosphere (ELRA) model (parameters by Sato and Greve 2012). The geothermal heat flux is by Martos et al. (2017), applied at the base of the thermal lithosphere layer (rather than directly at the ice base) to account for the thermal inertia of the lithosphere (Ritz 1987). The physical parameters are listed in Table 1. 6 3 Initialization via paleoclimatic spin-up A crucial prerequisite for future projections is a reasonable initial state of the ice sheet regarding ice geometry, surface velocities and englacial temperature (Goelzer et al. 2018, Seroussi et al. 2019). Here, we produce this initial state by carrying out a paleoclimatic spin-up over a full glacial cycle (140 ka) until the year 1990 CE (which corresponds to the time t= 0). 3.1 Climate forcing The climatic forcing for the spin-up consists of time-dependent distributions of the surface temperature and precipitation. Like in the study by Sato and Greve (2012), the mean annual and mean summer surface temperatures (Tma and Tms, respectively) are decomposed into present-day spatial distributions plus a purely time-dependent anomaly ∆T(t), Tma(h, φ, t) = Tpresent ma (h, φ)+∆T(t), Tms(h, φ, t) = Tpresent ms (h, φ)+∆T(t),(9) where tis time and φgeographical latitude. The present-day parameterizations are by Fortuin and Oerlemans (1990), Tpresent ma =     7.405 −0.014285 h−0.180 φ , for h > 1500 m , 36.689 −0.005102 h−0.725 φ , for 200 m < h ≤1500 m , 49.642 −0.943 φ , for h≤200 m , (10) Tpresent ms = 16.81 −0.00692 h−0.27937 φ , (11) where temperatures are in ◦C, surface elevations in m AMSL (above mean sea level) and latitudes in ◦S (counted positive). The time-dependent anomaly ∆T(t) results from the Vostok δD record converted to temperature with the relation by Petit et al. (1999). We equate the present-day precipitation distribution Ppresent(λ, φ) (where λis geographical longitude) to the surface accumulation data by Arthern et al. (2006), Le Brocq et al. (2010). For any other time t, the precipitation P(λ, φ, t) is computed by Ppresent and a temperature-dependent factor via the parameterization by Huybrechts et al. (2007). Conversion from precipitation to snowfall rate (solid precipitation) is done on a monthly-mean basis using the linear function by Marsiat (1994). Surface melting is parameterized by Reeh’s (1991) positive degree day (PDD) method, supplemented by the semi-analytical solution for the PDD integral by Calov and Greve (2005). The PDD factors are βice = 8 mm WE d−1◦C−1and βsnow = 3 mm WE d−1◦C−1 for ice and snow melt, respectively (where WE means water equivalent). Furthermore, the standard deviation of short-term, statistical air temperature fluctuations is σ= 5◦C, the saturation factor for the formation of superimposed ice is Pmax = 0.6, and we apply an empirical firn-warming correction due to refreezing melt water with a coefficient µ= 9.7155 d ◦C (mm WE)−1(Reeh 1991). For the part of the spin-up sequence prior to 2 ka ago with 32 and 16 km resolution (see Sect. 3.2 below), we use the parameterization for ice-shelf basal melting by Greve and Galton-Fenzi (2017) (SICOPOLIS parameter FLOATING ICE BASAL MELTING = 5). 7 It takes the melt rate as a function of both the depth of ice below mean sea level and ocean temperatures outside the ice-shelf fronts at 500 m depth, tuned differently for eight Antarctic sectors. However, for the most recent 2 ka (8 km resolution), we switch to the “ISMIP6 standard approach” (SICOPOLIS parameter FLOATING ICE BASAL MELTING = 6). This is a non-local quadratic melt-rate parameterization for the 18 IMBIE-2016 sectors (Rignot and Mouginot 2016, The IMBIE Team 2018), where the two sectors feeding the Ross ice shelf and the two sectors feeding the Filchner–Ronne ice shelf are combined, leaving 16 distinct sectors. The thermal forcing at the ice–ocean interface is derived by extrapolating the