The discovery of an unexpectedly hot rock beneath Earth's surface is challenging scientists' understanding of how heat is distributed deep inside the planet, with potential implications for how they understand earthquakes, volcanic activity and the movement of carbon through Earth.
When scientists discovered that increasing the ocean's natural alkalinity could enhance seawater's capacity to absorb and store carbon dioxide (CO₂), the idea quickly gained attention as a potential geoengineering solution to climate change. Early visions suggested it could remove gigatonnes of CO₂ from the atmosphere each year. But the reality is far more complex.
Research published today in Nature by University of Melbourne researchers, in collaboration with Gunaikurnai Land and Waters Corporation, reconstructed the past 1,000 years of vegetation, fire and erosion in Croajingolong National Park in East Gippsland, one of southeastern Australia's most fire-prone forests.
Our oceans are becoming more acidic, which could have major consequences for marine life. Of particular concern is what this could mean for phytoplankton, the microscopic photosynthetic organisms that form the basis of marine food webs and produce roughly half of the oxygen on Earth. Now, a study published in Nature Geoscience has found that the carbon dioxide we pump into the atmosphere is changing their internal chemistry.
Off the coast of East Antarctica, researchers led by Kiel University (CAU) and the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI), have investigated an exceptionally large sedimentary body embedded in the seafloor of Vincennes Bay: a so-called "grounding-zone wedge"—symmetrical, wedge-shaped, approximately 260 meters (853 feet) high and 65 kilometers (40 miles) long. With a volume of more than 580 cubic kilometers (139 cubic miles), it is the largest known isolated grounding-zone wedge on a glaciated continental shelf to date and constitutes an exceptional archive of East Antarctica's glacial history.
A hidden 'uranium hotspot' has been discovered beneath Aberdeen by University of Aberdeen researchers—shedding new light on the geological history of the region.
Penguin poop is fertilizing snow algae blooms along the Antarctic Peninsula, feeding dense green algae patches along the coastline, a new study shows. These expanding stretches of green snow melt faster than clean, white snow, potentially speeding up the spread of snow- and ice-free areas of Antarctica.
During the 2023–2024 El Niño, about 75% of the world's tropical forest areas experienced at least six months of unprecedented simultaneous drought and heat. According to a study published in Earth's Future, this scenario offers more than just a snapshot of an extreme event; it offers a preview of the "new normal" for the next two decades if greenhouse gas emissions are not reduced.
Ozone pollution caused by Amazon wildfires is significantly reducing the rainforest's ability to absorb carbon dioxide from the atmosphere, new research reveals. Ground-level ozone is formed when pollutants from sources such as wildfires react in the presence of sunlight. This harms human health and interferes with plants' ability to absorb carbon dioxide (CO2) from the atmosphere.
A new study shows that the world's oyster reefs are a precious resource that's vastly underestimated and underutilized. The authors set out to calculate the reefs' potential gross ecosystem product (GEP), the ecological counterpart of the gross economic product, and arrived at trillions of dollars in ecosystem goods and services delivered each year to different aspects of planetary health, including human well-being. But for their full potential to be realized, oyster reefs around the world will need to be restored and protected.
SummaryAccurate determination of rate-and-state friction parameters from experiments is crucial for physics-based modeling of the earthquake source. In this study, we develop a Bayesian inversion framework based on a spring-slider model of the experiment to infer rate-and-state frictional properties from stable velocity-step experiments of a fine-grained quartz gouge that is close to instability. The inversion framework infers frictional parameters with superior precision to conventional methods based on non-linear least squares. We identify that logarithmic sampling of the characteristic slip DRS is an essential step for accurate inversion. Testing the same gouge material under the same effective normal stress but different pore pressure additionally shows that the presence of pressurized pore fluids changes the inferred rate-and-state properties of the gouge material by increasing rate-and-state parameters a, b, and DRS. The Bayesian inversion framework provides a valuable tool for inferring accurately rate-and-state parameters of realistic gouge materials close to instability, with a small and quantifiable range of uncertainty in cases where the steady-state behavior quantified by a − b can be constrained independently. We also find that the steady-state friction properties evolve over the course of the experiment, adding additional uncertainty to the rate-and-state parameter estimates but not altering the conclusion about significant differences between the rate-and-state parameters in the presence of fluids.
SummaryEvaluating earthquake forecasts is a crucial step in understanding and improving the capabilities of forecasting models. The use of specific metrics to assess the consistency between forecasts and data on one particular aspect of the process is important to understand which aspects of seismicity a model is failing to describe, and, consequently, highlight where new versions of the model should improve. This can be done effectively only by metrics unaffected by inconsistencies in other aspects of the process. The Collaboratory for the Study of Earthquake Predictability (CSEP), which organises earthquake forecasting experiments around the globe, has developed different tests targeting different aspects of the process, such as the number N-test or the magnitude M-test, assessing (in)consistency between the observed and forecasted number and magnitude distributions of events, respectively. We find that the results of the recently proposed M-test for catalog-based forecasts (composed of a collection of synthetic catalogs from the model) depend on the N-test, i.e. the two tests do not isolate the desired aspects appropriately, rendering uninformative forecast results. Here, we address this problem using simulated data and provide a solution based on resampling of simulated catalogs. We implement this new M-test with resampling in the pyCSEP software toolkit, conduct two analyses using actual earthquake forecasts for Europe (1990-2015) and Switzerland (1933-1962, 1962-1992, 1993-2022) that provide inconsistent earthquake counts compared to observations, and analyse how the test results change using this proposed test. Lastly, we investigate alternative metrics, namely an unnormalised M-test, two Chi-square formulations, the Hellinger distance, the Brier score, and a novel Multinomial Log-Likelihood (MLL) score, and compare them based on the ability to find (in)consistency between data and forecast in various synthetic scenarios. We find that there are scenarios in which the alternative metrics outperform the resampled M-test. In particular, the MLL test outperforms the M-test in all the scenarios considered. We also study how the ability of finding inconsistency changes with the number of observations, and the cutoff magnitude, comparing one of the Chi-square metrics, the MLL, and the M-test against classical statistical methods such as the Kolmogorov-Smirnoff (KS) test, the Wilcoxon test, and the Anderson-Darling (AD) test. The MLL and the AD test are the ones providing the highest probability of finding inconsistencies for all combinations of number of observations and cutoff magnitude. This study shows how realistic synthetic examples can be used to compare the ability of different metrics in finding inconsistencies between data and forecasts, and shows that the MLL is the metric providing the best tradeoff between interpretability and probability of finding inconsistencies and, therefore, should be used.
