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Publication date: Available online 12 August 2025

Source: Advances in Space Research

Author(s): Penglin Wang, Fen Dang, Zekai Wang, Yifeng Xia, Yan Zhou, Cheng Zhou

Publication date: Available online 11 August 2025

Source: Advances in Space Research

Author(s): Su Zhou, Yongzhi Cai, Shunli Li, Zongxian Wu, Ying Hou

Publication date: Available online 11 August 2025

Source: Advances in Space Research

Author(s): Menghan Yin, Erming He, Yongzhi Li, Cong Zhang

Publication date: Available online 11 August 2025

Source: Advances in Space Research

Author(s): Junpeng Huang, Zizhao Zhang, Sixiang Ling, Kai Chen, Guangming Shi, Yanyang Zhang

Publication date: Available online 11 August 2025

Source: Advances in Space Research

Author(s): L.F. Chernogor

Publication date: Available online 7 August 2025

Source: Advances in Space Research

Author(s): Jungseon Lee, Jinah Lee, Chandeok Park, Hancheol Cho, Dongwon Jung

Publication date: Available online 7 August 2025

Source: Advances in Space Research

Author(s): Jiahe Yan, Haifei Zhu, Hanzhen Xiao

Publication date: Available online 7 August 2025

Source: Advances in Space Research

Author(s): Ifeoluwa Adawa, Yuichi Otsuka, Moataz Abdelwahab, Ayman Mahrous

Global sea-level change has now been measured by satellites for more than 30 years, and a comparison with climate projections from the mid-1990s shows that they were remarkably accurate, according to two Tulane University researchers whose findings were published in Earth's Future.

Phytoplankton—microscopic algae that form the base of ocean food webs—have long been viewed as transient players in the global carbon cycle: They bloom, die, and the carbon they contain is quickly recycled back into the ecosystem.

The power of a tornado can inflict tremendous damage on residential property, but the impact is also felt by nearby homeowners, even when their property is unscathed.

All the critical minerals the U.S. needs annually for energy, defense and technology applications are already being mined at existing U.S. facilities, according to a new analysis published in the journal Science.

Source: Global Biogeochemical Cycles

Throughout the first half of 2020, average monthly temperatures in Siberia reached 6°C above the norm. The situation climaxed on 20 June, when the temperature in the town of Verkhoyansk climbed to 38°C (100.4°F), the highest temperature ever recorded north of the Arctic Circle. With the extreme heat came wildfires, insect outbreaks, and thawing permafrost.

Now Kwon et al. suggest that the effects of the 2020 heat wave were still detectable the following year in the form of warmer- and wetter-than-usual soils.

The researchers obtained data on temperatures, precipitation, and other climatic factors from the European Centre for Medium-Range Weather Forecasts and incorporated them into a model of high-latitude ecosystems. To capture the effect of the 2020 Siberian heat wave, they replaced data from 2020 with data from each of the previous 5 years (2015 to 2019), which provided five estimates of what regional ecosystems might have looked like in 2021 had the heat wave not occurred.

The analysis indicated that the high heat caused soil temperature to remain roughly 1.2°C, or about 150%, warmer in 2021 than it would have been without the heat wave, even though air temperatures had returned to normal. The warmer temperatures also melted soil ice, resulting in wetter soil than usual. Root zone soil water availability, a measure of how much water soil can hold in the rooting depth of plants, increased by 10.9% in forests in 2021 and by 9.3% in grasslands. However, some of this meltwater left the soil via runoff.

In response to warmer, wetter soil, microbes proliferated and caused the soil ecosystem to emit more carbon dioxide than usual, the modeling indicated. In forests, this effect was largely offset by an increase in photosynthesis as plants flourished under the new conditions. In grasslands, on the other hand, photosynthesis initially increased during the heat wave event but then quickly decreased until 2021 as plants used up the available water and died off. As a result of the 2020 heat wave, the researchers reported, forests gained an additional 6 grams of carbon per square meter in the first half of 2021, whereas grasslands lost 10.9 grams of carbon per square meter. (Global Biogeochemical Cycles, https://doi.org/10.1029/2025GB008607, 2025)

—Saima May Sidik (@saimamay.bsky.social), Science Writer

Citation: Sidik, S. M. (2025), In the Arctic, consequences of heat waves linger, Eos, 106, https://doi.org/10.1029/2025EO250313. Published on 22 August 2025. Text © 2025. AGU. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.

