arXiv subjects

Peter Huybers

Publications and source records attributed to Peter Huybers.

7 recordsLinked to original sources

arXiv2026

How sea level paces faulting at fast-spreading mid-ocean ridges

Abyssal hills, arguably the most extensive coherent pattern in Earth's surface topography, record the spacing of normal faults formed at mid-ocean ridges. At fast-spreading ridges, high-resolution bathymetry shows a pronounced spectral peak near 41 ky, coincident with obliquity-paced Pleistocene sea-level variability. The origin of this apparent orbital imprint on seafloor structure remains unresolved. We hypothesise that glacial-interglacial sea-level variability influences fault spacing by modulating plate thickness and the flexural stresses produced during plate unbending. Sea-level change alters mantle melting rates and magma supply at ridge axes, generating variations in the properties of the accreting plate. As the plate moves off axis, it unbends from its ingrown curvature, producing tensile fibre stresses that drive normal faulting. We hypothesise that small perturbations in elastic plate thickness modulate these stresses and thereby influence fault spacing. To test this, we extend the elastic unbending theory of Buck (2001) to include spatially variable plate thickness and yield-weakening viscoplastic flexure, which localises deformation into discrete kinks interpreted as faults. Linearised analysis shows that plate-thickness perturbations generate proportional fibre-stress variations. Numerical solutions demonstrate that perturbations as small as approximately 0.1 percent can phase-lock faulting to the imposed forcing. When driven by plate-thickness perturbations derived from the Pleistocene oxygen-isotope record, the model predicts fault spacings concentrated near 41 ky in the early Pleistocene and near 100 ky in the late Pleistocene, consistent with observed abyssal-hill spectra. These results provide a quantitative mechanism by which glacial-interglacial sea-level variability can be transmitted into tectonic structure.

physics.geo-ph↗

arXiv2025

Climate change alters teleconnections

Internal modes of climate variability, such as El Niño and the North Atlantic Oscillation, can have strong influences upon distant weather patterns, effects that are referred to as "teleconnections". The extent to which anthropogenic climate change has and will continue to affect these teleconnections, however, remains uncertain. Here, we employ a covariance fingerprinting approach to demonstrate that shifts in teleconnection patterns affecting monthly temperatures between the periods 1960-1990 and 1990-2020 are attributable to anthropogenic forcing. We further apply multilinear regression to assess the regional contributions and statistical significance of changes in five key climate modes: the El Niño-Southern Oscillation, North Atlantic Oscillation, Southern Annular Mode, Indian Ocean Dipole, and the Pacific Decadal Oscillation. In many regions, observed changes exceed what would be expected from natural variability alone, further implicating an anthropogenic influence. Finally, we provide projections of how these teleconnections will alter in response to further changes in climate.

physics.ao-ph↗

arXiv2025

On the attribution of weather events to climate change using a fit to extreme value distributions

Changes in extreme weather events are a potentially important aspect of anthropogenic climate change (ACC), yet, are difficult to attribute to ACC because the record length is often similar to, or shorter than, extreme-event return periods. This study is motivated by the ``World Weather Attribution'' initiative (WWA) and, specifically, their approach of fitting extreme value distribution functions to local observations. They calculate the dependence of distribution parameters on global mean surface temperature (GMST) and use this dependence to attribute extreme events to ACC. Applying this method to preindustrial climate simulations with no time-varying greenhouse gas forcing, we still find a strong dependence of distribution parameters on GMST. This dependence results from internal climate variability (e.g., ENSO) affecting both extreme events and GMST. Therefore, dependence on GMST does not necessarily imply an effect of ACC on extremes. We further consider whether an extreme value, normal, or log-normal distribution better represents the data; if a GMST-dependence of distribution parameters is justified using a likelihood ratio test; and if a meaningful attribution is possible given uncertainties in GMST dependence. We find, for example, that an attribution of Australia's 2020--2021 Bushfires to ACC is difficult due to the effects of internal variability. For the 2019--2021 drought in Madagascar we find that the small number of available data points precludes a meaningful attribution analysis. Overall, we find that the effects of internal climate variability on GMST and the uncertain relationship between GMST and regional extremes may lead to inaccurate attribution conclusions using the part of the WWA approach examined here.

physics.ao-ph↗

arXiv2020

Late 19th-Century Navigational Uncertainties and Their Influence on Sea Surface Temperature Estimates

