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Ancient viruses preserved in glacial ice hold valuable information about changes in Earth's climate, a new study suggests.

The grid-level fixed asset model developed for China from 1951 to 2020
Danhua Xin, James Edward Daniell, Zhenguo Zhang, Friedemann Wenzel, Shaun Shuxun Wang, and Xiaofei Chen
Nat. Hazards Earth Syst. Sci. Discuss., https//doi.org/10.5194/nhess-2024-138,2024
Preprint under review for NHESS (discussion: open, 0 comments)
A high-resolution fixed asset model can help improve the accuracy of earthquake loss assessment. We develop a grid-level fixed asset model for China from 1951 to 2020. We first compile the provincial-level fixed asset from yearbook-related statistics. Then, this dataset is disaggregated into 1 km*1 km grids by using multiple remote sensing data as the weight indicator. We find that fixed asset value increased rapidly after the 1980s and reached 589.31 trillion Chinese yuan in 2020.

It is widely accepted that the early Earth largely consisted of molten magma, forming a global ocean of magma. This extreme state of Earth was likely caused by the intense heat generated from accretionary impacts, meaning the collision of smaller celestial bodies with Earth. Understanding the formation of this magma ocean is crucial for comprehending Earth's formation.

Author(s): Lucas Bourscheidt and Fernando Haas

The theory of gauge transformations in linearized gravitation is investigated. After a brief discussion of the fundamentals of the kinetic theory in curved spacetime, the Einstein-Vlasov-Maxwell (EVM) system of equations in terms of gauge-invariant quantities is established without neglecting the eq…

[Phys. Rev. E 110, 025207] Published Mon Aug 26, 2024

Nature Geoscience, Published online: 26 August 2024; doi:10.1038/s41561-024-01508-z

Genomes recovered from a Tibetan Plateau ice core extending back 41,000 years show that preserved viral communities varied substantially with cold-to-warm climate cycles.

Abstract

In this study, we simulate the Solar Wind Charge Exchange (SWCX) soft X-ray emissions at dayside magnetosheath and cusps by using magnetohydrodynamic (MHD) and LAtmos TEst Particle (LaTeP) models. MHD models are unable to resolve the particle kinetic effects, such as the different behaviors of ions with different q/m, or distinguish the magnetospheric plasma from the solar wind plasma. We investigate these effects with the LaTeP model. As the LaTeP model does not self-compute magnetic and electric field, the magnetic and electric field data obtained from Open Geospace General Circulation Model (OpenGGCM) and Lagrangian version of the piecewise parabolic method (PPMLR) MHD model are used as the input to LaTeP model. The soft X-ray emissivity maps simulated from pure OpenGGCM and PPMLR MHD approaches and from LaTeP-OpenGGCM and LaTeP-PPMLR approaches are presented and compared. The results indicate that the LaTeP model can well resolve the kinetic effects and can be used to investigate the individual spectral characteristics. Therefore, the LaTeP model is a complementary approach for simulating the X-ray emissions near the dayside magnetopause. We also calculate the ratio of integrated OVII/OVIII line intensities, produced by charge exchange of O7+ ions and O8+ ions, respectively. We find a relatively higher ratio at the bow shock compared to the surrounding areas, suggesting that this ratio can be an effective parameter to identify the bow shock location.

Abstract

Carbon and nitrogen are considered as candidate light elements present in planetary cores. However, there is limited understanding regarding the structure and physical properties of Fe-C-N alloys under extreme conditions. Here diamond anvil cell experiments were conducted, revealing the stability of hexagonal-structured Fe7(N0.75C0.25)3 up to 120 GPa and 2100 K, without undergoing any structural transformation or dissociation. Notably, the thermal expansion coefficient and Grüneisen parameter of the alloy exhibit a collapse at 55–70 GPa. First-principles calculations suggest that such anomaly is associated with the spin transition of iron within Fe7(N0.75C0.25)3. Our modeling indicates that the presence of ∼1.0 wt% carbon and nitrogen in liquid iron contributes to 9–12% of the density deficit of the Earth's outer core. The thermoelastic anomaly of the Fe-C-N alloy across the spin transition is likely to affect the density and seismic velocity profiles of (C,N)-rich planetary cores, thereby influencing the dynamics of such cores.

The global concentrations of one of the main air pollutants known to affect human health has been graphically illustrated for the first time by a team of scientists.