oceanic fields into the ice-shelf cavities. This melt-rate parameterization and its calibration by observations is described in detail by Jourdain et al. (2020). In both cases, no time dependence is considered; the parameterizations are kept fixed at their forms valid for present-day conditions during the entire model time of the spin-up. Over the abyssal ocean, defined by a threshold sea-bed elevation of −2500 m AMSL, we prescribe a large melt rate of 10 m WE a−1to avoid that ice shelves spread out over these areas. We describe ice-shelf calving by a 50-m thickness threshold. Any floating ice at the calving front that is thinner than this threshold is assumed to calve off instantaneously (Sato and Greve 2012). The sea level surrounding the ice sheet is kept constant at its present-day level. 3.2 Spin-up sequence 3.2.1 32 km resolution, t= –140. . . –9 ka (1) Simulation ant32 b2 spinup22 init001a Short simulation over 0.01 a, starting from the observed present-day topography, no basal sliding, glacial temperature anomaly ∆T=−6.864◦C (Vostok value for t=−135 ka). The initial temperature field is computed by the Robin (1955) solution for grounded ice columns with positive SMB and a linear profile otherwise (SICOPOLIS parameter TEMP INIT = 4). Time steps: ∆t= 0.01 a (for dynamics and topography), ∆ttemp = 0.01 a (for thermodynamics), ∆twss = 0.01 a (for steady-state displacement of the elastic lithosphere). The purpose of this run is to produce a slightly smoothed present-day topography (surface htarget(x, y), bed btarget(x, y)) of the Antarctic ice sheet that serves as a target for the nudging technique of run (2). (2) Simulation ant32 b2 spinup22 fixtopo Time t=−140 . . . −9 ka, starting from the output of run (1). Time steps: ∆t= 1 a, ∆ttemp = 2 a, ∆twss = 100 a. Basal sliding with a constant sliding parameter C0 b= 2.5 m a−1Pa−1after −135 ka. During the first 5 ka (prior to −135 ka), basal sliding is ramped up as follows (R¨uckamp et al. 2019): Introducing a non-dimensionalized time valid for the first 5 ka: ˜ t=t−(−140 ka) 5 ka ( thus ˜ t∈[0,1] ) ,(12) gradual increase of the sliding coefficient by the quintic function (4): C0 b→f5(˜ t)C0 b.(13) 8 The computed topography (surface h(x, y), bed b(x, y)) is continuously nudged towards the output of run (1) by first solving the normal evolution equations for the bed and the ice thickness (e.g., Greve and Blatter 2009, Eqs. (5.55) and (8.5)) and then applying the relaxation equations ∂h ∂t =−h−htarget τrelax ,(14) ∂b ∂t =−b−btarget τrelax ,(15) with a relaxation time of τrelax = 100 a (Rezvanbehbahani et al. 2019, R¨uckamp et al. 2019). This nudging is equivalent to applying an SMB correction (Aschwanden et al. 2013, 2016, Calov et al. 2018), which is diagnosed by the model. 3.2.2 16 km resolution, t= –9. . . –2 ka (3) Simulation ant16 b2 spinup22 init001a Similar to run (1): short simulation over 0.01 a, starting from the observed presentday topography, no basal sliding, temperature anomaly ∆T=−6.864◦C. Time steps: ∆t= 0.01 a, ∆ttemp = 0.01 a, ∆twss = 0.01 a. Purpose: to produce a slightly smoothed present-day topography that serves as a target for the nudging technique of run (4). (4) Simulation ant16 b2 spinup22 fixtopo Time t=−9. . . −2 ka, starting from the resolution-doubled output of run (2) at t=−9 ka. Time steps: ∆t= 0.1 a, ∆ttemp = 1 a, ∆twss = 10 a. Basal sliding with a constant sliding parameter C0 b= 2.5 m a−1Pa−1. The computed topography is continuously nudged towards the output of run (3) as described above for run (2) [Eqs. (14), (15) and accompanying text]. 3.2.3 8 km resolution, t= –2. . . 