We present a new set of global and local sea-level projections at example tide gauge locations under the RCP2.6, RCP4.5, and RCP8.5 emissions scenarios. Compared to the CMIP5-based sea-level projections presented in IPCC AR5, we introduce a number of methodological innovations, including (i) more comprehensive treatment of uncertainties, (ii) direct traceability between global and local projections, and (iii) exploratory extended projections to 2300 based on emulation of individual CMIP5 models. Combining the projections with observed tide gauge records, we explore the contribution to total variance that arises from sea-level variability, different emissions scenarios, and model uncertainty. For the period out to 2300 we further breakdown the model uncertainty by sea-level component and consider the dependence on geographic location, time horizon, and emissions scenario. Our analysis highlights the importance of local variability for sea-level change in the coming decades and the potential value of annual-to-decadal predictions of local sea-level change. Projections to 2300 show a substantial degree of committed sea-level rise under all emissions scenarios considered and highlight the reduced future risk associated with RCP2.6 and RCP4.5 compared to RCP8.5. Tide gauge locations can show large (> 50%) departures from the global average, in some cases even reversing the sign of the change. While uncertainty in projections of the future Antarctic ice dynamic response tends to dominate post-2100, we see substantial differences in the breakdown of model variance as a function of location, time scale, and emissions scenario.

[1] This study analyzes the uncertainties in the models of the Greenland Ice Sheet (GIS) that arise from ill-constrained geothermal heat flux (GHF) distribution. Within the context of dynamic GIS modeling, we consider the following questions: (i) What is the significance of the differences between the existing GHF models for the GIS modeling studies? (ii) How well does the modeled GIS controlled by the GHF models agree with the observational data? (iii) What are the relative contributions of uncertainties in GHF and climate forcing to the misfit between the observed and modeled present-day GIS? The results of paleoclimatic simulations suggest that differences in the GHF models have a major effect on the history and resulting present-day state of the GIS. The ice sheet model controlled by any of these GHF forcings reproduces the observed GIS state to only a limited degree and fails to reproduce either the topography or the low basal temperatures measured in southern Greenland. By contrast, the simulation controlled by a simple spatially uniform GHF forcing results in a considerably better fit with the observations, raising questions about the use of the three GHF models within the framework of GIS modeling. Sensitivity tests reveal that the misfit between the modeled and measured temperatures in central Greenland is mostly due to inaccurate GHF and Wisconsin precipitation forcings. The failure of the ice sheet model in southern Greenland, however, is mainly caused by inaccuracies in the surface temperature forcing and the generally overestimated GHF values suggested by all GHF models.

[1] In this study, the memory of the Greenland Ice Sheet (GIS) with respect to its past states is analyzed. According to ice core reconstructions, the present-day GIS reflects former climatic conditions dating back to at least 250 thousand years before the present (kyr BP). This fact must be considered when initializing an ice sheet model. The common initialization techniques are paleoclimatic simulations driven by atmospheric forcing inferred from ice core records and steady state simulations driven by the present-day or past climatic conditions. When paleoclimatic simulations are used, the information about the past climatic conditions is partly reflected in the resulting present-day state of the GIS. However, there are several important questions that need to be clarified. First, for how long does the model remember its initial state? Second, it is generally acknowledged that, prior to 100 kyr BP, the longest Greenland ice core record (GRIP) is distorted by ice-flow irregularities. The question arises as to what extent do the uncertainties inherent in the GRIP-based forcing influence the resulting GIS? Finally, how is the modeled thermodynamic state affected by the choice of initialization technique (paleo or steady state)? To answer these questions, a series of paleoclimatic and steady state simulations is carried out. We conclude that (1) the choice of an ice-covered initial configuration shortens the initialization simulation time to 100 kyr, (2) the uncertainties in the GRIP-based forcing affect present-day modeled ice-surface topographies and temperatures only slightly, and (3) the GIS forced by present-day climatic conditions is overall warmer than that resulting from a paleoclimatic simulation.

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