Feed aggregator

Snow algae accelerate Antarctic ice shelf melting, research discovers

Phys.org: Earth science - Wed, 08/27/2025 - 12:42

A new study has revealed that tiny organisms called snow algae are significantly contributing to the surface melting on Antarctic ice shelves. The discovery could have far-reaching implications for global sea level rise.

New AI approach sharpens picture of carbon export in the Southern Ocean

Phys.org: Earth science - Wed, 08/27/2025 - 12:23

The Southern Ocean plays an important role in global climate and carbon cycling. Understanding carbon export in this region is critical for modeling Earth's changing climate and evaluating potential ocean-based climate interventions.

Equatorial Deep Ocean Response to the Madden-Julian Oscillation

EOS - Wed, 08/27/2025 - 12:00

Editors’ Highlights are summaries of recent papers by AGU’s journal editors. Source: Journal of Geophysical Research: Oceans

The Madden-Julian Oscillation (MJO) is the dominant weather system in the tropics. It lasts several weeks and changes rainfall, cloudiness, and winds across the tropics. The MJO is well known for triggering an extratropical and global atmospheric circulation response. And recently, several case studies have been conducted on a deeper ocean response to the MJO.

Using 18 years of output from a high-resolution ocean reanalysis product (GLORYS12) largely constrained by Argo data, Robbins et al. [2025] discover intraseasonal anomalies (20-200 days) signals in currents, temperature, and salinity in the tropical oceans down to at least 2,000 meters. They describe that such deep-penetrating structure are equatorial Kelvin waves, which are forced by the MJO in the equatorial Pacific and Indian Oceans. This is one of the first studies to examine the impact of the MJO on the deep ocean and will be beneficial for future investigations into deep-ocean changes.

Citation: Robbins, C., Matthews, A. J., Hall, R. A., Webber, B. G. M., & Heywood, K. J. (2025). The equatorial deep ocean structure associated with the Madden-Julian Oscillation from an ocean reanalysis. Journal of Geophysical Research: Oceans, 130, e2025JC022457. https://doi.org/10.1029/2025JC022457

—Xin Wang, Editor, JGR: Oceans

Text © 2025. The authors. 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.

Fatal landslides in June 2025

EOS - Wed, 08/27/2025 - 07:43

In June 2025, I recorded 51 fatal landslides worldwide, resulting in 479 fatalities. The number of fatal landslides is significantly above the long term mean.

Yesterday, I provided an update on fatal landslides that occurred in May 2025. This post is a follow-up, providing the data for June.

As always, allow me to remind you that this is a dataset on landslides that cause loss of life, following the methodology of Froude and Petley (2018). At this point, the monthly data is provisional.

The headline is that I recorded 51 landslides over the course of the month, claiming 479 lives. Note that the landslide total is lower than for May (n=66), which is a little unusual. However, 51 landslides is still substantially higher than the 2004-2016 mean (n=40.8), whilst the number of fatalities is also below the mean (n=746).

So, this is the monthly total graph to the end of June 2025:-

The number of fatal landslides to the end of June 2025 by month.

Plotting the data by pentad to the end of pentad 36 (29 June), the trend looks like this (with the exceptional year of 2024 plus the 2004-2016 mean for comparison):-

The number of fatal landslides to 29 June 2025, displayed in pentads. For comparison, the long term mean (2004 to 2016) and the exceptional year of 2024 are also shown.

Through to about 10 June, the trend for 2025 very closely matched that of 2024. However, by the end of the month a significant difference had emerged, with the landslide rate this year being somewhat lower. The data for July and August will start to tell us whether this is a trend.

So, what lies behind a monthly figure that is above the long term average but below the exceptional year for 2024? The Copernicus surface air temperature data for June 2025 notes the following:-

“June 2025 was 0.47°C warmer than the 1991-2020 average for June with an absolute surface air temperature of 16.46°C. [It was the] third-warmest June on record, 0.20°C cooler than the warmest June in 2024, and 0.06°C cooler than 2023, the second warmest.”

Thus, if the hypothesis that the landslide numbers are driven in part by atmospheric temperature, the lower total than in 2024 is perhaps unsurprising.

Reference

Froude M.J. and Petley D.N. 2018. Global fatal landslide occurrence from 2004 to 2016. Natural Hazards and Earth System Science 18, 2161-2181. https://doi.org/10.5194/nhess-18-2161-2018

Return to The Landslide Blog homepage Text © 2023. The authors. 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.

