Recent structural and geodetic data define the Guatemala City graben region as the continental triple junction between the North American plate, Caribbean plate, and a forearc sliver. We present a minor fault analysis, geochronological and geochemical analyses, and newly updated GPS velocities in western Guatemala, west of the Guatemala City graben, to characterize the magnitude and timing of extensional deformation in this poorly understood area. Elongations estimated from fault data are parallel (~east-west) and perpendicular to the Polochic-Motagua fault system to the north, similar to geodetically-measured active deformation observed east of the Guatemala City graben. Four new 40Ar/39Ar dates and correlation of tephra deposits suggests that faulting was active during the Pliocene, but ceased eastward towards the Guatemala City graben over time. From west to east, fault cessation occurred before the deposition of the Los Chocoyos ash (84 ka) and E tephra (51 ka). Faulting just west of the Guatemala City graben appears to be active, where a major fault cuts the most recent Amatitlan tephras. Based on this data, we propose a time-progressive strain model for deformation related to North America-Caribbean plate interactions, whereby distributed elongation of the westernmost Caribbean plate occurred during the Pliocene but localized mostly within the Guatemala City graben and nearby faults during the Quaternary. Our model supports that: 1) The Guatemala City graben is effectively the western limit of the Caribbean plate; and 2) Western Guatemala, which used to be the trailing edge of the Caribbean plate, has been transferred to the forearc region.

Extreme heat under global warming is a concerning issue for the growing tropical population. However, model projections of extreme temperatures, a widely used metric for extreme heat, are uncertain on regional scales. In addition, humidity also needs to be taken into account in order to estimate the health impact of extreme heat. Here we show that an integrated temperature-humidity metric for the health impact of heat, namely the extreme wet-bulb temperature (TW), is controlled by established atmospheric dynamics and thus can be robustly projected on regional scales. For each 1°C of tropical mean warming, global climate models project extreme TW (the annual maximum of daily-mean or 3-hourly values) to increase roughly uniformly between 20°S and 20°N latitude by about 1°C. This projection is consistent with the theoretical expectation based on tropical atmospheric dynamics, and observations over the past 40 years, which gives confidence to the model projection. For a 1.5°C warmer world, the likely (66 per cent confidence interval) increase of regional extreme TW is projected to be 1.33-1.49°C, whereas the uncertainty of projected extreme temperatures is 3.7 times as large. These results suggest that limiting global warming to 1.5°C will prevent most of the tropics from reaching a TW of 35°C, the limit of human adaptation.

Biomass burning is a primary emission source for a host of gas- and aerosol-phase compounds, which can damage environmental and human health. During the FIREX-AQ campaign in July and August of 2019, we measured reactive nitrogen species (NOx, NO2, HONO, HNO3 and p-NO3-), in wildfire plumes aboard NASA Langley’s Mobile Aerosol Characterization Laboratory (MACH-2). Nitrous acid (HONO) and nitric acid (HNO3) mixing ratios were measured at nominal 5-minute resolution using a dual mist chamber-ion chromatograph from five separate areas of fire in the western US and are the primary focus of this paper. Average HONO mixing ratios were significantly higher in young daytime smoke compared to young nighttime smoke, while no statistical differences were observed between young versus aged smoke during the day or night. In the largest fire sampled during the day, UV-A irradiation was highly correlated (R2 = 0.91) with HONO to nitrogen dioxide (NO2) ratios indicating that photo-enhanced heterogeneous NO2 to HONO conversion, likely facilitated by ground surfaces (e.g. soil, foliage, and dust), more than compensated for rapid photolytic loss of HONO.