Abstract

We extend the previous three-dimensional (3D) empirical reconstruction (ER) model for a set of ideal magnetohydrodynamics (MHD) constraints into a resistive MHD 3D ER model that includes additional resistive MHD constraints and additional measurements from NASA's Magnetospheric Multiscale (MMS) mission. The same form of a stochastic optimization algorithm is used as in the previous ideal MHD 3D ER model to directly minimize the loss function that includes a few more highly nonlinear terms characterizing the model-measurement differences and the model departures from physical constraints. The resistive MHD 3D ER model is applied to three regions of MMS measurements that correspond to direct sampling of an electron diffusion region (EDR), a region adjacent to the EDR, and one far away from the EDR. The reconstructed plasma and electromagnetic fields are of high quality in all three regions as measured by model-measurement difference indices and physics-based quality indicators. The reconstructed fields in the EDR provide us with a good view of the spatial configuration of the reconnection site. We specifically examine the effect of resistivity on energy exchange in the vicinity of the EDR. It was discovered that in the EDR, the energy exchange shows an exclusive and systematic one-channel process between the plasma thermal energy and electromagnetic energy with the conversion rate highly correlated with the strength of the turbulent electromagnetic fields. In the other two regions away from the EDR, the energy exchange between the electromagnetic energy and the plasma thermal and kinetic energies shows rapidly-varying and random characteristics.

Abstract

This study proposes a data-driven troposphere zenith total delay (ZTD) modeling method that takes into account the distance of GNSS continuously operating reference station (CORS) to the coast. Using ZTD data from 106 CORS stations, the strong correlation between the CORS-to-coast distance and ZTD is first identified. Then, this CORS-to-coast distance is incorporated as a new input data, along with the traditional time epoch and location input data, to develop a deep learning-based neural network model for ZTD prediction. This model is trained using 96 CORS stations spaced an average of 92 km apart. Ten testing CORS stations are divided into five inner stations and five outer stations from the CORS network to evaluate the ZTD prediction accuracy. Results from the study show that the proposed method improves the accuracy of ZTD prediction over traditional methods for a four-month period in 2018. At inner testing stations, the average ZTD prediction Root-mean-square errors (RMSEs) of the proposed method is 29.5 mm, which is smaller than the 34.2 mm of the traditional method. For outer testing stations, the average ZTD prediction RMSEs are 31.0 mm and 41.3 mm for the proposed method and traditional method respectively, resulting in a 5/17% ZTD prediction accuracy improvement. To sum up, the proposed method, which considers the CORS-to-coast distance for ZTD modeling, is demonstrated to enhance ZTD prediction accuracy over the traditional method.

Abstract

At mid-latitudes, a narrow band of near-surface wind convergence (NSWC) overlies the western boundary currents in long-term climatology as a response to steep sea surface temperature gradients. The underlying dynamics shaping mean convergence in the mid-latitude region have been investigated in detail. In polar regions, surface temperature gradients are intense along the sea ice edges. However, literature concerning NSWC near sea ice edges is limited. This study investigates time-mean NSWC along sea ice edges and its shaping processes, focusing on the Greenland Sea, based on atmospheric reanalysis. In cold-season climatology, positive NSWC overlies the sea ice edge, resulting in a localized upward motion reaching the free atmosphere. The mean NSWC was insensitive to sea ice thickness and surface roughness in the regional model. This study suggests that, in addition to local atmospheric boundary processes, extreme NSWC events play a vital role in shaping the mean distribution. Although these features are similar to those along the Gulf Stream, atmospheric fronts appear to play a relatively minor role in the Greenland Sea. Instead, the frequent cyclone generation near the sea ice edge and the anticyclonic circulation over Greenland in conjunction with the transient synoptic circulation seem essential. In the warm season, positive NSWC was virtually missing in the Greenland Sea, unlike in the Gulf Stream region, reflecting the shallow virtual temperature response to the surface thermal forcing. This study contributes to understanding the mechanisms by which sea ice variability affects large-scale atmospheric circulation in remote regions.

