Supporting Online Material For s4

Geophysical Research Letters

Supporting Information for

Coupled simulations of Greenland ice sheet and climate change up to AD 2300

Miren Vizcaino (1), Uwe Mikolajewicz (2), Florian Ziemen (2,3),

Christian B. Rodehacke (2,4), Ralf Greve (5), and Michiel R. van den Broeke (6)

(1) Department of Geoscience and Remote Sensing, Delft University of Technology, The Netherlands;

(2) Max Planck Institute for Meteorology, Hamburg, Germany;

(3) Geophysical Institute, University of Alaska, Fairbanks, United States;

(4) Now at Department of Arctic and Climate Research, Danish Meteorological Institute, Copenhagen, Denmark;

(5) Institute of Low Temperature Science, Hokkaido University, Sapporo, Japan;

(6) Institute for Marine and Atmospheric Research, Utrecht University, The Netherlands

Contents of this file

Text S1 to S2

Figures S1 to S7

Table S1

Introduction

This supporting information includes the description of the method used to calculate the Surface Mass Balance, as well as seven figures and a table that support the analysis presented in the main text. References are included for the surface mass balance calculation and data used for the figures.


Text S1. Description of the Surface Mass Balance calculation

The GrIS surface mass balance (SMB, the difference between accumulation and surface ablation) is calculated at the resolution of the ice sheet model (ISM) in a subroutine separated from the Atmopshere-Ocean General Circulation Model (AOGCM), using an energy balance scheme with parameterizations for albedo evolution and refreezing. Atmospheric forcing is provided every six-hours and bilinearly interpolated to the ISM grid. It consists of incoming shortwave and longwave radiation, near-surface and dew-point temperature, 10m wind speed, surface pressure and precipitation. Near surface and dew point temperatures are downscaled to the ISM grid using a lapse rate of -6.5 K km-1. A fixed lapse rate of -29 W m-2 km-1 is used for incoming longwave radiation [Marty et al., 2002]. Shortwave radiation and wind speed are not corrected for elevation differences. Precipitation rates are halved for each km above a reference elevation of 2000 m, and uncorrected below this elevation, hence assuming that water vapor concentrations vary little in the lowest tropospheric layer, and decay exponentially higher in the atmosphere (elevation-desertification effect). Downscaled six-hourly precipitation rates are converted into snowfall if the downscaled near-surface temperature is below freezing; otherwise rainfall is assumed to occur.

The SMB is calculated as the difference between accumulation and runoff. The former is calculated as snowfall minus sublimation. Drifting snow is not considered. Runoff is calculated as the difference between surface melt and refreezing. The energy available for surface melt M is calculated from the surface energy budget (positive signs indicate ice/snow surface gain):

M = SWd (1-α) + LWd – σTs4- E + H + Hcond

which balances incoming shortwave and longwave radiation SWd and LWd with outgoing radiation, latent and sensible turbulent fluxes (E, H) and subsurface heat conduction to/from lower snow layers Hcond. σ is the Stefan-Boltzmann constant and Ts is surface temperature. The turbulent fluxes E and H are calculated from bulk formulae, as in Vizcaíno et al. [2010]

The snow model is simple, but improves upon previous GCM versions. Snow/ice albedo α is parameterized in the AOGCM as a function of surface temperature [Roeckner et al., 2003], varying linearly from 0.55 when the surface is at the melting point to 0.825 for Ts < 268.15 K. For consistency, the SMB subroutine and the AOGCM use the same albedo parameterization. A more realistic simulation of the SMB-albedo feedback would require explicit simulation of albedo as a function of snow density or grain size [Box et al., 2012; van Angelen et al., 2012], as implemented in only a few AOGCMs [Cullather et al., 2014; Vizcaíno et al., 2013]. Bare ice surface and below-surface penetration of shortwave radiation are not considered. The snowpack has a constant thickness of 20 m and four internal layers. Density and thermal conductivity are prescribed as functions of depth and held constant. Refreezing is calculated based on the temperature of each snow layer, without considering pore space. Capillary water retention is not modeled. Further details of the snow model are given in Vizcaíno et al. [2010].

Text S2. References for Supplementary Information including figures

Bamber, J. L., R. L. Layberry, and S. Gogineni (2001), A new ice thickness and bed data set for the Greenland ice sheet 1. Measurement, data reduction, and errors, J Geophys Res-Atmos, 106(D24), 33773-33780, doi: 10.1029/2001jd900054.

Box, J. E., X. Fettweis, J. C. Stroeve, M. Tedesco, D. K. Hall, and K. Steffen (2012), Greenland ice sheet albedo feedback: thermodynamics and atmospheric drivers, Cryosphere, 6(4), 821-839, doi: 10.5194/Tc-6-821-2012.

