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Blowing from the north, winds emerge as key driver of Antarctic ice loss

Phys.org: Earth science - Wed, 09/10/2025 - 15:00

Most of Earth's fresh water is locked in the ice that covers Antarctica. As the ocean and atmosphere grow warmer, that ice is melting at a startling pace with sea levels and global currents changing in response. To understand the potential implications, researchers need to know just how fast the ice is disappearing, and what is driving it back.

Groundwater modeling tool helps rural Colorado community make informed irrigation and water management decisions

Phys.org: Earth science - Wed, 09/10/2025 - 14:30

Farmers near Meeker, in northwestern Colorado, have been diverting water from the White River to flood their fields for irrigation for more than 100 years.

The role of nanoscale crystals in volcanic eruptions

Phys.org: Earth science - Wed, 09/10/2025 - 14:20

Researchers at Diamond Light Source have used advanced imaging to look at microscopic crystals, called nanolites, to see what they can tell us about volcanic eruptions.

When Is a Climate Model “Good Enough”?

EOS - Wed, 09/10/2025 - 12:59

Source: Earth’s Future

Global climate models are software behemoths, often containing more than a million lines of code.

Inevitably, such complex models will contain mistakes, or “bugs.” But because model outputs are widely used to inform climate policy, it’s important that they generate trustworthy results.

Proske and Melsen set out to understand how climate modelers think about, identify, and address bugs. They interviewed 11 scientists and scientific programmers from the Max-Planck-Institut für Meteorologie who work on the ICON climate model.

When new code is developed for ICON, it’s screened and tested to catch bugs before being integrated into the model itself, the interviewees said.

After code is integrated, however, such testing usually stops. The code is assumed to be bug free until the model behaves weirdly or a programmer serendipitously discovers a bug while examining the code for other reasons. Even when the model crashes, it’s not necessarily a sign that a bug needs to be fixed because researchers are always making trade-offs between the speed and the stability of the model, and sometimes they simply push the model outside the bounds of what it can handle given those constraints.

Tracking down bugs and fixing them can be time-consuming, so even if the team suspects the presence of a bug, they sometimes estimate its impact to be minor enough that it doesn’t warrant correction. When the researchers do decide to fix a bug, many view the process as an extension of climate science: They generate hypotheses about how the bug might cause the model to behave, then test those hypotheses to discern the exact nature of the bug and how to address it.

The best way to avoid bugs is to test code thoroughly before it’s integrated into the full model, many interviewees said. Tools exist to facilitate testing, such as Buildbot and the GitLab development platform, and the scientists said such tools could be leveraged more fully in ICON’s development process. However, they also said there are inherent limits to how thoroughly researchers can test climate models because researchers don’t always know what a 100% accurate model output would look like. Thus, they do not have that basis to which they can compare actual model output.

Though the interviewees acknowledged that ICON is imperfect, they also considered it to be “good enough” to forecast weather or to answer research questions such as how increased atmospheric carbon will affect global temperatures. The authors write that although “the principle of ‘good enoughness’” is pragmatic and understandable, it could also lead to misunderstandings if users don’t appreciate a model’s limits. (Earth’s Future, https://doi.org/10.1029/2025EF006318, 2025)

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

Citation: Sidik, S. M. (2025), When is a climate model “good enough”?, Eos, 106, https://doi.org/10.1029/2025EO250332. Published on 10 September 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.

Extreme Heat in U.S. Cities Revealed at High Resolution

EOS - Wed, 09/10/2025 - 12:57

Source: GeoHealth

Recent heat waves in the United States underscore a growing public health threat: Extreme heat events are growing longer, hotter, and more frequent. Soaring temperatures raise the risk of various health problems, such as heat stroke and cardiovascular disease—particularly for older people, people with preexisting conditions, and people who work outdoors.

Understanding these risks, and how to handle them, requires epidemiological research on heat exposure in cities, where most U.S. residents live. However, scientific instruments for measuring urban temperatures are often located at airports, rather than in city centers, where temperatures are typically higher than in surrounding rural regions. Thus, these tools often do not adequately capture the so-called urban heat island effect.

A novel method for measuring heat exposure, created by Marquès and Messier, can pinpoint urban heat islands that previously went undetected. The researchers’ approach harnesses crowdsourced data from the thousands of personal weather stations already installed by residents seeking precise weather information.

The new method employs a statistical technique known as Bayesian hierarchical modeling, which helps account for uncertainty in the crowdsourced temperature data. To demonstrate its capabilities, the researchers applied the method to four urban areas with distinct climates and geography: New York City, Philadelphia, Phoenix, and North Carolina’s “Triangle,” which includes Raleigh, Durham, and Chapel Hill.

Compared with existing tools, the new method captured urban air temperatures at much higher resolution. It identified urban heat islands that were previously detected imprecisely or not at all, such as hot spots clustered in Philadelphia. In addition, it recognized the cooling effects of urban green spaces, such as New York’s Central Park. It performed well at both high and low temperatures, including during Phoenix’s hottest month on record (July 2023) and a cold blizzard event in Philadelphia and New York in January 2021. The new method also revealed that compared with other areas in the same city, more densely populated neighborhoods were more likely to experience hot temperatures and longer hot nights.

The researchers have made their method publicly available in the hope that it will aid research into the health impacts of heat. This work could also help inform public health initiatives to support communities facing extreme heat, they say. (GeoHealth, https://doi.org/10.1029/2025GH001451, 2025).