Abstract

Tropical wetlands account for ∼20% of the global total methane (CH4) emissions, but uncertainties remain in emission estimation due to the inaccurate representation of wetland spatiotemporal variations. Based on the latest satellite observational inundation data, we constructed a model to map the long-term time series of wetland extents over the Sudd floodplain, which has recently been identified as an important source of wetland CH4 emissions. Our analysis reveals an annual, total wetland extent of 5.73 ± 2.05 × 104 km2 for 2003–2022, with a notable accelerated expansion rate of 1.19 × 104 km2 yr−1 during 2019–2022 driven by anomalous upstream precipitation patterns. We found that current wetland products generally report smaller wetland areas, resulting in a systematic underestimation of wetland CH4 emissions from the Sudd wetland. Our study highlights the pivotal role of comprehensively characterizing the seasonal and interannual dynamics of wetland extent to accurately estimate CH4 emissions from tropical floodplains.

Abstract

Tectonic landforms and surficial lithologic age are essential data for producing quality late Quaternary fault maps and predicting coseismic fault rupture location before an earthquake. However, we lack a clear understanding of the relationship between tectonic landforms and shallow earthquake processes and how lithologic age relates to landform preservation. We assess how fault location error (rupture-to-fault separation distance) and coseismic displacement residual (difference between observed and predicted coseismic displacement) vary with tectonic landform and lithologic age for four historical earthquakes. Certain tectonic landforms identified before these earthquakes correlate with lower fault location errors and median displacements below model predictions. Faults cutting Holocene units exhibit the largest location errors, reflecting surface processes that erode or bury fault evidence. This study shows that tectonic landforms and lithologic age have a significant impact on fault location uncertainty and coseismic displacement, which should be considered in fault mapping and fault displacement assessment.

Abstract

Nickel stable isotopes (δ60Ni) provide insight to Ni biogeochemistry in the modern and past oceans. Here, we present the first Pacific Ocean high-resolution dissolved Ni concentration and δ60Ni data, from the US GEOTRACES GP15 cruise. As in other ocean basins, increases in δ60Ni toward the surface ocean are observed across the entire transect, reflecting preferential biological uptake of light Ni isotopes, however the observed magnitude of fractionation is larger in the tropical Pacific than the North Pacific Subtropical Gyre. Such surface ocean fractionation by phytoplankton should accumulate isotopically lighter Ni in the deep Pacific, yet we find that North Pacific deep ocean δ60Ni is similar to previously reported values from the deep Atlantic. Finally, we find that seawater dissolved δ60Ni in regions with hydrothermal input can be either higher or lower than background deep ocean δ60Ni, depending on vent geochemistry and proximity.

Abstract

Increasing impacts of wildfires on Western US air quality highlights the need for forecasts of smoke emissions based on dynamic modeled wildfires. This work utilizes knowledge of weather, fuels, topography, and firefighting, combined with machine learning and other statistical methods, to generate 1- and 2-day forecasts of fire radiative energy (FRE). The models are trained on data covering 2019 and 2021 and evaluated on data for 2020. For the 1-day (2-day) forecasts, the random forest model shows the most skill, explaining 48% (25%) of the variance in observed daily FRE when trained on all available predictors compared to the 2% (<0%) of variance explained by persistence for the extreme fire year of 2020. The random forest model also shows improved skill in forecasting day-to-day increases and decreases in FRE, with 28% (39%) of observed increase (decrease) days predicted, and increase (decrease) days are identified with 62% (60%) accuracy. Error in the random forest increases with FRE, and the random forest tends toward persistence under severe fire weather. Sensitivity analysis shows that near-surface weather and the latest observed FRE contribute the most to the skill of the model. When the random forest model was trained on subsets of the training data produced by agencies (e.g., the Canadian or US Forest Services), comparable if not better performance was achieved (1-day R 2 = 0.39–0.48, 2-day R 2 = 0.13–0.34). FRE is used to compute emissions, so these results demonstrate potential for improved fire emissions forecasts for air quality models.

Abstract

The last deglaciation experienced the retreat of massive ice sheets and a transition from the cold Last Glacial Maximum to the warmer Holocene. Key simulation challenges for this period include the timing and extent of ice sheet decay and meltwater input into the oceans. Here, major uncertainties and forcing factors for the last deglaciation are evaluated. Two sets of transient simulations are performed based on the novel ice-sheet reconstruction PaleoMist and the more established GLAC1D. The simulations reveal that the proximity of the Atlantic meridional overturning circulation (AMOC) to a bifurcation point, where it can switch between on- and off-modes, is primarily determined by the interplay of greenhouse gas concentrations, orbital forcing and freshwater forcing. The PaleoMist simulation qualitatively replicates the Bølling-Allerød (BA)/Younger Dryas (YD) sequence: a warming in Greenland and Antarctica during the BA, followed by a cooling northern North Atlantic and an Antarctic warming during the YD.

