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The Central Andes in northwestern Argentina are characterized by steep topographic and climatic gradients. The humid foreland areas at 1 km asl elevation rapidly rise to over 5 km in the eastern Cordillera, and they form an orographic rainfall barrier on the eastern windward side. This topographic setting combined with seasonal moisture transport through the South American monsoon system leads to intense rainstorms with cascading effects such as landsliding and flooding. In order to better quantify the dynamics of water vapour transport, we use high-temporal-resolution global navigation satellite system (GNSS) remote sensing techniques. We are particularly interested in better understanding the dynamics of high-magnitude storms with high water vapour amounts that have destructive effects on human infrastructure. We used an existing GNSS station network with 12 years of time series data, and we installed two new ground stations along the climatic gradient and collected GNSS time series data for three years. For several stations we calculated the GNSS signal delay gradient to determine water vapour transport direction. Our statistical analysis combines in situ rainfall measurements and ERA5 reanalysis data to reveal the water vapour transport mechanism for the study area. The results show a strong relationship between altitude and the water vapour content, as well as between the transportation pathways and the topography.
Sedimentary records of Plio-Pleistocene intermontane basins of the Eastern Cordillera and the adjacent Puna Plateau in the Central Andes of NW Argentina (hinterland basins) are important geological archives that provide spatiotemporal insights into regional tectonism, the uplift history of basin-bounding mountain ranges, and associated depositional and paleoenvironmental changes. Here, we reconstruct the Plio-Pleistocene evolution of the intermontane Humahuaca Basin based on the study of depositional systems, unconformities, accumulation rates, depositional patterns, U-Pb geochronology, magnetostratigraphy, and sediment provenance of the Uquia Formation - a ca. 4.8-1.5-My-old sedimentary basin record consisting of a 100-400 m thick fining-upward stack of conglomerates, sandstones, and siltstones with intercalated volcanic tuffs. The sedimentary facies of the Uquia Formation comprise debris flow, deep sandy gravel braided alluvial fan deposits, sheetflood dominated, floodplains, and shallow ephemeral lake deposits. Facies characteristics and 818O and 813C values from pedogenic and palustrine carbonates indicate freshwater lacustrine conditions at the base and evaporative conditions towards the top of the Uquia Formation (ca. 2.3 Ma). During the deposition of the Uquia Formation, the Humahuaca Basin was already bounded by uplifted mountain ranges: (a) the Sierra Alta to the west, which experienced early uplift during the middle Eocene and increased exhumation from about 15 to 10 Ma; and (b) the Aparzo and Tilcara ranges to the east, whose deformation and uplift began about 15-10 Ma and culminated in the structural and fluvial separation of the Humahuaca Basin from the foreland by ca. 4.8 Ma in the center and by about 4.2 Ma in the southern sector of the basin. This is supported by variable unroofing patterns, the paleoenvironmental evolution, deposition of sheetflood dominated alluvial fans, and lacustrine deposits.
Global seismicity models provide scientific hypotheses about the rate, location and magnitude of future earthquakes to occur worldwide. Given the aleatory variability of earthquake activity and epistemic uncertainties in seismicity forecasting, the veracity of these hypotheses can only be confirmed or rejected after prospective forecast evaluation. In this study, we present the construction of and test results for two updated global earthquake models, aimed at providing mean estimates of shallow (d <= 70 km) seismicity for seismic hazard assessment. These approaches, referred to as the Tectonic Earthquake Activity Model (TEAM) and the World Hybrid Earthquake Estimates based on Likelihood scores (WHEEL) model, use the Subduction Megathrust Earthquake Rate Forecast (SMERF2), an earthquake-rate model for subduction zones constrained by geodetic strain measurements and earthquake-catalogue information. Thus, these global ensemble seismicity models capture two independent components necessary for long-term earthquake forecasting, namely interseismic crustal strain accumulation and sudden lithospheric stress release. The calibration period for TEAM and WHEEL extends from 1977 January 1 to 2013 December 31. Accordingly, we use to >= 5.95 earthquakes recorded during the 2014-2019 period to pseudo-prospectively evaluate the forecasting skills of these earthquake models, and statistically compare their performances to that of the Global Earthquake Activity Rate (GEAR1) model. As a result, GEAR1 and WHEEL are the most informative global seismicity models during the pseudo-prospective test period, as both rank with the highest information scores among all participant earthquake-rate forecasts. Nonetheless, further prospective evaluations are required to more accurately assess the abilities of these global ensemble seismicity models to forecast long-term earthquake activity.
