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The Antarctic Ice Sheet is an important indicator of climate change and driver of sea-level rise. Here we combine satellite observations of its changing volume, flow and gravitational attraction with modelling of its surface mass balance to show that it lost 2,720 ± 1,390 billion tonnes of ice between 1992 and 2017, which corresponds to an increase in mean sea level of 7.6 ± 3.9 millimetres (errors are one standard deviation). Over this period, ocean-driven melting has caused rates of ice loss from West Antarctica to increase from 53 ± 29 billion to 159 ± 26 billion tonnes per year; ice-shelf collapse has increased the rate of ice loss from the Antarctic Peninsula from 7 ± 13 billion to 33 ± 16 billion tonnes per year. We find large variations in and among model estimates of surface mass balance and glacial isostatic adjustment for East Antarctica, with its average rate of mass gain over the period 1992–2017 (5 ± 46 billion tonnes per year) being the least certain.

Rebound effects are commonly defined as the relative gap between the potential and realized savings in resource use following efficiency improvements or sufficiency changes. While a considerable number of studies quantify rebound effects, empirical estimates vary widely. Reliable information on the magnitude of rebound effects is therefore still lacking, despite being essential to devise and adjust, for example, energy efficiency policies accordingly. Here, we present the first meta-regression analysis of microeconomic rebound effects at the household level, using forty-three studies with 1,118 estimates to determine average rebound effects and to explain heterogeneous empirical findings. We find that the total microeconomic rebound is, on average, about 41%–52%. The variance can be explained by differences in the type of data used, the scenario setup, and the specifics of the rebound estimation in the primary studies. Furthermore, we find only small absolute transfer errors, indicating a good predictability of rebound effects using our meta-regression model.

Teleworking is widely considered to be a way of solving mobility issues by decreasing the number of commuting trips. However, little is known about teleworking and, more specifically, its links with spatial mobilities and the potential rebound effects. Statistical analysis of data from the Swiss Mobility and Transport Microcensus shows some limits to the ability of teleworking to regulate mobility in Switzerland. Firstly, commuting to a conventional workplace is replaced by (albeit shorter) journeys for other purposes. Secondly, and more importantly, teleworkers live further away from the workplace than their colleagues (24.6 km vs. 16.1 km). Our analysis shows that, although teleworking may reduce the number of commuting trips, it is likely to increase the distance travelled over a working week. Being able to work at home for part of the week may consequently decrease the propensity for residential relocation and increase tolerance for long distance commuting.

Ice losses from the Greenland and Antarctic ice sheets have accelerated since the 1990s, accounting for a significant increase in the global mean sea level. Here, we present a new 29-year record of ice sheet mass balance from 1992 to 2020 from the Ice Sheet Mass Balance Inter-comparison Exercise (IMBIE). We compare and combine 50 independent estimates of ice sheet mass balance derived from satellite observations of temporal changes in ice sheet flow, in ice sheet volume, and in Earth's gravity field. Between 1992 and 2020, the ice sheets contributed 21.0±1.9 mm to global mean sea level, with the rate of mass loss rising from 105 Gt yr−1 between 1992 and 1996 to 372 Gt yr−1 between 2016 and 2020. In Greenland, the rate of mass loss is 169±9 Gt yr−1 between 1992 and 2020, but there are large inter-annual variations in mass balance, with mass loss ranging from 86 Gt yr−1 in 2017 to 444 Gt yr−1 in 2019 due to large variability in surface mass balance. In Antarctica, ice losses continue to be dominated by mass loss from West Antarctica (82±9 Gt yr−1) and, to a lesser extent, from the Antarctic Peninsula (13±5 Gt yr−1). East Antarctica remains close to a state of balance, with a small gain of 3±15 Gt yr−1, but is the most uncertain component of Antarctica's mass balance. The dataset is publicly available at https://doi.org/10.5285/77B64C55-7166-4A06-9DEF-2E400398E452 (IMBIE Team, 2021).