0 ka (5) Simulation ant08 b2 spinup25 init10a Similar to runs (1) and (3): short simulation over 10 a, starting from the observed present-day topography, no basal sliding, temperature anomaly ∆T=−6.864◦C. Time steps: ∆t= 0.02 a, ∆ttemp = 0.02 a, ∆twss = 1 a. Purpose: to produce a slightly smoothed present-day topography that serves as a target for the nudging technique of runs (6) and (7). (6) Simulation ant08 b2 spinup25 fixtopo Time t=−2. . . −0 ka, starting from the resolution-doubled output of run (4) at t=−2 ka. Time steps: ∆t= 0.05 a, ∆ttemp = 0.5 a, ∆twss = 10 a. Basal sliding with a constant sliding parameter C0 b= 2.5 m a−1Pa−1. The computed topography is continuously nudged towards the output of run (5) as described above for run (2) [Eqs. (14), (15) and accompanying text]. 9 tions are tested, as well as a calibration in which only observed basal-melt values near the grounding line of the Pine Island ice shelf are used (“PIGL-medium”) (Jourdain et al. 2020). In one experiment, ice-shelf fracture triggered by surface melting is accounted for. This is implemented by a time-dependent ice-shelf-collapse mask, computed on the assumption that collapse occurs following a 10-year period with annual surface melt above 725 mm (Trusel et al. 2015). An overview of the ISMIP6 Tier-1 and Tier-2 experiments is given in Table 2. There are 17 experiments, 14 of which are for RCP8.5/SSP5-8.5 and three for RCP2.6/SSP1-2.6. In three experiments, the impact of different calibrations of the parameterization for iceshelf basal melting is tested, and one experiment includes ice-shelf hydrofracture. [Further 14 experiments employ an “open forcing”, in which the choice of the parameterization for ice-shelf basal melting is left to the discretion of the individual modellers. We have not conducted them with SICOPOLIS, so that they are not considered here.] For more details on the forcing, we refer to Nowicki et al. (2020) and Seroussi et al. (2020). Figure 8: ISMIP6-Antarctica historical run (hist), projection control run (ctrl proj), schematic initMIP experiments (asmb, abmb) and Tier-1 and 2 future climate experiments: Simulated ice mass change, counted positively for loss and expressed as sea-level contribution. The red and blue boxes to the right show the mean ±1-sigma ranges for RCP8.5/SSP5-8.5 and RCP2.6/SSP1-2.6, respectively; the whiskers show the corresponding full ranges. The contribution to sea-level change produced by these experiments is shown in Fig. 8 (coloured lines). Over the entire period from 1990 until 2100, it is 18.5±45.4 mm (mean±1-sigma uncertainty) for the RCP8.5/SSP5-8.5 experiments (excluding Exp. #13, see below) and 8.4±15.9 mm for the RCP2.6/SSP1-2.6 experiments. Thus, for both pathways, mass losses and gains occur, depending on the GCM forcing. For RCP8.5/SSP58.5, 6 out of 14 experiments, and for RCP2.6/SSP1-2.6, 1 out of 3 experiments produce 16 slight mass gains (negative contributions to sea-level change), the most extreme case being Exp. #B6 (CNRM-CM6-1/SSP5-8.5) with a value of −31.1 mm SLE. On the other end of the range, the largest mass loss is produced by Exp. #A5 (HadGEM2-ES/RCP8.5), 143.6 mm SLE. As already discussed in Sect. 4.1, this is due to the interplay between increasing precipitation, leading to a mass gain, and increasing ice-shelf basal melt, leading to a mass loss. These counteracting drivers have different relative strengths in the different GCM results, which leads to the inconsistent response of the ice sheet. The influence of the calibration of the parameterization for ice-shelf basal melt is explored by Exps. #5, 9, 10 (NorESM1-M/RCP8.5 with “medium”, “high” and “low” calibrations, respectively). The results are shown by the green line and green-shaded region in Fig. 8. By 2100, the simulated mass loss is 40.0+11.9 −7.4mm SLE. Thus, the uncertainty due to these three calibrations is notable, but smaller than the uncertainty due to the GCM forcings. A more extreme test is Exp. #13, which is NorESM1-M/RCP8.5 with the “PIGL-medium” calibration described above. During the 86 years model time, this leads to an, on average, ∼2 times larger total ice-shelf basal melting than for Exp. #5. It has a pronounced effect on the mass loss of the ice sheet: By 2100, it is 216.7 mm SLE compared to the initial 1990 state, and 176.6 mm SLE larger than that of Exp.