Although iron and light are understood to regulate the Southern Ocean biological carbon pump, observations have also indicated a possible role for manganese. Low concentrations in Southern Ocean surface waters suggest manganese limitation is possible, but its spatial extent remains poorly constrained and direct manganese limitation of the marine carbon cycle has been neglected by ocean models. Here, using available observations, we develop a new global biogeochemical model and find that phytoplankton in over half of the Southern Ocean cannot attain maximal growth rates because of manganese deficiency. Manganese limitation is most extensive in austral spring and depends on phytoplankton traits related to the size of photosynthetic antennae and the inhibition of manganese uptake by high zinc in Antarctic waters. Importantly, manganese limitation expands under the increased iron supply of past glacial periods, reducing the response of the biological carbon pump. Overall, these model experiments describe a mosaic of controls on Southern Ocean productivity that emerge from the interplay of light, iron, manganese and zinc, shaping the evolution of Antarctic phytoplankton since the opening of the Drake Passage.

During the global COVID-19 pandemic, anthropogenic emissions of air pollutants and greenhouse gases, especially traffic emissions in urban areas, have declined significantly. Long-term measurements of trace gas concentrations in urban areas can be used to quantify the impact of emission reductions on local air quality. Open-path Fourier transform infrared (OP-FTIR) spectroscopy is a non-intrusive technique that can be used to simultaneously measure multiple atmospheric trace gases in the boundary layer. This study investigates the reduction of surface CO, CO2 , and CH4 mole fractions during the lockdown in downtown Toronto, Canada, which is the fourth largest city in North America. The mean daily CO mole fraction anomaly (ΔCO) for the period from March 14 to May 18, 2020 declined by 46 ± 16% compared to the period before lockdown from January 13 to March 13, 2020. The mean daily ΔCO during the lockdown also declined relative to the same period in previous years: by 50 ± 20% relative to 2019 and by 44 ± 25% relative to 2018. Changes in the diurnal variations of CO, CO2 and CH4 during the lockdown are also investigated and compared to 2019 and 2018. Both CO and CO2 show early morning maxima on weekdays corresponding to rush hour. The change of the amplitude of the diurnal variation in CO during the lockdown is significant, compared to the period before lockdown. The differences in the diurnal variation in CO during the same two periods in 2019 and 2018 are not significant. Ratios of CO/CO2 anomalies show seasonal variations, which are also likely due to seasonal changes of emissions from local sources. These results show that the COVID-19 lockdown in Toronto modified surface mole fractions, diurnal variations, and ratios of air pollutants monitored by OP-FTIR. In addition, measured CO mole fractions are compared with simulated CO mole fractions by WRF-STILT to assess the relationship between atmospheric measurements and urban emissions from Toronto.

The majority of lake temperature studies have investigated climate-induced changes occurring at the lake surface, primarily by analyzing detailed satellite images of surface water temperature. Whilst essential to observe long-term change, satellite images do not provide information on the thermal environment at depth, thus limiting our understanding of lake thermal responses to a warming world. Long-term in-situ observational data can fill some of the information gap, with depth-resolved field measurements providing a detailed view of thermal change throughout the water column. However, previous studies that have investigated multi-decadal changes in lake temperature, both at the surface and at depth, have typically focused on north temperate lakes. Relatively few studies have investigated temperature variations in lakes situated north of the Arctic circle, which is one of the most rapidly warming regions globally. Here, using a sixty-year (1961-2020) observational dataset of summer water temperature from Lake Inari (Finland), we investigate changes in the thermal environment of this pristine lake. Our analysis suggests a significant summer warming trend at the lake surface (+0.247 °C decade-1) and a marginal cooling trend (–0.027 °C decade-1) at depth. The contrasting thermal response of surface and bottom water temperatures to climatic warming has likewise resulted in a strengthening of summer stratification in this high latitude lake. Implications of the observed change in both temperature and stratification on the lake ecosystem will likely be extensive, including impacts on aquatic organisms in which this lake supports. Our work builds on ever-growing literature regarding lake thermal responses to climate change.