The post‐movement beta rebound has been studied extensively using magnetoencephalography (MEG) and is reliably modulated by various task parameters as well as illness. Our recent study showed that rebounds, which we generalise as “post‐task responses” (PTRs), are a ubiquitous phenomenon in the brain, occurring across the cortex in theta, alpha, and beta bands. Currently, it is unknown whether PTRs following working memory are driven by transient bursts, which are moments of short‐lived high amplitude activity, similar to those that drive the post‐movement beta rebound. Here, we use three‐state univariate hidden Markov models (HMMs), which can identify bursts without a priori knowledge of frequency content or response timings, to compare bursts that drive PTRs in working memory and visuomotor MEG datasets. Our results show that PTRs across working memory and visuomotor tasks are driven by pan‐spectral transient bursts. These bursts have very similar spectral content variation over the cortex, correlating strongly between the two tasks in the alpha (R2 = .89) and beta (R2 = .53) bands. Bursts also have similar variation in duration over the cortex (e.g., long duration bursts occur in the motor cortex for both tasks), strongly correlating over cortical regions between tasks (R2 = .56), with a mean over all regions of around 300 ms in both datasets. Finally, we demonstrate the ability of HMMs to isolate signals of interest in MEG data, such that the HMM probability timecourse correlates more strongly with reaction times than frequency filtered power envelopes from the same brain regions. Overall, we show that induced PTRs across different tasks are driven by bursts with similar characteristics, which can be identified using HMMs. Given the similarity between bursts across tasks, we suggest that PTRs across the cortex may be driven by a common underlying neural phenomenon. We investigated post‐task magnetoencephalography responses, that is, oscillatory rebounds, following working memory and movement tasks by performing a burst analysis using hidden Markov models. Results showed that these responses were driven by transient bursts whose intrinsic characteristics were remarkably similar across tasks, despite large differences in the two tasks performed.

The ITRF2008 velocity field is demonstrated to be of higher quality and more precise than past ITRF solutions. We estimated an absolute tectonic plate motion model made up of 14 major plates, using velocities of 206 sites of high geodetic quality (far from plate boundaries, deformation zones and Glacial Isostatic Adjustment (GIA) regions), derived from and consistent with ITRF2008. The precision of the estimated model is evaluated to be at the level of 0.3 mm/a WRMS. No GIA corrections were applied to site velocities prior to estimating plate rotation poles, as our selected sites are outside the Fennoscandia regions where the GIA models we tested are performing reasonably well, and far from GIA areas where the models would degrade the fit (Antarctica and North America). Our selected velocity field has small origin rate bias components following the three axis (X, Y, Z), respectively 0.41 ± 0.54, 0.22 ± 0.64 and 0.41 ± 0.60 (95 per cent confidence limits). Comparing our model to NNR‐NUVEL‐1A and the newly available NNR‐MORVEL56, we found better agreement with NNR‐MORVEL56 than with NNR‐NUVEL‐1A for all plates, except for Australia where we observe an average residual rotation rate of 4 mm/a. Using our selection of sites, we found large global X‐rotation rates between the two models (0.016°/Ma) and between our model and NNR‐MORVEL56 of 0.023°/Ma, equivalent to 2.5 mm/a at the Earth surface. Key Points We estimated a global motion model of 14 major plates consistent with ITRF2008 We found a small frame origin rate bias, at the level of 0.4 +/‐0.6 mm/yr The precision of the estimated model is at the level of 0.3 mm/a WRMS

A tectonic Plate Motion Model (PMM) is essential for geodetic applications, while contributing to the understanding of geodynamic processes affecting the Earth's surface. We introduce a PMM derived from the horizontal velocities of 518 sites extracted from the ITRF2020 solution. These sites were chosen away from plate boundaries, Glacial Isostatic Adjustment regions, and other deforming zones. Unlike the ITRF2014‐PMM, which showed no significant Origin Rate Bias (ORB), velocities used to determine the ITRF2020‐PMM exhibit a statistically significant ORB (0.74 ± 0.09 mm/yr along the Z‐component). Users are advised to add the estimated ORB to the horizontal velocities predicted by the ITRF2020‐PMM rotation poles for full consistency with the ITRF2020. However, the predicted vertical velocities resulting from the addition of the ORB should be discarded. The overall precision with which the ITRF2020 velocity field is represented by the rigid ITRF2020‐PMM is at the level of 0.25 mm/yr WRMS. Plain Language Summary The Earth's surface is divided in large and small tectonic plates, which evolve and move slowly over time, resulting in lateral displacements of the ground surface typically of the order of a few cm/yr. Because of the relative motion between tectonic plates, plate boundaries can be either divergent (when two plates move away from each other), convergent (when two plates collide) or transform (when two plates slide past each other). Plate motion models are used to quantify the relative motions of the plates with respect to each other, and are determined using geological data or observations collected by space geodesy instruments distributed over different plates at the Earth's surface. In the latter case, space geodesy observations from the four space geodetic techniques covering more than 40 years of data are analyzed to estimate the long‐term displacements (or velocities) of each instrument in a well defined and self‐consistent global reference frame. The derived velocity field is then used to estimate a comprehensive plate motion model (PMM). This article presents a PMM for 13 tectonic plates based on a subset of the velocity field from the recently released International Terrestrial Reference Frame 2020 (ITRF2020); see https://itrf.ign.fr/en/solutions/ITRF2020. Key Points We derive a plate motion model for 13 tectonic plates from the ITRF2020 horizontal velocity field Built under the rigid‐plate motion hypothesis, the model represents the ITRF2020 velocity field with a precision of 0.25 mm/yr WRMS The residual velocities would show a global northward motion if a translation rate was not included in the inversion model

