Accurate wildfire forecasting can inform regional management and mitigation strategies in advance of fire occurrence. Existing systems typically use fire danger indices to predict landscape flammability, based on meteorological forecasts alone, often using little or no direct information on land surface or vegetation state. Here, we use a vegetation characteristic model, weather forecasts and a data-driven machine learning approach to construct a global daily ~9 km resolution Probability of Fire (PoF) model operating at multiple lead times. The PoF model outperforms existing indices, providing accurate forecasts of fire activity up to 10 days in advance, and in some cases up to 30 days. The model can also be used to investigate historical shifts in regional fire patterns. Furthermore, the underlying data driven approach allows PoF to be used for fire attribution, isolating key variables for specific fire events or for looking at the relationships between variables and fire occurrence.

Large earthquakes, especially those occurring in a city or population centers, create devastation and havoc, and often times kindle several deaths and injuries, and significant infrastructure damage that lead to several billions of dollars in losses. Marine earthquakes are the leading cause of large tsunamis which cause deaths, destruction, displacement of population, and a possible nuclear meltdown. Thus, prediction of earthquake or its aftershocks or earthquake early warning system has a great potential to mitigate the loss of life as well as different kinds of damage. Earthquake prediction would mean forecasting the occurrence of an earthquake by providing both its magnitude estimate and accurate location. Earthquake prediction has been an important area of seismology research for quite a while, and it looks like it will continue to be an important area of research. Recently, with the implementation of deep learning in seismology, scientists have been able to detect, predict, and model seismic waves and earthquake aftershocks. Earthquake aftershocks are generally triggered by changes in stress formed by large earthquakes that happen within, or surrounding a given fault network system. The main goal of this study is to investigate the improvement of aftershock pattern predictions with the implementation of tuning and optimizing of deep learning parameters. To achieve these goals, we have developed an algorithm that can help first gather mainshock-aftershock sequence data. Some of the criteria used in identifying earthquakes that initiate an aftershock is to look at earthquakes that happen within a certain radius, the values we attempted are within about 0.5 degrees range, and within a certain period, from few seconds to several weeks of the occurrence of the main shock. For the sequence identification, we have been using seismic data from the United States Geological Survey (USGS)-National Earthquake Information Center (NEIC). We are also looking at different open-source data gathered by researchers for a similar study. The deep neural networks we are implementing make use of Keras python Toolkit, and Theano and Tensorflow libraries, with a plan to use PyTorch python library instead of Theano library in the future because of some maintenance issues. To this point our attempts have shown a good progress.

Mount Padang archeological site has been known since late nineteen century as a megalithic complex that sits on top. Our studies proves that the structure does not cover just the top but also wrap around the slopes covering about 15 ha area at least. Comprehensive geophysical surveys combining ground penetration radar (GPR) and multi-channel resistivity methods, seismic tomography augmented by bore-holes coring data and archeological excavations, show further that the structures are not only superficial but rooted into greater depth. The structures are not built at once but consisting several layers from consecutive periods. The uppermost layer on the surface consists of horizontal piles of basaltic columnar rocks forming step-structure terraces and decorated by exotic arrangements of stand-up rock columns forming walls, paths and spaces. The second layer, which had been previously misinterpreted as natural rock formation, buried 1-3 meters beneath the ground surface, is a several-meter thick fills consisting of more compact and advance arrangement of similar columnar rocks in fine-grain matrix. The third layer is also artificial arrangement of rock fragments with various kinds that extent down to about 15 meter deep. The third layer sits on fractured, massive basaltic lava tongue. The survey also reveals evidences of large underground cavities or chambers. Results of preliminary radiocarbon dating indicates that the first layer was built around Cal BP 3,000. The second layer was built around about Cal BP 7,000. The third layer was built prior to Cal.BP. 9,500, and could be as old as Cal.BP.13,000 to 28,000 years old.

A holistic review is given of the Southern Ocean dynamic system, in the context of the crucial role it plays in the global climate and the profound changes it is experiencing. The review focuses on connections between different components of the Southern Ocean dynamic system, drawing together contemporary perspectives from different research communities, with the objective of "closing loops" in our understanding of the complex network of feedbacks in the overall system. The review is targeted at researchers in Southern Ocean physical science with the ambition of broadening their knowledge beyond their specific field and facilitating better-informed interdisciplinary collaborations. For the purposes of this review, the Southern Ocean dynamic system is divided into four main components: large-scale circulation; cryosphere; turbulence; and gravity waves. Overviews are given of the key dynamical phenomena for each component, before describing the linkages between the components. The reviews are complemented by an overview of observed Southern Ocean trends and future climate projections. Priority research areas are identified to close remaining loops in our understanding of the Southern Ocean system.