SummaryFull waveform inversion (FWI) has significant potential to reconstruct the subsurface structure at high resolution, but it is a nonlinear and ill-posed problem. Traditional physics-based FWI methods that employ gradient-based optimization algorithms to minimize the discrepancy between observed and synthetic data face several challenges. The aforementioned methods are susceptible to becoming trapped in local minima as a result of cycle-skipping problems and require substantial computational resources, particularly with the increasing size and required resolution of seismic datasets. Nowadays many deep-learning methods are proposed to solve the above challenges. FWIGAN, a recently proposed unsupervised seismic velocity inversion method based on the Wasserstein generative adversarial network with gradient penalty (WGAN-GP), has demonstrated certain success in overcoming these challenges. However, FWIGAN enforces the Lipschitz constraint on the critic through a gradient penalty evaluated at dynamically sampled interpolation points between the observed and synthetic data. This strategy introduces additional computational and hyperparameter tuning costs. Motivated by these limitations, we introduce the spectral normalization into the FWIGAN framework, directly constraining the spectral norm of each network layer to control the Lipschitz behavior of the critic. It can enhance the stability of network training. The proposed method mitigates the dependence on having large labeled data and the pretraining of the network. The new scheme is applied to the Marmousi and Sigsbee models to verify its robust performance. The experimental results clearly indicate that our method outperforms common methods in terms of inversion accuracy.
SummaryPolar motion (PM) provides an independent, observation-based constraint on global mass redistribution, but interpretation of its long-term variability is primarily limited by uncertainty in the glacial isostatic adjustment (GIA) contribution, for which conventional model-based estimates exhibit substantial differences. Here we develop a refined approach that uses primarily GRACE and GRACE Follow-On (GRACE-FO) observations to estimate the secular PM associated with non-surface-loading processes, predominantly GIA. We restore the pole tide contribution removed during standard GRACE processing to reconstruct the total degree-2, order-1 spherical harmonic coefficients and the corresponding PM excitation, then isolate the non-surface-loading component as the difference between this excitation and the GRACE/GRACE-FO-derived excitation from contemporary surface loads and pole tides. This strategy avoids uncertainties associated with atmospheric winds, ocean currents, and core–mantle processes present in geodetic PM records. The resulting secular trends are 1.77 mas/yr in χ1 and − 2.49 mas/yr in χ2, differing markedly from commonly used GIA model predictions (e.g. 1.01 and − 4.67 mas/yr from Peltier et al., 2018). We validate our estimate by reconstructing the PM excitation for 1992–2016 using a comprehensive set of cryospheric, hydrologic, atmospheric, and oceanic contributions together with our inferred secular term; the reconstruction agrees closely with geodetic observations. Compared with the previous PM-based approach of Seo et al. (2021), our method yields a substantially smaller 95 per cent confidence region, providing an improved characterization of the long-term secular contribution to PM and a stronger foundation for using multi-decadal PM observations to diagnose twentieth-century ice mass and terrestrial water storage changes.
"Is there enough water? What's happening with climate change? How do I operate my hydro plant? In a world of water problems, there are problems that we need a good model to solve," says Colin Gleason, a hydrologist in the Riccio College of Engineering at the University of Massachusetts Amherst. But the models are only as good as the math that they're based on, and short of measuring every river in the world, there is no way to check this on a global scale.
A flash flood can devastate a community for years in a matter of minutes. These disasters account for roughly 85% of floods and cause thousands of deaths annually worldwide, with water levels capable of shooting up 30 feet (9 meters) and receding to normal levels in the span of a single, unpredictable day, according to the National Weather Service.
Publication date: Available online 11 September 2026
Source: Earth and Planetary Science Letters
Author(s): Fabrice Gaillard, Yves Marrocchi, Gregory Rogerie, Mohamed A. Bouhifd, Camille Bernard, Mathieu Roskosz
In January 2013, Lake Michigan reached its lowest water level on record. Seven years later, in the summer of 2020, it broke the opposite record with widespread flooding, eroded shorelines and closed lakeside roads.
El Niño and warming in the tropical Atlantic could help provide advance warning of extreme heat in the Amazon, with some signals appearing as much as seven months earlier, new research has found. Researchers from the ARC Center of Excellence for the Weather of the 21st Century and the National Institute of Space Research in Brazil studied how major patterns of natural climate variability influence temperature and rainfall extremes across the Amazon Basin.
A better understanding of how earthquakes occur and what happens during a rupture is important for assessing seismic risk. Small earthquakes, which are more frequent than large ones, are a tempting data set, but they're hard to study, in large part because their energy is quickly sapped as it travels through Earth.