Stepwise Iterative Enhanced De-striping of GRACE/GRACE-FO Data for Improving Global Water Mass Estimation

Geophysical Journal International - Wed, 08/27/2025 - 00:00

SummaryThe time-variable gravity field obtained from the Gravity Recovery and Climate Experiment/Follow-On (GRACE/GRACE-FO) satellites has been successfully used to detect global water mass changes over the past two decades. However, the north-south striping noise in the GRACE spherical harmonic (SH) solution limits their effectiveness. Efforts to suppress this noise and achieve a higher signal-to-noise ratio (SNR) continue with various product releases, but there is still a great need for improvement. This study presents a new de-striping method called GBVMD, which employs a stepwise enhancing framework combining Gaussian filtering with bi-dimensional variational mode decomposition (BVMD). The methodological breakthrough comes from two innovations: First, it employs adaptive scale decomposition by dynamically adjusting the radius of the Gaussian filter in conjunction with BVMD reconstruction, effectively reducing noise across multiple scales. Second, it features a dual-decision optimization strategy that integrates SNR-driven mode reconstruction and iterative termination, thereby maximizing the SNR while adapting to the specific characteristics of the noise. In simulations, the GBVMD outperforms the five other filters in reducing noise and keeping signals, achieving an improvement in SNR by at least 19%, and reductions in root mean square error and mean absolute error by at least 14% and 11%, respectively. When applied to GRACE/GRACE-FO Level-2 SH solutions, GBVMD led to a higher SNR with an improvement of at least 12% compared to other filters. The GBVMD-filtered SH data showed strong consistency with three Level-3 Mascon solutions across 183 river basins. Comparable results were also found in polar regions, validated by altimetry data. Furthermore, we effectively corrected the leakage errors for two examples in the Caspian Sea and the Great Lakes, demonstrating the advantages of GBVMD-filtered SH over the Mascons for signal reanalysis. We recommend GBVMD for further applications, especially in specific regions such as ocean areas and other satellite missions requiring similar de-striping approaches.

Anomaly Detection Algorithm of Single Variable Time Series Data Based on Dynamic Parameterization for Subsurface Fluid Data Anomaly Detection

Geophysical Journal International - Wed, 08/27/2025 - 00:00

SummaryDue to the extremely destructive characteristics of seismic hazards, and as one of the effective pre-seismic physical signals, abnormal changes in subsurface fluids can provide key precursor information for earthquake prediction. Furthermore, an efficient method for labelling anomalies in seismic monitoring data is urgently needed. Therefore, this paper analyses the change characteristics of subsurface fluid-water level data and proposes an Anomaly Detection Algorithm of Single Variable Time Series Data Based on Dynamic Parameter Tuning (ADSV-DPT) based on three important characteristics (jump, step and steep), which firstly determines the central tendency of the data by calculating the median of the water level data within the initial window, and then utilizes the Median Absolute Deviation (MAD) as a robust dispersion metric to reduce the impact of extreme values on the anomaly detection. The sliding window mechanism is employed to update the median and MAD step by step, thereby ensuring the efficiency and adaptability of the algorithm in processing time series data. Finally, the anomalies in the data are detected by setting dynamic thresholds. A comparison of the anomaly detection efficacy of the proposed ADSV-DPT algorithm with that of three alternative models (namely, K-Nearest Neighbor, KNN; Pruned Exact Linear Time, PELT; and One-Class Support Vector Machine, OC-SVM) was conducted. The experimental results demonstrate that the ADSV-DPT algorithm outperforms the other models in accurately identifying anomalous features. The average precision, recall, and F1-score of the ADSV-DPT algorithm all exceed 85%. The algorithm's capacity for adapting to variations in the data is noteworthy, as is its ability to accurately identify abnormal values that deviate from the established normal range.

Study projects increases in lightning, wildfire risk for the U.S. Northwest

Phys.org: Earth science - Tue, 08/26/2025 - 20:22

The Northwest can expect a widespread increase in days with cloud-to-ground lightning in the years to come, along with heightened wildfire risk, according to projections made with a unique machine-learning approach developed at Washington State University.

Wind isn't the only threat: Scientists urge shift to more informed hurricane scale

Phys.org: Earth science - Tue, 08/26/2025 - 20:13

Wind alone does not account for all hurricane-related fatalities. Storm surge and rainfall do as well. Yet the current warning system—the Saffir-Simpson Hurricane Wind Scale—measures a storm's strength solely by wind speed.