High-resolution records from International Ocean Discovery Program (IODP) sites U1386 and U1387 drilled during IODP Expedition 339 into the Faro drift, made it possible to assess the impact of intensifications of the upper core (MOWu) of the Mediterranean Outflow Water (MOW) and of changes in sediment supply on the sedimentation in the northern Gulf of Cadiz since the Middle Pleistocene. This work focuses on the comparison of records covering Marine Isotope Stage (MIS) 2-1 and MIS 12-11, in order to investigate the behaviour and circulation regime of the MOWu over two climatic cycles of similar astronomical configurations and their associated deglaciation. The analysis of facies established on the basis of grain size, XRF core-scanning, and carbonate content revealed contourite beds formed by the MOWu during MIS 11 and MIS 1 and deglaciations (deglaciation V and I). Contourite sequences show that MOWu velocity at the seabed was higher during MIS 2-1 than during MIS 12-11, and that sediment supply was different between these two climatic cycles. While overall low during MIS 12-11, MOWu intensity increased during deglaciation V and MIS 11 and preceded large ice rafted events and cooling in the North Atlantic Ocean. As a major element of the MOW, MOWu strengthening during deglaciation V likely contributed to higher heat and moisture transport towards the high latitudes inducing a slight increase of calving and size of boreal ice sheets. The MOW-derived injection of heat and salt in the North Atlantic Ocean during deglaciation V might have contributed, through reactivation of the upper AMOC, to the switch of the Atlantic thermohaline circulation from a glacial to an interglacial mode.
Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction
(2022)
The end-Permian mass extinction occurred alongside a large swath of environmental changes that are often invoked as extinction mechanisms, even when a direct link is lacking. One way to elucidate the cause(s) of a mass extinction is to investigate extinction selectivity, as it can reveal critical information on organismic traits as key determinants of extinction and survival. Here we show that machine learning algorithms, specifically gradient boosted decision trees, can be used to identify determinants of extinction as well as to predict extinction risk. To understand which factors led to the end-Permian mass extinction during an extreme global warming event, we quantified the ecological selectivity of marine extinctions in the well-studied South China region. We find that extinction selectivity varies between different groups of organisms and that a synergy of multiple environmental stressors best explains the overall end-Permian extinction selectivity pattern. Extinction risk was greater for genera that had a low species richness, narrow bathymetric ranges limited to deep-water habitats, a stationary mode of life, a siliceous skeleton, or, less critically, calcitic skeletons. These selective losses directly link the extinctions to the environmental effects of rapid injections of carbon dioxide into the ocean-atmosphere system, specifically the combined effects of expanded oxygen minimum zones, rapid warming, and potentially ocean acidification.
High-altitude ecosystems react sensitively to hydroclimatic triggers. Here we evaluated the ecological and hydrological changes in a glacier-influenced lake (Hala Hu, China) since the last glacial. Rapid fluctuations of aquatic biomarker concentrations, ratios, and hydrogen isotope values, from 15 to 14,000 and 8 to 5000 years before present, provided evidence for aquatic regime shifts and changes in lake hydrology. In contrast, most negative hydrogen isotope values of terrestrial biomarkers were observed between 9 and 7,000 years before present. This shows that shifts of vapour sources and increased precipitation amounts were not relevant drivers behind ecosystem changes in the studied lake. Instead, receding glaciers and increased meltwater discharge, driven by higher temperatures, caused the pronounced ecological responses. The shifts within phytoplankton communities in the Late Glacial and mid Holocene illustrate the vulnerability of comparable ecosystems to climatic and hydrological changes. This is relevant to assess future ecological responses to global warming.
