Semiempirical and process-based global sea level projections

Authors

  • John C. Moore,

    Corresponding author
    1. State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
    2. Arctic Centre, University of Lapland, Rovaniemi, Finland
    3. Department of Earth Sciences, Uppsala University, Uppsala, Sweden
    • Corresponding author: J. C. Moore, State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, 19 Xinjiekou Wai St., Beijing 100875, China. (john.moore.bnu@gmail.com)

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  • Aslak Grinsted,

    1. State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
    2. Centre for Ice and Climate, Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
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  • Thomas Zwinger,

    1. State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
    2. CSC-IT Center for Science Ltd., Espoo, Finland
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  • Svetlana Jevrejeva

    1. State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
    2. National Oceanography Centre, Liverpool, UK
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Abstract

We review the two main approaches to estimating sea level rise over the coming century: physically plausible models of reduced complexity that exploit statistical relationships between sea level and climate forcing, and more complex physics-based models of the separate elements of the sea level budget. Previously, estimates of future sea level rise from semiempirical models were considerably larger than those from process-based models. However, we show that the most recent estimates of sea level rise by 2100 using both methods have converged, but largely through increased contributions and uncertainties in process-based model estimates of ice sheets mass loss. Hence, we focus in this paper on ice sheet flow as this has the largest potential to contribute to sea level rise. Progress has been made in ice dynamics, ice stream flow, grounding line migration, and integration of ice sheet models with high-resolution climate models. Calving physics remains an important and difficult modeling issue. Mountain glaciers, numbering hundreds of thousands, must be modeled by extensive statistical extrapolation from a much smaller calibration data set. Rugged topography creates problems in process-based mass balance simulations forced by regional climate models with resolutions 10–100 times larger than the glaciers. Semiempirical models balance increasing numbers of parameters with the choice of noise model for the observations to avoid overfitting the highly autocorrelated sea level data. All models face difficulty in separating out non-climate-driven sea level rise (e.g., groundwater extraction) and long-term disequilibria in the present-day cryosphere-sea level system.

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