Earth’s multi-scale topographic response to global mantle flow

Abstract

Earth's surface topography is a direct physical expression of our planet's dynamics. Most is isostatic, controlled by thickness and density variations within the crust and lithosphere, but a significant proportion arises from forces exerted by underlying mantle convection. This dynamic topography directly connects the evolution of surface environments to Earth's deep interior, but predictions from mantle flow simulations are often inconsistent with inferences from the geological record, with little consensus about its spatial pattern, wavelength and amplitude. Here, we demonstrate that previous comparisons between predictive models and observational constraints have been biased by subjective choices. Using measurements of residual topography beneath the oceans, and a hierarchical Bayesian approach to performing spherical harmonic analyses, we generate a robust estimate of Earth's oceanic residual topography power spectrum. This indicates power of 0.5 ± 0.35 km 2 and peak amplitudes of ∼0.8 ± 0.1 km at long-wavelength (∼10 4 km), decreasing by roughly one order of magnitude at shorter wavelengths (∼10 3 km). We show that geodynamical simulations can only be reconciled with observational constraints if they incorporate lithospheric structure and its impact on mantle flow. This demonstrates that both deep (long-) and shallow (shorter-wavelength) processes are crucial, and implies that dynamic topography is intimately connected to the structure and evolution of Earth's lithosphere. Between Earth's crust and core lies the mantle, a 2,900 km-thick layer of hot rock that constitutes greater than 80% of Earth's volume. Carrying heat to the surface, the convecting mantle is the 'engine' that drives our dynamic planet: it is directly or indirectly responsible for almost all large-scale tectonic and geological activity [1]. As the mantle flows, it transmits normal stresses to the lithosphere-Earth's rigid outermost shell-that are balanced by gravitational stresses arising through topographic deflections of Earth's surface [2, 3, 4, 5, 6, 7, 8, 9]. This so-called dynamic topography is transient, varying both spatially and temporally in response to underlying mantle flow. As a result, it is more challenging to isolate than isostatic topography. The relative importance of dynamic versus isostatic topography varies according to setting: for example, the elevation of the Himalaya is principally isostatic, due to the presence of Earth's thickest continental crust; but the broad excess elevation of the stable South African craton has been

Drainage and Sedimentary Responses to Dynamic Topography

“…Although dynamic topography is best identified for wavelengths greater than 2,500 km (Davies et al, 2019), wavelengths as short as~300 km have been proposed (Hartley et al, 2011;Rudge et al, 2008). These short wavelengths interact with isostatic and flexural topography (e.g., McKenzie, 2010 (Hartley et al, 2011).…”

Global Models From Sparse Data: A Robust Estimate of Earth's Residual Topography Spectrum

“…The present paper, therefore, develops an alternative approach to analysis, building on the statistical theory of Gaussian processes (e.g., Rasmussen & Williams, 2006) and the work of Sambridge (2020a, 2020b). This method remains stable for even the smallest data sets and allows us to convincingly demonstrate that the conclusions of Davies et al (2019) remain valid when only the highest quality data points are used. We are also able to obtain maps showing where new measurements would be most valuable in constraining the residual topography profile.…”

“…Results indicate that the spectrum was dominated by long-wavelength (∼10 4 km) features, with power dropping by about an order of magnitude at shorter wavelengths (∼10 3 km). Based upon predictions from instantaneous models of global mantle dynamics, Davies et al (2019) demonstrate that both deep mantle flow and shallow, lithosphere-controlled processes are important in generating this surface response.…”

“…In an effort to reduce subjectivity in the analysis, a recent study by Davies et al (2019) considered the database of Hoggard et al (2017) using a novel hierarchical Bayesian approach (Valentine & Sambridge, 2018). In particular, a strategy of "Automatic Relevance Determination" (ARD) was employed to avoid the need to impose any predetermined notion of "smoothness" upon the residual topography.…”

“…However, one drawback in the ARD approach employed by Davies et al (2019) is that it becomes unstable as the number of data points is reduced. Within the database compiled by (Hoggard et al, 2016(Hoggard et al, , 2017, a subset of around 5% of the measurements are considered markedly more robust than the rest.…”