The Lake Hala ecosystem on the Tibetan Plateau is more sensitive to local changes in temperature and glacial melt than it is to large-scale monsoonal variability, according to aquatic biomarker analysis in lacustrine sediment cores.
We report the first direct measurements of the refractive index of silica glass up to 145 GPa that allowed quantifying its density, bulk modulus, Lorenz-Lorentz polarizability, and band gap. These properties show two major anomalies at similar to 10 and similar to 40 GPa. The anomaly at similar to 10 GPa signals the onset of the increase in Si coordination, and the anomaly at similar to 40 GPa corresponds to a nearly complete vanishing of fourfold Si. More generally, we show that the compressibility and density of noncrystalline solids can be accurately measured in simple optical experiments up to at least 110 GPa.
Analyzing seismic data in a timely manner is essential for potential eruption forecasting and early warning in volcanology. Here, we demonstrate that unsupervised machine learning methods can automatically uncover hidden details from the continuous seismic signals recorded during Iceland’s 2021 Geldingadalir eruption. By pinpointing the eruption’s primary phases, including periods of unrest, ongoing lava extrusion, and varying lava fountaining intensities, we can effectively chart its temporal progress. We detect a volcanic tremor sequence three days before the eruption, which may signify impending eruptive activities. Moreover, the discerned seismicity patterns and their temporal changes offer insights into the shift from vigorous outflows to lava fountaining. Based on the extracted patterns of seismicity and their temporal variations we propose an explanation for this transition. We hypothesize that the emergence of episodic tremors in the seismic data in early May could be related to an increase in the discharge rate in late April.
Extreme events are defined as events that largely deviate from the nominal state of the system as observed in a time series. Due to the rarity and uncertainty of their occurrence, predicting extreme events has been challenging.
In real life, some variables (passive variables) often encode significant information about the occurrence of extreme events manifested in another variable (active variable).
For example, observables such as temperature, pressure, etc., act as passive variables in case of extreme precipitation events. These passive variables do not show any large excursion from the nominal condition yet carry the fingerprint of the extreme events. In this study, we propose a reservoir computation-based framework that can predict the preceding structure or pattern in the time evolution of the active variable that leads to an extreme event using information from the passive variable.
An appropriate threshold height of events is a prerequisite for detecting extreme events and improving the skill of their prediction. We demonstrate that the magnitude of extreme events and the appearance of a coherent pattern before the arrival of the extreme event in a time series affect the prediction skill.
Quantitatively, we confirm this using a metric describing the mean phase difference between the input time signals, which decreases when the magnitude of the extreme event is relatively higher, thereby increasing the predictability skill.
Iron-bearing carbonates play an important role in Earth's carbon cycle. Owing to their stability at mantle conditions, recently discovered iron carbonates with tetrahedrally coordinated carbon atoms are candidates for carbon storage in the deep Earth.
The carbonates' iron oxidation and spin state at extreme pressure and temperature conditions contribute to the redox conditions and element partitioning in the deep mantle.
By laser heating FeCO3 at pressures of about 83 GPa, Fe43+C3O12 and Fe22+Fe23+C4O13 were synthesized and then investigated by x-ray emission spectroscopy to elucidate their spin state, both in situ and temperature quenched. Our experimental results show both phases in a high-spin state at all pressures and over the entire temperature range investigated, i.e., up to 3000 K.
The spin state is conserved after temperature quenching. A formation path is favored where Fe43+C3O12 forms first and then reacts to Fe22+Fe23+C4O13, most likely accompanied by the formation of oxides. Density functional theory calculations of Fe22+Fe23+C4O13 at 80 GPa confirm the experimental findings with both ferric and ferrous iron in high-spin state with antiferromagnetic order at 80 GPa.
As the intercrystalline cation partitioning between the Fe-bearing carbonates and the surrounding perovskite and ferropericlase depends on the spin state of the iron, an understanding of the redox conditions prevalent in subducted slab regions in the lower mantle has to take the latter into account.
Especially, Fe22+Fe23+C4O13 may play a key role in subducted material in the lower mantle, potentially with a similar role as silicate perovskite.