The Nankai Trough is a locus of slow slip, low frequency earthquakes and Mw>8 classical earthquakes. It is assumed that high pore pressure contributes substantially to earthquake dynamics. Hence, a full understanding of the hydraulic regime of the Nankai accretionary prism is needed to understand this diversity of behaviors. We contribute to this understanding by innovatively integrating the drilling and logging data of the NanTroSEIZE project. We focus on the toe of the accretionary prism by studying data from Hole C0024A drilled and intersected the décollement at 813 mbsf about 3km away from the trench. Down Hole Annular Pressure was monitored during drilling. We perform a careful quantitative reanalysis of its variation and show localized fluid exchange between the formation and the borehole (excess of 0.05m3/s), especially in the damage zones at the footwall of the décollement. Pore pressure was estimated using Eaton’s method on both drilling and sonic velocity data. The formation fluids are getting significantly over-pressurized only a few hundred meters from the toe of the accretionary prism near the décollement with excess pore-pressure (P*≈0.04–4.79MPa) and lithostatic load (λ≈88-0.96 & λ*≈0.1-0.62 ) contributing to maximum 62% of the overburden stress. The hydraulic profile suggests that the plate boundary acts as a barrier inhibiting upward fluid convection, as well as a lateral channel along the damage zone, favouring high pore pressure at the footwall. Such high pressure at the toe of the subsection zone makes high pressure probable further down in the locus of tremors and slow slip events.

The impulse response of CO2 concentration due to variation in CO2 emissions was estimated from observed time series using the ARX method augmented by testing residuals for self-correlation. It was found to be a simple exponential with a half-time of 43 years with no remnant component. The longer half times and large remnant fraction of the impulse response derived from ocean circulation models is attributed to the failure of these models to account for turbulent mixing in the deep ocean.

Barrier inlets and marshes behind them are often viewed and managed as separate systems with independent controls because they are affected by different boundary conditions. Here, we make use of a 120-year-old storm-driven change in inlet location to illustrate how barrier beaches and wetland processes are intricately linked. Further, we show that tidal marshes can be resilient to a rapid increase in inundation given sufficient sediment supply and discuss implications for coastal management along sediment-deficient coastlines. In 1898, a coastal storm eroded a new inlet through the barrier beach that fronts the North-South Rivers Estuary in Massachusetts, USA. The old inlet silted in after the storm, and the change in inlet location shortened the North River channel by 5.6 km. After the inlet location change, historical records indicated increased high tide levels along the North River. We make use of this increase in water levels and associated marsh response to examine conditions that have allowed for marsh resilience after a rapid increase in inundation depth. Sediment cores show that increased mineral sediment deposition after 1898 played a dominant role in allowing marshes along the North River channel to adjust to greater inundation. To accommodate greater tidal flow after the change in inlet location, the North River channel widened by an average of 18%. Edge erosion from channel widening likely provided sediment to the marsh platform. Modern water level monitoring along the channel shows that mean high water declines landward by at 4.8 cm/km up to 10 km from the inlet. North River channel shortening thereby likely increased mean high water by at least 27 cm within the lower estuary. At present, the marsh platform elevation along both channels has largely reequilibrated to the effective change in sea level, with similar marsh inundation depths along both channels of the estuary. The role of mineral sediment in allowing for rapid marsh sediment deposition and resilience of this marsh to an abrupt increase in inundation depth points to the importance of management strategies that maintain sediment supplies to coastal regions.

The “felt temperature” is the preferred measure of hotness or coldness expressed to depict human sensory. However, to date, our perception of its spatial pattern with fine spatiotemporal data remains incomplete. Here, we demonstrated an empirical statistical approach incorporating atmospheric dynamics theory with aerodynamic parameters capable of developing hourly datasets at a high spatial resolution (0.01ᵒ x 0.01ᵒ). This fusion mechanism model, named the Humidex Reconstruction Model based on Numerical Simulation Data (HRMNSD), employed reanalysis data and satellite data for both near surface temperature(Tair) and the dew point temperature(Tdew) to combine their respective advantages in the correct representation of a turbulent exchange between the surface and the atmosphere. We showed the good performance of this model in each season using the Yangtze River Delta, China as an example. The RMSEs of the Humidex were 2.47°C (in winter), 2.49°C (in spring), 2.80°C (in summer) and 2.56°C (in autumn), respectively.