Accurate estimates of historical changes in sea surface temperatures (SSTs) and their uncertainties are important for documenting and understanding historical changes in climate. A source of uncertainty that has not previously been quantified in historical SST estimates stems from position errors. A Bayesian inference framework is proposed for quantifying errors in reported positions and their implications on SST estimates. The analysis framework is applied to data from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS3.0) in 1885, a time when astronomical and chronometer estimation of position was common, but predating the use of radio signals. Focus is upon a subset of 943 ship tracks from ICOADS3.0 that report their position every two hours to a precision of 0.01° longitude and latitude. These data are interpreted as positions determined by dead reckoning that are periodically updated by celestial correction techniques. The posterior medians of uncertainties in celestial correction are 33.1 km (0.30° on the equator) in longitude and 24.4 km (0.22°) in latitude, respectively. The posterior medians of two-hourly dead reckoning uncertainties are 19.2% for ship speed and 13.2° for ship heading, leading to random position uncertainties with median 0.18° (20 km on the equator) in longitude and 0.15° (17 km) in latitude. Reported ship tracks also contain systematic position uncertainties relating to precursor dead-reckoning positions not being updated after obtaining celestial position estimates, indicating that more accurate positions can be provided for SST observations. Finally, we translate position errors into SST uncertainties by sampling an ensemble of SSTs from the Multi-scale Ultra-high resolution Sea Surface Temperature (MURSST) data set.

stat.AP↗

arXiv2020

An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time

Non-pharmaceutical interventions (NPIs) have been crucial in curbing COVID-19 in the United States (US). Consequently, relaxing NPIs through a phased re-opening of the US amid still-high levels of COVID-19 susceptibility could lead to new epidemic waves. This calls for a COVID-19 early warning system. Here we evaluate multiple digital data streams as early warning indicators of increasing or decreasing state-level US COVID-19 activity between January and June 2020. We estimate the timing of sharp changes in each data stream using a simple Bayesian model that calculates in near real-time the probability of exponential growth or decay. Analysis of COVID-19-related activity on social network microblogs, Internet searches, point-of-care medical software, and a metapopulation mechanistic model, as well as fever anomalies captured by smart thermometer networks, shows exponential growth roughly 2-3 weeks prior to comparable growth in confirmed COVID-19 cases and 3-4 weeks prior to comparable growth in COVID-19 deaths across the US over the last 6 months. We further observe exponential decay in confirmed cases and deaths 5-6 weeks after implementation of NPIs, as measured by anonymized and aggregated human mobility data from mobile phones. Finally, we propose a combined indicator for exponential growth in multiple data streams that may aid in developing an early warning system for future COVID-19 outbreaks. These efforts represent an initial exploratory framework, and both continued study of the predictive power of digital indicators as well as further development of the statistical approach are needed.

stat.AP↗

arXiv2020

Fever and mobility data indicate social distancing has reduced incidence of communicable disease in the United States

In March of 2020, many U.S. state governments encouraged or mandated restrictions on social interactions to slow the spread of COVID-19, the disease caused by the novel coronavirus SARS-CoV-2 that has spread to nearly 180 countries. Estimating the effectiveness of these social-distancing strategies is challenging because surveillance of COVID-19 has been limited, with tests generally being prioritized for high-risk or hospitalized cases according to temporally and regionally varying criteria. Here we show that reductions in mobility across U.S. counties with at least 100 confirmed cases of COVID-19 led to reductions in fever incidences, as captured by smart thermometers, after a mean lag of 6.5 days ($90\%$ within 3--10 days) that is consistent with the incubation period of COVID-19. Furthermore, counties with larger decreases in mobility subsequently achieved greater reductions in fevers ($p<0.01$), with the notable exception of New York City and its immediate vicinity. These results indicate that social distancing has reduced the transmission of influenza like illnesses, including COVID 19, and support social distancing as an effective strategy for slowing the spread of COVID-19.

q-bio.PE↗

arXiv2017

Modelling Coupled Oscillations of Volcanic CO2 Emissions and Glacial Cycles

Following the mid-Pleistocene transition, the dominant period of glacial cycles changed from 40 ka to ~100 ka. It is broadly accepted that the 40 ka glacial cycles were driven by cyclical changes in obliquity. However, this forcing does not explain the ~100 ka glacial cycles. Mechanisms have been proposed for ~100 ka cycles, but none are universally accepted. Any proposed mechanism for ~100 ka glacial cycles must give the Earth's climate system a memory of tens-of-thousands-of-years. This timescale is difficult to achieve for surface processes, however it is possible for the solid Earth. Recent work suggests volcanic CO2 emissions change in response to glacial cycles and that there could be a ~50 ka delay in that response. Such a lagged response could drive glacial cycles from 40 ka cycles to an integer multiple of the forcing period. Under what conditions could the climate system admit such a response? We investigate this using a reduced-complexity model to establish the threshold volcanic variations required to induce 100 ka glacial cycles. This threshold is compared to the probable range of volcanic activity over glacial cycles. Our model is composed of three component models for energy balance, ice sheet growth and atmospheric CO2. The model is driven by insolation alone, with other components varying according to a system of coupled, differential equations. The model replicates modern climate conditions and seasonality, CO2-doubling experiments, and - when forced with ice core CO2 - the late-Pleistocene sea level record. We obtain a switch from 40 ka to ~100 ka cycles as the mid-ocean ridge CO2 response to glacial cycles is increased. These ~100 ka cycles are phase-locked to obliquity, lasting 80 or 120 ka. The mid-ocean ridge response required is, at best, just within a 95% confidence interval of MOR CO2 emissions.

physics.geo-ph↗