SummaryThere are two fundamental probabilities in the seismic phase picking process – the probability of the existence of a seismic phase (detection probability) and the probability associated with the phase arrival time estimation (timing probability). The nearly ubiquitous approach in developing deep learning phase picking models is to use a kernel, such as a truncated Gaussian, to mask the labeled phase arrival time and train a segmentation model. Once a model is trained, the times of the peaks in the output are taken as phase arrival times (picks), and the height of the peaks are taken as “probability” of the picks. Here, we show that this “probability” represents neither the detection nor the timing probability because this approach forces the output to follow the shape of the kernel. We introduce an approach using two models to estimate these two distinct probabilities. We use a binary classifier with a calibrated confidence to address the detection probability and a multi-class classifier to obtain a probability mass function to address the timing probability. This new approach can make the deep learning-based phase picking process more interpretable and provide options to logically control seismic monitoring workflows.

SummaryThis study introduces a new method for calculating acoustic-gravity waves in a spherically layered atmosphere. The method introduces a model assumption and divides the atmosphere into finely stratified layers to solve the PDE with respect to the radial coordinate. The time-domain synthetic signal is obtained by summing over the orders of the associated Legendre functions and then applying the FFT. The method is applied to numerically simulate wave behaviour, including Earth curvature effects, and compares with the horizontally layered model (HLM). Results show that at near-field distances, our method aligns closely with HLM, but significant differences emerge in the far field, particularly beyond an epicentral distance of 50°, where Earth curvature becomes critical. Our method successfully simulates head waves of seismic phases, and Rayleigh waves, even for waves travelling multiple times around the Earth, which HLM cannot achieve. Simulations using a homogeneous Earth model reveal head wave characteristics consistent with previous studies, with the strongest energy observed in Rayleigh head waves. The application of the AK135 Earth model highlights the visibility of seismic phases through the Earth’s core. We validate our method by comparing synthetic records with actual data from the 1999 Chi-Chi earthquake. The synthetic records show good agreement with observed seismic signals and ionospheric perturbations in terms of arrival time and wave envelope. These results demonstrate the accuracy of our method in simulating acoustic-gravity waves at large epicentral distances.

SummaryThe widespread, multi-year crustal deformation induced by megathrust earthquakes (Mw8+) is primarily controlled by the combined effects of continuous aseismic slip on the fault plane (afterslip) and viscoelastic relaxation driven by coseismic stress perturbations in the upper mantle. However, till today it remains a considerable challenge to separate these two mechanisms in geodetic observations. We derived the first 3-year GNSS observations following the 2021 Chignik Mw8.2 earthquake to investigate the mechanisms of postseismic deformation. We established a model capable of simultaneously simulating afterslip and viscoelastic relaxation, and constrained the upper mantle rheology beneath the Alaska Peninsula. The best-fit model effectively reproduces the GNSS observations and reveals a notable viscosity difference between the mantle wedge and the oceanic asthenosphere, with steady-state viscosities of $3 \times {{10}^{18}}$ Pa s and $4 \times {{10}^{19}}$ Pa s, respectively. The inferred mantle wedge viscosity beneath the Alaska Peninsula is lower than the values reported for south-central and southeastern Alaska, suggesting an eastward increase in viscosity along the subduction zone. Two main patches of afterslip are identified during the first 3 years. The patch of up-dip afterslip overlaps with the 1938 Chignik Mw8.3 earthquake rupture zone, and demonstrates a close spatial correlation with the slow slip event in 2018. The above new results enhance our insights into the spatial variability of regional rheology and slip behavior along the Alaska-Aleutian subduction zone.

A team of Johns Hopkins researchers is using an innovative X-ray imaging approach to reveal how compression reshapes the tiny spaces and stresses within sandstone—findings that could predict how this common rock used for fuel reservoirs behaves under deep subterranean pressure. The results appear in the Journal of Geophysical Research: Solid Earth.

New research from Purdue University reveals how moisture influences atmospheric blocking, a phenomenon that often drives heat waves, droughts, cold outbreaks and floods, helping solve a mystery in climate science and improving future extreme weather predictions.

Last summer, Stephanie McNamara got her first glimpse of Great Sand Dunes National Park and Preserve in southern Colorado. The park is a monument to sand, where dunes stretch across 30 square miles and tower nearly 750 feet high, making them the tallest such formations in North America.

A research team has developed a new artificial intelligence (AI) tool that can automatically identify and track tropical easterly waves (TEWs)—clusters of clouds and wind that often develop into hurricanes—and separate them from two major tropical wind patterns: the Intertropical Convergence Zone (ITCZ) and the monsoon trough (MT).