#5. This is the largest mass loss of the ensemble of simulations, thus highlighting the great sensitivity of the Antarctic ice sheet to oceanic forcing. Exps. #8 and 12 (CCSM4/RCP8.5) investigate the influence of ice-shelf hydrofracture as described above (included in Exp. #12). Exp. #8 is actually one of the cases that produce a mass gain of the ice sheet. Adding ice-shelf hydrofracture via the time-dependent collapse mask in Exp. #12 reverts this behaviour to a mass loss, the difference between the two experiments amounting to 46.1 mm SLE. Thus, the process can act as a significant amplifier of the mass loss of the Antarctic ice sheet. For a more detailed analysis of the future climate experiments, including results from other ice-sheet models, regional patterns etc., we refer to the ISMIP6 community papers by Seroussi et al. (2020) and Payne et al. (2020). 5 Summary We described the ISMIP6 future projections for the Antarctic ice sheet with the model SICOPOLIS, with a focus on the model-specific methods and set-up. Our paleoclimatic spin-up over 140 ka with successively refining resolution employed a topography nudging technique and an optimization of the basal sliding coefficients for 18 different basins to ensure a good match between the observed and the simulated initial (1990 CE) ice sheet. A historical run, forced by NorESM1-M/RCP8.5, brought the modelled ice sheet from this initial state to the projection start date January 2015. Our ensemble of 17 ISMIP6 Tier-1 and Tier-2 future climate experiments, forced by ten different CMIP5 and CMIP6 GCMs for two different pathways (RCP8.5/SSP5-8.5, RCP2.6/SSP1-2.6), forked off from there and was run until the end of the 21st century (December 2100). The response of the Antarctic ice sheet is mainly determined by two counteracting processes. Increasing ocean temperatures entail increasing ice-shelf basal melting, leading to a mass loss (positive contribution to sea-level rise). By contrast, increasing precipitation over the ice sheet leads to a mass gain (negative contribution to sea-level rise). The 17 ensemble means for both pathways are small, positive sea-level contributions. However, uncertainties in the calibration of the parameterization for ice-shelf basal melting and hydrofracturing of ice shelves can potentially lead to a significantly larger mass loss of the ice sheet. Additional ISMIP6 Tier-3 experiments have already been defined (ISMIP6 Steering Committee, personal communication 2019). They include experiments with atmosphereonly and ocean-only forcing, more sensitivity tests for the oceanic forcing and further experiments including ice-shelf hydrofracture. Further, we are planning to carry out simulations extending beyond 2100 to assess the longer-term response of the Antarctic ice sheet to climate-change conditions. Acknowledgements We thank the Climate and Cryosphere (CliC) effort, which provided support for ISMIP6 through sponsoring of workshops, hosting the ISMIP6 website and wiki, and promoting ISMIP6. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6. We thank the climate modelling groups for producing their model output and making it available; the Earth System Grid Federation (ESGF) for archiving the CMIP data and providing access to it; the University at Buffalo for ISMIP6 data distribution and upload; and the multiple funding agencies who support CMIP5, CMIP6, and ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and ISMIP6 dataset preparation group for their continuous engagement in defining ISMIP6. 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