Cullather, R. I., S. M. J. Nowicki, B. Zhao, and M. J. Suarez (2014), Evaluation of the Surface Representation of the Greenland Ice Sheet in a General Circulation Model, Journal of Climate, doi:10.1175/JCLI-D-13-00635.1.

Marty, C., R. Philipona, C. Frohlich, and A. Ohmura (2002), Altitude dependence of surface radiation fluxes and cloud forcing in the alps: results from the alpine surface radiation budget network, Theor Appl Climatol, 72(3-4), 137-155, doi: 10.1007/S007040200019.

Roeckner, E., et al. (2003), The atmospheric general circulation model ECHAM5. PART I: Model description Rep.

van Angelen, J. H., J. T. M. Lenaerts, S. Lhermitte, X. Fettweis, P. K. Munneke, M. R. van den Broeke, E. van Meijgaard, and C. J. P. P. Smeets (2012), Sensitivity of Greenland Ice Sheet surface mass balance to surface albedo parameterization: a study with a regional climate model, Cryosphere, 6(5), 1175-1186, doi: 10.5194/Tc-6-1175-2012.

Vizcaíno, M., W. Lipscomb, W. Sacks, J. van Angelen, B. Wouters, and M. van den Broeke (2013), Greenland Surface Mass Balance as Simulated by the Community Earth System Model. Part I: Model Evaluation and 1850-2005 Results, Journal of Climate, 26(20), 7793-7812, doi:10.1175/JCLI-D-12-00615.1.

Vizcaíno, M., U. Mikolajewicz, J. Jungclaus, and G. Schurgers (2010), Climate modification by future ice sheet changes and consequences for ice sheet mass balance, Climate Dynamics, 34(2-3), 301-324, doi: 10.1007/S00382-009-0591-Y.

Figure S1. Simulated GrIS volume from 9,000 B.P. to 100 B.P. (or A.D. 1850) (in units of meters sea level equivalent, m SLE).


Figure S2. Simulated 1960-2005 Greenland ice sheet. a) Absolute thickness with elevation contours (m). b) Thickness bias (m) with respect to measurements from Bamber et al. (2001) (glaciers and ice caps are excluded in the observational data set). The thick black line separates negative and positive anomalies. The white and light blue contours delimit the measured and modeled ice sheet, respectively. Elevation contours of the observed ice sheet are plotted every 500 m.

Figure S3. Comparison of simulated SMB (kg m-2 yr-1) with the high-resolution regional climate model RACMO2 (van Angelen et al. [2012]). The SMB of the upper left panel is calculated at the ice sheet model topography, and of all other panels (RACMO and * labels), at the observed topography [Bamber et al., 2001]. The comparison at the observed and modeled topography is included to separate the effects of atmospheric and SMB model biases from the effects of the coupling between SMB and elevation biases. The runoff (with negative sign) and accumulation corresponding to SMB* are shown in the lower panels. Black elevation contours are plotted every 1000 m. The equilibrium line (SMB=0) is plotted in red.

Figure S4. Sensitivity of the Greenland ice sheet to internal climate variability: 1850-2300 volume evolution for three ensemble simulations with the same historical and scenario forcing (RCP8.5_4x). Simulations only differ in their initial condition at year 1850. Lower panel is a zoom of the upper panel over the period 1850-2100. Units are meters of sea level equivalent.

Figure S5. Greenland ice sheet surface mass balance (SMB, kg m-2 yr-1) projections averaged over 2080-2099 and 2280-2099 under scenarios RCP2.6, RCP4.5, and RCP8.5_4x. Elevation contours are shown every 1000 m. The equilibrium line (SMB=0) is plotted in red. For reference, the modeled mean 1960-2005 SMB is shown in Figure S3.

Figure S6. a) Elevation change by 2080-99 with respect to 1980-99 under RCP8.5 and relative contributions from b) cumulative surface mass balance anomalies and c) ice flow redistribution (m), the latter calculated as the difference between a) and b). d) Ratio between absolute values of ice flow contribution (c) and the sum of absolute cumulative SMB (b) and ice flow contributions (c), as %. Elevation contour lines are plotted in black every 1000 m.

Figure S7. Annual ice discharge to the ocean (Gt yr-1 per 104 km2) for periods 1980-1999 (left) and 2280-2299 (right). The latter corresponds to one of the RCP-ECP8.5_4x simulations (_4x denotes stabilization at four times pre-industrial concentration). The colors represent ice discharge per 100 km x 100 km grid cell (the sum over 100 ice sheet model grid cells). Contour lines represent the modeled 0 m topography (black) and extent of the ice sheet (green).

Min / Mean / Max
Elevation change / -304 / -12 / 197
Cumulative SMB / -659 / -16 / 32
Ice transport anomaly / -313 / 4 / 478

Table S1. Spatial range and mean of elevation change, and of the contributions from cumulative surface mass balance and ice flow anomalies (m) by 2080-2099 with respect to 1980-1999 under RCP8.5 forcing.

1