Sea-level rise (SLR) will cause coastal groundwater to rise in many coastal urban environments. Inundation of contaminated soils by groundwater rise (GWR) will alter the physical, biological, and geochemical conditions that influence the fate and transport of existing contaminants. These transformed products can be more toxic and/or more mobile under future conditions driven by SLR and GWR. We reviewed the vulnerability of contaminated sites to GWR in a US national database and in a case comparison with the San Francisco Bay region to estimate the risk of rising groundwater to human and ecosystem health. The results show that 326 sites in the US Superfund program may be vulnerable to changes in groundwater depth or flow direction as a result of SLR, representing 18.1 million hectares of contaminated land. In the San Francisco Bay Area, we found that GWR is predicted to impact twice as much land area as inundation from SLR, and 5,282 additional state-managed sites of contamination may be vulnerable to inundation from GWR in a 1.0 m SLR scenario. Increases of only a few centimeters of elevation can mobilize soil contaminants, alter flow directions in a heterogeneous urban environment with underground pipes and utility trenches, and result in new exposure pathways. Pumping for flood protection will elevate the saltwater interface, changing groundwater salinity and mobilizing metals in soil. Socially vulnerable communities are disproportionately exposed to this risk at both the national scale and in a regional comparison with the San Francisco Bay Area.

Numerical simulation of rupture dynamics provides critical insights for understanding earthquake physics, while the complex geometry of natural faults makes numerical method development challenging. The discontinuous Galerkin (DG) method is suitable for handling complex fault geometries. In the DG method, the fault boundary conditions can be conveniently imposed through the upwind flux by solving a Riemann problem based on a velocity-strain elastodynamic equation. However, the universal adoption of upwind flux can cause spatial oscillations in cases where elements on adjacent sides of the fault surface are not nearly symmetric. Here we propose a nodal DG method with an upwind/central mixed-flux scheme to solve the spatial oscillation problem, and thus to reduce the dependence on mesh quality. We verify the new method by comparing our results with those from other methods on a series of published benchmark problems with complex fault geometries, heterogeneous materials, off-fault plasticity, roughness, thermal pressurization, and various versions of fault friction laws. Finally, we demonstrate that our method can be applied to simulate the dynamic rupture process of the 2008 Mw 7.9 Wenchuan earthquake along/across multiple fault segments. Our method can achieve high scalability in parallel computing under different orders of accuracy, showing high potential for adaptation to earthquake rupture simulation on natural tectonic faults.

We report observations of Rayleigh waves that orbit around Mars up to three times following the S1222a marsquake. Averaging these signals, we find the largest amplitude signals at 30 s and 85 s central period, propagating with distinctly different group velocities of 2.9 km/s and 3.8 km/s, respectively. The group velocities constraining the average crustal thickness beneath the great circle path rule out the majority of previous crustal models of Mars that have a >200 kg/m3 density contrast across the dichotomy. We find that the thickness of the martian crust is 42-56 km on average, and thus thicker than the crusts of the Earth and Moon. Together with thermal evolution models, a thick martian crust suggests that the crust must contain 50-70% of the total heat production to explain present-day local melt zones in the interior of Mars.

The 2022 Hunga eruption caused unprecedented stratospheric hydration.  Aura Microwave Limb Sounder (MLS) measurements show that the stratospheric water vapor mass remains essentially unchanged as of early 2024 and that the Hunga hydration occurred atop a robust (possibly accelerating) moistening trend in the stratosphere. Enhanced by the excess Hunga water vapor, dehydration via polar stratospheric cloud (PSC) sedimentation in the 2023 Antarctic vortex exceeded climatological values by ~50%. Simple projections, based solely on Antarctic dehydration, illustrate that the timing of the return to humidity levels that would have been expected absent the Hunga hydration depends on the ongoing stratospheric water vapor trend. For strong moistening, the influx of water entering the stratosphere could offset the enhanced PSC dehydration, resulting in a new, more humid 'equilibrium' stratospheric state. With the Hunga hydration compounding an underlying moistening trend, the stratosphere could remain anomalously humid for an extended period. 

• Climate change and impacts will continue to accelerate until the warming influences are reduced or offset by direct cooling approaches. • Direct climate cooling approaches have the potential to reduce local to global portions of human-induced warming influences. • GHG emission reduction and removal policies alone will take at least decades to halt warming, much less restore 20 th century conditions.

WWLLN (World Wide Lightning Location Network) data on global lightning are used to investigate the increase of total lightning strokes at Arctic latitudes. We focus on the summertime data from June, July and August, which average >200,000 strokes each year above 65o North latitude, for each of the years from 2010 – 2020. The influence of WWLLN network detection efficiency increases is minimized by normalizing to the total global strokes for each northern summer. The ratio of strokes occurring above 65o increases with latitude, showing that the Arctic is becoming much more influenced by lightning. We compare the increasing fraction of strokes with the global temperature anomaly for those months, and find that the fraction of strokes above 65o to total global strokes for these months increases linearly with the temperature anomaly and grows by a factor of 3 as the anomaly increases from 0.65 to 0.95 degrees C.

At the current permanent sequestration rate of CO2 into Limestone, all life of all forms on planet Earth could be extinct in as short as 54,286 years as we run out of CO2. During ice ages the cold oceans sequester CO2 out of the atmosphere and into the oceans. During the last ice age which ended just 12,000 years ago, CO2 dropped to 180 ppm. Plants do not grow with CO2 at 150 ppm or less. There is evidence of plant stress during this last ice age period. All our food comes from plants. Without CO2 there will be no plants and therefore no life on planet Earth at all. We were a mere 30 ppm short of the total extinction of all life on Earth.

