Watershed nitrogen (N) budgets provide insights into drivers and solutions for groundwater and surface water N contamination. We constructed a comprehensive N budget for the transboundary Nooksack River Watershed (BC, Canada and WA, US) using locally-derived data, national statistics and standard parameters. Feed imports for dairy (mainly in the US) and poultry (mainly in Canada) accounted for 30 and 29% of the total N input to the watershed, respectively. Synthetic fertilizer was the next largest source contributing 21% of inputs. Food imports for humans and pets together accounted for 9% of total inputs, slightly lower than atmospheric deposition (10%). Returning salmon represented <0.06% of total N input but was an important ecological flux by importing marine-derived nutrients. Quantified N export was 80% of total N input, driven by ammonia emission (32% of exports). Animal product export was the second largest output of N (31%) as milk and cattle in the US and poultry products in Canada. Riverine export of N was estimated 28% of total N export. The commonly used crop nitrogen use efficiency (crop NUE) alone did not provide sufficient information on farming activities and should be combined with other criteria such as farm-gate NUE to understand management efficiency. Agriculture was the primary driver of N inputs to the environment despite widespread adoption of conservation practices, illustrating a need to optimize management to minimize hydrologic and volatilization losses. The N budget provides key information for stakeholders across sectors and borders to create environmentally and economically viable and effective solutions.

The amount of ice versus supercooled water in clouds defines their radiative properties and role in climate feedbacks. Hence, knowledge of the concentration of ice-nucleating particles (INPs) is needed. Generally, the concentrations of INP is found to be very low in remote marine locations allowing clouds to persist in a supercooled state. However, little is known about the INP population in clouds at and around the summertime North Pole. We had expected that concentrations of INPs at the North Pole would have been very low given the distance from open ocean and terrestrial sources coupled with effective wet scavenging processes. Here we show that during summer 2018 (August and September) high concentrations of biological INPs (active at >-20°C) were present at the North Pole. In fact, INP concentrations were sometimes as high as those recorded in mid-latitude locations strongly impacted by highly active biological INPs, in strong contrast to the Southern Ocean. Furthermore, using a balloon borne sampler we demonstrated that INP concentrations were often different at the surface versus higher in the boundary layer where clouds form. Back trajectory analysis suggests that there were strong sources of INPs near the Russian coast, possibly associated with wind-driven sea spray production, whereas the pack ice, open leads, and the marginal ice zone were not sources of highly active INPs. These findings suggest that primary ice production, and therefore Arctic climate, is sensitive to transport from locations such as the Russian coast that are already experiencing marked climate change.

Similar to many areas of the globe, South America is under a monsoon regime. The Amazon rain-forest is one of the regions critical for this regime, known as the South American Monsoon System (SAMS). Besides being crucial to the Earth’s carbon budget, the Amazon is also important for SAMS development. During SAMS onset, the trade winds intensify and a northwesterly low-level jet, known as the South American Low Level Jet (SALLJ), is formed east of the Andes. Moisture originating in the Atlantic Ocean is recycled and reinvigorated over the Amazon and carried by the SALLJ to continental and subtropical South America. In this project, we analyze the projected changes in precipitation and extreme events of rainfall over South America by the end of the 21st century using the CESM Large Ensemble Project (LENS). LENS is able to reproduce the important elements of the SAMS. We find that wet season rainfall is projected to increase over the east coast and central South America. The SALLJ is projected to become stronger, especially during late wet season, and carry more moisture to subtropical South America. As a result, moisture convergence between the SALLJ and the South Atlantic Subtropical High increases, creating the conditions to rainfall over Central and Southeastern South America. Meanwhile, Amazon is projected to become dryer during both dry and wet seasons. As a result, rain-forest productivity is reduced during late dry season in Southern Amazon. As vegetation struggles to save water, evapotranspiration, and consequently surface latent heat flux are reduced. These factors have been suggested as crucial to SAMS initiation and their reduction is responsible for a delay in SAMS onset by the end of the century. Extreme wet events are projected to become more frequent, especially over Northeastern Brazil, Southern Brazil and Uruguay. As dry season rainfall is reduced in all of the regions analyzed, drought events are projected to become more frequent and longer over both the wettest (Northern Amazon) and the driest (Northeastern Brazil) regions analyzed.

Data obtained from social media microblogging websites such as Twitter provide the unique ability to collect and analyze conversations of the public in order to gain perspective on the thoughts and feelings of the general public. Sentiment and volume analysis techniques were applied to the dataset in order to gain an understanding of the amount and level of sentiment associated with certain disaster-related tweets, including a topical analysis of specific terms. This study showed that disaster-type events such as a hurricane can cause some strong negative sentiment in the period of time directly preceding the event, but ultimately returns quickly to normal levels. An analysis of the volume of tweets during the same time revealed that the public responds in near real-time to events with conversation on Twitter. This information can be an effective tool in which to arm emergency management personnel with vital human intelligence information to inform decision-making processes ahead of future storm, or disaster-related events. In addition, this study performed empirical performance evaluation experiments on Latent Dirichlet Allocation (LDA) topic models which were generated from Twitter data collected from Hurricane Florence. The performance evaluation experiments showed that LDA topic models struggle to accurately reflect the true latent conversation topics present within a medium-term, event-based dataset. Although the study successfully modeled LDA topic models, it could not produce models that were interpretable by human beings as distinct groups of topic words that were tightly coupled to one another.