Abstract

During the 3 month long eruption of Kı̄lauea volcano, Hawaii in 2018, the pre-existing summit caldera collapsed in over 60 quasi-periodic failure events. The last 40 of these events, which generated Mw > 5 very long period (VLP) earthquakes, had inter-event times between 0.8 and 2.2 days. These failure events offer a unique data set for testing methods for predicting earthquake recurrence based on locally recorded GPS, tilt, and seismicity data. In this work, we train a deep learning graph neural network (GNN) to predict the time-to-failure of the caldera collapse events using only a fraction of the data recorded at the start of each cycle. We find that the GNN generalizes to unseen data and can predict the time-to-failure to within a few hours using only 0.5 days of data, substantially improving upon a null model based only on inter-event statistics. Predictions improve with increasing input data length, and are most accurate when using high-SNR tilt-meter data. Applying the trained GNN to synthetic data with different magma-chamber pressure decay times predicts failure at a nearly constant stress threshold, revealing that the GNN is sensing the underling physics of caldera collapse. These findings demonstrate the predictability of caldera collapse sequences under well monitored conditions, and highlight the potential of machine learning methods for forecasting real world catastrophic events with limited training data.

Abstract

Increases in temperature and pressure caused by rapid burial of sediments seaward of the Sumatra subduction zone have been hypothesized to trigger dehydration reactions that diagenetically strengthen sediments and contribute to the formation of an over-pressured pre-décollement, which together facilitate the occurrence of large shallow earthquakes. We present paleomagnetic, rock magnetic, and electron microscopic analyses from drill cores collected offshore Sumatra at Site U1480 during IODP Expedition 362 that support this hypothesis. The older pre-fan units (Late Cretaceous to early Paleocene) were deposited when Site U1480 was moving rapidly northward with the Indian plate from a paleolatitude of 50° to 30°S, which would equate to expected absolute paleomagnetic inclinations of 70°–43°. Most of the older pre-fan sediments, however, have shallow observed inclinations (shallower than ±20°), indicating that the sediments were overprinted when Site U1480 was located near the paleoequator, as it has been since the early Oligocene. Electron microscopic observations reveal that the pre-existing detrital magnetite grains have undergone pervasive dissolution and alteration by hydrothermal fluids. The diagenesis observed is consistent with mineral dehydration, possibly driven by rapid burial of pelagic sediments by the ∼1250 m thick Nicobar Fan sequence. In addition, the elevated burial temperature also facilitated the smectite to illite conversion reaction. We hypothesize that chemical reactions resulted in the formation of fine-grained magnetite that records a chemical remanent magnetization overprint. This overprint is consistent with the alteration occurring after burial by the thick Nicobar Fan sequence sometime in the past few million years.

Abstract

Megathrust geometric properties exhibit some of the strongest correlations with maximum earthquake magnitude in global surveys of large subduction zone earthquakes, but the mechanisms through which fault geometry influences subduction earthquake cycle dynamics remain unresolved. Here, we develop 39 models of sequences of earthquakes and aseismic slip (SEAS) on variably-dipping planar and variably-curved nonplanar megathrusts using the volumetric, high-order accurate code tandem to account for fault curvature. We vary the dip, downdip curvature and width of the seismogenic zone to examine how slab geometry mechanically influences megathrust seismic cycles, including the size, variability, and interevent timing of earthquakes. Dip and curvature control characteristic slip styles primarily through their influence on seismogenic zone width: wider seismogenic zones allow shallowly-dipping megathrusts to host larger earthquakes than steeply-dipping ones. Under elevated pore pressure and less strongly velocity-weakening friction, all modeled fault geometries host uniform periodic ruptures. In contrast, shallowly-dipping and sharply-curved megathrusts host multi-period supercycles of slow-to-fast, small-to-large slip events under higher effective stresses and more strongly velocity-weakening friction. We discuss how subduction zones' maximum earthquake magnitudes may be primarily controlled by the dip and dimensions of the seismogenic zone, while second-order effects from structurally-derived mechanical heterogeneity modulate the recurrence frequency and timing of these events. Our results suggest that enhanced co- and interseismic strength and stress variability along the megathrust, such as induced near areas of high or heterogeneous fault curvature, limits how frequently large ruptures occur and may explain curved faults' tendency to host more frequent, smaller earthquakes than flat faults.