Global emission reduction efforts continue to be insufficient to meet the temperature goal of the Paris Agreement1. This makes the systematic exploration of so-called overshoot pathways that temporarily exceed a targeted global warming limit before drawing temperatures back down to safer levels a priority for science and policy2–5. Here we show that global and regional climate change and associated risks after an overshoot are different from a world that avoids it. We find that achieving declining global temperatures can limit long-term climate risks compared to a mere stabilisation of global warming, including for sea-level rise and cryosphere changes. However, the possibility that global warming could be reversed many decades into the future might be of limited relevance for adaptation planning today. Temperature reversal could be undercut by strong Earth-system feedbacks resulting in high near-term and continuous long-term warming6,7. To hedge and protect against high-risk outcomes, we identify the geophysical need for a preventive carbon dioxide removal (CDR) capacity of several hundred gigatonnes. Yet, technical, economic and sustainability considerations may limit the realisation of CDR deployment at such scales8,9. We thus cannot be confident that temperature decline after overshoot is achievable within the timescales envisioned today. Only rapid near-term emission reductions are effective in reducing climate risks.

Imaging of plants using multi-camera arrays in high-density growth environments is a strategy for affordable high-throughput phenotyping. In multi-camera systems, simultaneous imaging of hundreds to thousands of plants eliminates the time delay in measurements between plants seen in plant-to-camera or camera-to-plant systems, which allows for the analysis of plant growth, development, and environmental responses at a high temporal resolution. On the other hand, high plant density, camera-to-camera variation, and other trade-offs increase the complexity of data analysis. Here we present two recent updates to the PlantCV image analysis package to improve usability when working with multi-plant datasets. First, we introduce a method to automate detection of plants organized in a grid layout, reducing the need to make separate workflows for each camera in a multi-camera system. Second, we reduced the number of input and output parameters for functions handling the shape and location of plants and introduce automatic iteration over multiple objects of interest (e.g. plants), reducing the level of programming needed to build workflows.

The Hunga Tonga-Hunga Ha’apai (HTHH) volcanic eruption in January 2022 injected extreme amounts of water vapor (H2O) and a moderate amount of the aerosol precursor (SO2) into the Southern Hemisphere (SH) stratosphere. The H2O and aerosol perturbations have persisted and resulted in large-scale SH stratospheric cooling, equatorward shift of the Antarctic polar vortex, and slowing of the Brewer-Dobson circulation associated with a substantial ozone reduction in the SH winter midlatitudes. Chemistry-climate model simulations forced by realistic HTHH inputs of H2O and SO2 reproduce the observed stratospheric cooling and circulation effects, demonstrating the observed behavior is due to the volcanic influences. Furthermore, the combination of aerosol transport to polar latitudes and a cold polar vortex enhances springtime Antarctic ozone loss, consistent with observed polar ozone behavior in 2022.

Developing AI models that match or exceed the forecast skill of numerical weather prediction (NWP) systems but run much more quickly is a burgeoning area of research. Most AI-NWP models, however, have been trained on global ERA5 data, which does not resolve storm-scale evolution. We have therefore adapted Google’s GraphCast framework for limited-area, storm-scale domains, then trained on archived forecasts from the Warn-on-Forecast System (WoFS), a convection-allowing ensemble with 5-min forecast output. We evaluate the WoFSCast predictions using object-based verification, grid-based verification, spatial storm structure assessments, and spectra analysis. The WoFSCast closely emulates the WoFS environment fields, matches 70–80% of WoFS storms out to 2-h forecast times, and suffers only modest blurring. When verified against observed storms, WoFSCast produces a similar CSI as WoFS but with a lower frequency bias due to fewer false-alarm storms. WoFSCast demonstrates that AI-NWP can be extended to rapidly evolving, small-scale, chaotic phenomena like thunderstorms.

A document by Martijn van den Ende. Click on the document to view its contents.

We introduce a computer program for time series tuning and analysis. The well known in paleo-climatic community program AnalySeries after Paillard et al. is restricted to the Mac OS (32-bit) and, according to Apple plans to move entirely onto the 64-bit system, it will not be supported in the future upgrades of Macintosh OS. QAnalySeries is an attempt to re-implement the major functionality of AnalySeries thus providing the community with a useful tool. QAnalySeries is written using Qt SDK as a free software and can be run on Macintosh, Windows and Linux systems. Paillard, D., L. Labeyrie and P. Yiou (1996), Macintosh program performs time-series analysis, Eos Trans. AGU, 77: 379.

The agricultural Sustainable Nitrogen Management Index (SNMI) is defined to provide a more comprehensive measurement of the environmental performance of the agricultural production. Here, the SNMI is defined based on two important efficiency terms in crop production, namely Nitrogen Use Efficiency and land use efficiency. As more data become available, the SNMI could be reviewed and improved by including more efficiency terms in crop production, such as water use efficiency.