—Sarah Stanley, Science Writer

Citation: Stanley, S. (2025), Extreme heat in U.S. cities revealed at high resolution, Eos, 106, https://doi.org/10.1029/2025EO250296. Published on 10 September 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.

Far from West Coast, team tracks California quakes

Phys.org: Earth science - Wed, 09/10/2025 - 08:22

The University of Texas at Arlington is far from California earthquake country, yet its researchers are helping pinpoint which sections of the San Andreas Fault are most active.

Imprints of time-dependent microseism source distributions on the coda of correlations

Geophysical Journal International - Wed, 09/10/2025 - 00:00

SummarySeismic interferometry, applied to continuous seismic records, yields correlation wavefields that can be exploited for information about Earth’s subsurface. The coda of the correlation wavefield has been described as multiply scattered waves that are highly sensitive to crustal heterogeneity and its changes. Therefore, the coda of consecutive correlation wavefields allows to monitor velocity variations over time to detect weak changes in the medium at depth. Ocean microseisms, generated by ocean-land interactions, are the dominant continuous source of seismic energy at frequencies below 0.5 Hz. It is well-understood that these oceanic sources are not homogeneously distributed over Earth and change over the seasons, which commonly results in asymmetric correlation wavefields from seismic data. The impact of these seasonal changes on the coda of the correlation wavefield is typically considered negligible. In contrast, we demonstrate that oceanic noise sources and their changes directly impact the composition of the coda. We compute correlation wavefields between several master stations throughout Europe and the Gräfenberg array in Germany. We beamform these correlation wavefields, in the microseism frequency band, to detect coherent waves arriving at the Gräfenberg array. We perform this analysis for a two-year period, which enables us to compare variations in source direction over the seasons. We find seismic waves arriving from dominant sources to the North-Northwest of Gräfenberg in boreal winter (with slownesses corresponding to surface waves) and towards the South in summer (with slownesses corresponding to body waves) throughout the entire correlation wavefield, including its late coda. Beamforming the original recordings before cross-correlation confirms that the seasonally dominant source regions are directly detected also in the correlation wavefield coda. We derive that seismic waves propagating from isolated microseism source regions will be present in correlation wavefields even if the master station, or ”virtual source”, used for correlation recorded no physical signal at all. The findings we present raise concerns about velocity monitoring approaches relying on the coda being comprised exclusively of scattered waves. Our results also suggest that higher-order correlations do not achieve an effectively more homogeneous source distribution, and instead may even enhance such bias.

Dual-Layer Gradient-Boosted Equivalent Sources for Magnetic Data

Geophysical Journal International - Wed, 09/10/2025 - 00:00

SummaryMagnetic data often require interpolation onto a regular grid at constant height before further analysis. A widely used approach for this is the equivalent sources technique, which has been adapted over time to improve its computational efficiency and accuracy of the predictions. However, many of these adaptations still face challenges, including border effects in the predictions or reliance on a stabilising parameter. To address these limitations, we introduce dual-layer gradient-boosted equivalent sources to: (1) use a dual-layer approach to improve the predictions of both short- and long-wavelength signals, as well as, reduce border effect; (2) use block-averaging and the gradient-boosted equivalent sources method to reduce the computational load; (3) apply block K-fold cross-validation to guide optimal parameter selection for the model. The proposed method was tested on both synthetic datasets and the ICEGRAV aeromagnetic dataset to evaluate the methods ability to interpolate and upward continue onto a regular grid, as well as predict the amplitude of the anomalous field from total-field anomaly data. The dual-layer approach proved better compared to the single-layer approach when predicting both short- and long-wavelength signals, particularly in the presence of truncated long-wavelength anomalies. The use of block-averaging and the gradient-boosting method enhances the computational efficiency, being able to grid over 400,000 data points in under 2 minutes on a moderate workstation computer.

Smallholder Farmers Face Risks in China’s Push for Modern Agriculture

EOS - Tue, 09/09/2025 - 20:37

Editors’ Highlights are summaries of recent papers by AGU’s journal editors. Source: Community Science

In China, efforts to modernize agriculture through large-scale farming have pushed many smallholder farmers—who produce most of the country’s food—to the margins. One promising solution is “circular agriculture,” which focuses on sustainability, productivity, and rural economic development by encouraging cooperation between large- and small-scale farming operations.

In Community Science’s special collection on Transdisciplinary Collaboration for Sustainable Agriculture, Li and Nielsen [2025] examine a circular agriculture project in southwest China that combines pomelo growing with pig breeding. The authors conducted 35 interviews with smallholder farmers, government officials, employees from financial institutions, and various other stakeholders, capturing a wide range of interests and risks faced in this model.

Their findings show that local governments play a key role in creating platforms for cooperation, while agricultural cooperatives are central to business management. The study also reveals the challenge that government involvement is often politically motivated, and smallholders can lose both autonomy and fair representation in decision-making. The authors suggest that for circular agriculture to truly benefit everyone, smallholders need both a voice and power in shaping their future—without having their interests exploited.

Citation: Li, H., & Nielsen, J. Ø. (2025). Smallholders, capital, and circular agriculture—The case of combined pomelo and pig farming in southwest China. Community Science, 4, e2025CSJ000127. https://doi.org/10.1029/2025CSJ000127

—Claire Beveridge, Editor, Community Science

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.