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Abstract
The slow creeping motion of Earth’s mantle drives transient changes in surface topography across a variety of spatial and temporal scales. Recent decades have seen substantial progress in understanding this so-called `dynamic topography’, with a growing number of studies highlighting its fundamental role in shaping the surface of our planet. In this review, we outline the current frontiers of geodynamical research into dynamic topography. It begins with a summary of ongoing observational, theoretical and computational efforts that aim to quantify the present-day expression of dynamic topography, including its geographical distribution and sensitivity to different components of the mantle's flow regime. Next, observational constraints that shed light on how dynamic topography has changed over time are summarised, and compared with predictions from a range of geodynamical modelling studies, to highlight our current understanding of its evolution through the geological past. Although many model predictions can be reconciled with the available observational constraints, these comparisons demonstrate that there remain inconsistencies, particularly at shorter spatial and temporal scales. These discrepancies allow us to isolate the shortcomings of existing modelling approaches and identify pathways towards improving future reconstructions of dynamic topography through space and time. Such reconstructions are vital if we are to robustly connect the evolution of Earth's surface environments to the processes that are occurring deep within its interior.
DOI
https://doi.org/10.31223/X55W5T
Subjects
Earth Sciences
Keywords
Dynamic topography, Residual Topography, mantle convection, Epiorogeny, geoid, uplift, subsidence, Reconstructions
Dates
Published: 2022-04-10 02:23
Last Updated: 2022-04-10 09:23
License
CC BY Attribution 4.0 International
Additional Metadata
Data Availability (Reason not available):
The specific present-day residual topography dataset used here, which was originally constructed by Hoggard et al. (2017) may be obtained from https://github.com/drhodrid/Davies_etal_NGeo_2019_Datasets. Gaussian Process code to analyse the present-day residual topography dataset may be obtained from https://github.com/valentineap/DynamicTopographyGP (doi: 10.5281/zenodo.3895317). Model predictions of dynamic topography and geoid height anomalies may be obtained from https://github.com/drhodrid/Davies_etal_2022_observations_models_dynamic_topography.
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