We report on a new method to calibrate the FORMOSAT-7/COSMIC-2 (F7/C2) ion velocity meter (IVM) in-situ ion density data using the Tri GNSS (Global Navigation Satellite System) Radio-occultation System (TGRS) differential total electron content (TEC) data. This calibration is made using collocated measurements from the IVM and TGRS instruments on the same satellite. We found that the IVM ion density is about 8-15% lower than the TGRS derived density at the insertion orbit (~ 710 km) and 5% higher at the mission operation orbit (~ 540 km). Using a linear correction specific to orbit altitude and satellite, an adjustment is implemented for the IVM density data. These corrections remove the offsets between the IVM and TGRS density measurements. We believe the corrected densities eliminate any inter-spacecraft discrepancy in the IVM density data, making it suitable for use in multi-satellite scientific investigations of longitudinal and local time variations and space weather operational applications.

Machine learning models using seismic emissions can predict instantaneous fault characteristics such as displacement in laboratory experiments and slow slip in Earth. Here, we address whether the acoustic emission (AE) from laboratory experiments contains information about near-future frictional behavior. The approach uses a convolutional encoder-decoder containing a transformer layer. We use as input progressively larger AE input time windows and progressively larger output friction time windows. The attention map from the transformer is used to interpret which regions of the AE contain hidden information corresponding to future frictional behavior. We find that very near-term predictive information is indeed contained in the AE signal, but farther into the future the predictions are progressively worse. Notably, information for predicting near future frictional failure and recovery are found to be contained in the AE signal. This first effort predicting future fault frictional behavior with machine learning will guide efforts for applications in Earth.

Coal fly ash has long been considered a potential resource for recovery of valuable elements, such as rare earth elements (REE), which are retained and concentrated upon combustion of coal feedstocks. Understanding REE occurrence within fly ash is a key to developing possible recovery methods. Recent results using modern analytical approaches shed light on the distribution REE in fly ash and the approaches required for their recovery. Some of the highest REE contents occur in fly ash derived from U.S. Appalachian Basin coals, and among these, coals influenced by input volcanic ash (Fire Clay coal, Kentucky) are especially enriched. Leaching studies of bulk fly ash show that, as a proportion of the total REE present, samples from eastern U.S. coals are generally less readily extractible than fly ash derived from western U.S. coals having lower REE contents. Direct determinations by ion microprobe show that REE in a range of fly ash samples are partitioned into aluminosilicate glasses formed during melting at boiler temperatures. These glasses comprise the largest mass fraction of coal fly ash. REE-enriched domains are present locally in fly ash at the nanometer scale (as shown by TEM), and these REE coexist with the glass phase. To enable systematic study of these REE, Ce has been proposed as a proxy for the trivalent lanthanides, as supported by speciation determinations demonstrating that Ce occurs in the trivalent form in fly ash. Despite a decreasing proportion of coal use for electric power generation in the U.S. and elsewhere, annual fly ash production, combined with coal ash already in storage, make up a large resource for potential recovery of rare earths and associated critical elements. Further developments in extraction technologies are needed to overcome difficulties in REE concentration and purification to produce REE materials of saleable purity derived from coal ash.

Horse collar aurora (HCA) are an auroral feature where the dawn and dusk sector auroral oval moves polewards and the polar cap becomes teardrop shaped. They form during prolonged periods of northward IMF, when the IMF clock angle is small. Their formation has been linked to dual-lobe reconnection (DLR) closing magnetic flux at the dayside magnetopause. The conditions necessary for DLR are currently not well-understood therefore understanding HCA statistics will allow DLR to be studied in more detail. We have identified over 600 HCA events between 2010 and 2016 in UV images captured by the Special Sensor Ultraviolet Spectrographic Imager (SSUSI) instrument on-board the Defense Meteorological Satellite Program (DMSP) spacecraft F16, F17 and F18. As expected, there is a clear preference for HCA occurring during northward IMF. We find no clear seasonal dependence in their occurrence, with an average of 8 HCA events per month. The occurrence of HCA events does not appear to depend on the Bx component of the IMF, suggesting that Bx does not modulate the rate of lobe reconnection. Considering the average radiance intensity across the dusk-dawn meridian shows the HCA as a separate bulge inside the auroral oval and that the dawn side arc of the HCA is usually brighter than the dusk in the Lyman-Birge-Hopfield short band (LBHs). We relate this to the expected field aligned current (FAC) pattern of HCA formation. We further suggest that transpolar arcs observed in the dawn sector simultaneously in both northern and southern hemispheres are misidentified HCA.