Innovations in the mobility industry such as automated and connected cars could significantly reduce congestion and emissions by allowing the traffic to flow more freely and reducing the number of vehicles according to some researchers. However, the effectiveness of these sustainable product and service innovations is often limited by unexpected changes in consumption: some researchers thus hypothesize that the higher comfort and improved quality of time in driverless cars could lead to an increase in demand for driving with autonomous vehicles. So far, there is a lack of empirical evidence supporting either one or other of these hypotheses. To analyze the influence of autonomous driving on mobility behavior and to uncover user preferences, which serve as indicators for future travel mode choices, we conducted an online survey with a paired comparison of current and future travel modes with 302 participants in Germany. The results do not confirm the hypothesis that ownership will become an outdated model in the future. Instead they suggest that private cars, whether conventional or fully automated, will remain the preferred travel mode. At the same time, carsharing will benefit from full automation more than private cars. However, the findings indicate that the growth of carsharing will mainly be at the expense of public transport, showing that more emphasis should be placed in making public transport more attractive if sustainable mobility is to be developed.

Earth’s spin axis slowly moves relative to the crust over time. A 120-year-long record of this polar motion from astronomical and more modern geodetic measurements displays interannual and multidecadal fluctuations of 20 to 40 milliarcseconds superimposed on a secular trend of about 3 milliarcseconds per year. Earth’s polar motion is thought to be driven by various surface and interior processes, but how these processes operate and interact to produce the observed signal remains enigmatic. Here we show that predictions made by an ensemble of physics-informed neural networks trained on measurements to capture geophysical processes can explain the main features of the observed polar motion. We find that glacial isostatic adjustment and mantle convection primarily account for the secular trend. Mass redistribution on the Earth’s surface—for example, ice melting and global changes in water storage—yields a relatively weak trend but explains about 90% of the interannual and multidecadal variations. We also find that core processes contribute to both the secular trend and fluctuations in polar motion, either due to variations in torque at the core–mantle boundary or dynamical feedback of the core in response to surface mass changes. Our findings provide constraints on core–mantle interactions for which observations are rare and global ice mass balance over the past century and suggest feedback operating between climate-related surface processes and core dynamics. Core processes, dynamically linked to mantle and climate-related surface processes, contribute to both the long-term trend and shorter-term fluctuations observed in Earth’s polar motion, according to predictions from physics-informed neural networks.

During the last interglacial period, 127–116 kyr ago, global mean sea level reached a peak of 5–9 m above present-day sea level. However, the exact timing and magnitude of ice sheet collapse that contributed to the sea-level highstand is unclear. Here we explore this timing using stratigraphic and geomorphic mapping and uranium-series geochronology of fossil coral reefs and geophysical modelling of sea-level records from Western Australia. We show that between 127 and 119 kyr ago, eustatic sea level remained relatively stable at about 3–4 m above present sea level. However, stratigraphically younger fossil corals with U-series ages of 118.1±1.4 kyr are observed at elevations of up to 9.5 m above present mean sea level. Accounting for glacial isostatic adjustment and localized tectonics, we conclude that eustatic sea level rose to about 9 m above present at the end of the last interglacial. We suggest that in the last few thousand years of the interglacial, a critical ice sheet stability threshold was crossed, resulting in the catastrophic collapse of polar ice sheets and substantial sea-level rise. Sea level during the last interglacial period reached a peak of between 5 and 9 m above the present-day level. A detailed reconstruction of sea level and isostatic rebound from Western Australia indicates a prolonged period of sea-level stability at 3–4 m above present, followed by an abrupt sea-level rise of 5–6 m.