For decades, the distinction between statistical models and machine learning ones has been clear. The former are optimized to produce interpretable results, whereas the latter seeks to maximize the predictive performance of the task at hand. This is valid for any scientific field and for any method belonging to the two categories mentioned above. When attempting to predict natural hazards, this difference has lead researchers to make drastic decisions on which aspect to prioritize, a difficult choice to make. In fact, one would always seek the highest performance because at higher performances correspond better decisions for disaster risk reduction. However, scientists also wish to understand the results, as a way to rely on the tool they developed. Today, very recent development in deep learning have brought forward a new generation of interpretable artificial intelligence, where the prediction power typical of machine learning tools is equipped with a level of explanatory power typical of statistical approaches. In this work, we attempt to demonstrate the capabilities of this new generation of explainable artificial intelligence (ExAI). To do so, we take the landslide susceptibility context as reference. Specifically, we build an ExAI trained to model landslides occurred in response to the Gorkha earthquake (25 April 2015), providing an educational overview of the model design and its querying opportunities. The results are surprising, the performance are extremely high, while the interpretability can be extended to the probabilistic result assigned to single mapping units. This is also showcased in a web-GIS (\textcolor{blue}{https://arcg.is/0unziD}) platform we built.

The relatively weak tropical storms, known as low-pressure systems (LPSs), contribute as much as 60% of the seasonal precipitation over the hugely populated central India. More than a third of LPS are formed by the downstream amplification of the westward propagating disturbances from the Pacific. Here, we show that the downstream LPS genesis are associated with the tropical cyclones (TCs) in the Western North Pacific (WNP). Four major clusters of landfalling TCs that have a relatively lesser degree of recurvature account for about 83% of the downstream LPS genesis. Causality in the fluctuations of the sea level pressure over the Bay of Bengal (BoB) prior to the initiation of LPS is attributed to the Rossby wave activity over WNP through transfer entropy analysis. Our results suggest a potential for the prediction of the downstream synoptic activity over the BoB at least seven days ahead.

A document by Patrick Ongom. Click on the document to view its contents.

The shallowest intracrustal layer (extending to 8 ± 2 km depth) beneath the Mars InSight Lander site exhibits low seismic wave velocity, which are likely related to a combination of high porosity and other lithological factors. The SsPp phase, an SV-to P-wave reflection on the receiver side, is naturally suited for constraining the seismic structure of this top crustal layer since its prominent signal makes it observable with a single station without the need for stacking. We have analyzed eight broadband and low-frequency seismic events recorded on Mars and made the first coherent detection of the SsPp phase on the red planet. The timing and amplitude of SsPp confirm the existence of the ~8 km interface in the crust and the large wave speed (or impedance) contrast across it. With our new constraints from the SsPp phase, we determined that the P-wave speed in the top crustal layer is between 2.5 km/s and 3.3 km/s, which is a more precise and robust estimate than the previous range of 2.0-3.5 km/s obtained by receiver function analysis. The porosity in Layer 1 is estimated to be as much as 21-31% (assuming an aspect ratio of 0.1 for the pore space), but could be lower if some pores are filled by low-density cements or other secondary 1 mineral phases. These porosities and P-wave speeds are compatible with our current understanding of the upper crustal stratigraphy beneath the InSight Lander site.