In this study, in situ and assimilated model data are used to study spatiotemporal variation in eddy heat transport (EHT) in the South China Sea (SCS) and associated mechanisms. Combining satellite data with data from a mooring buoy deployed in the northwestern SCS, we find that surface EHT exhibits a direction opposite to that calculated by the frequently used downgradient method, indicating the existence of upgradient EHT in the SCS. A well-validated model further confirms this finding and gives a detailed distribution of EHT for the entire SCS. Both time-averaged zonal and meridional EHT are significant at southeast of Vietnam (SEV) and southwest of Taiwan (SWT), and their vertical structures suggest that most of the EHT is confined to the upper 400 m. It is found that the EHT is strong in summer, autumn, and winter but relatively weaker in spring, with the upper 30 m showing stronger EHT seasonality than the next 370 m. In terms of physical process, zonal EHT is associated with its barotropic term, whereas meridional EHT is determined by both the barotropic term and deviations in the baroclinic shear term. When using model data, the downgradient method fails to reproduce the model’s actual EHT. Instead, the model exhibits a significant upgradient region SWT and a significant downgradient region SEV. Possible reasons for these disparities are further investigated. The time-mean state of the baroclinic energy transfer tendency due to temperature is mainly controlled by barotropic processes, but its frequencies differ among its time-varying parts.

A document by Richard Hester. Click on the document to view its contents.

The micro Advanced Stellar Compass (µASC), an attitude reference for the Juno Magnetic Field investigation, also continuously monitors high energy particle fluxes in Jupiter’s magnetosphere. The µASC camera head unit (CHU) shielding is sufficient to stop electrons with energy <15MeV. By recording the number of particles that penetrate µASC CHU shielding and deposit energy in the CCD sensor, the µASC functions as an energetic particle sensor with a detection threshold well above that of the Juno Energetic Particle Detector Instrument (JEDI) flown for that purpose. Radiation data gathered by the µASC is used to monitor the radiation environment of Jupiter and mapping of the trapped high energy particles. Comparison of the particle population around Jupiter with individual perijove particle observations reveals disturbances when Juno is traversing Ganymede’s M-shell. We present highly energetic electrons interaction with Ganymede’s magnetic field, magnitude and extend of the particle depletion associated with the Ganymede interaction.

The retreat of Arctic sea ice coincides with increased ocean surface wave activity, and wave-ice interactions are consequently poised to have a growing influence on the Arctic climate system. Recent field campaigns have focused on rectifying the scarcity of wave measurements inside the marginal ice zone, and work is now underway to incorporate wave-ice interactions in global climate models. Here, we apply a collection of in situ wave observations spanning multiple years in the Beaufort Sea and including wave activity beyond 100 kilometers inside the sea ice edge. To better understand waves in the presence of sea ice, we connect the in situ data with satellite-derived ice concentrations across the Arctic and compare the observations with a recent global climate model experiment that includes coupled interactions between waves and a sea ice floe size distribution. We present a series of comparisons focused on wave energy and wind-wave relationships in partial ice cover. These analyses provide a framework for assessing the impact of uncertainty in wave-ice physics on the marginal ice zone in new experiments in the coupled wave-ice model. Our work guides further model development and future observational campaigns.

Erosion and riverine transport of organic carbon is an important component of the global carbon cycle, but the significance of this process for Earth’s surface carbon budgets depends on the sources of carbon being mobilised. In this study, we aim to constrain how runoff-driven erosion modulates the contribution of different carbon source endmembers, i.e., bedrock, soil and vegetation, in three forested headwater catchments in the Swiss Prealps. The sources of organic carbon are determined using an inverse model based on bulk carbon isotope signatures and the abundances and distributions of long-chain n-alkane plant wax biomarkers in suspended sediments collected over a range of discharges. Despite landcover differences and contrasting bulk particulate organic carbon (POC) signatures, the increase of soil-sourced organic carbon with discharge is similar in all three studied catchments. This apparent existence of common processes implies that export fluxes of soil organic carbon may be extrapolated to similar catchments. Overall, our analysis shows that runoff-driven soil erosion in these alpine headwater streams is responsible for the export of ca. 0.3 to 0.8 gC m-2 a-1 as POC, which represents ca. 0.1 - 0.3 % of carbon fixed by NPP. Most of this soil OC export occurs during high-discharge events. Our study also shows that despite a significant variability in isotopic and molecular POC signatures at low discharge, all three catchments show a convergence of these signatures at higher discharges. Suspended sediment samples collected at above-average discharges are hence most representative of overall endmember contributions.

Single-aliquot (U-Th)/He dating of iron-oxides requires less mass and has a higher spatial resolution than the two-aliquot approach, and is, therefore, a more reliable tool for quantifying the timescales of weathering processes, fault activity, and the development of soils and surfaces. Highly helium-retentive hematite samples must be heated to 1000-1100 °C to completely degas He, but we show that U is lost progressively during laser-heating starting at ~900 °C, with major U-loss at ~980 °C and complete U-loss at 1050-1100 °C. This partial or complete loss of U leads to incorrect (U-Th)/He ages that appear older than the true ages. We performed a series of heating tests on hematite and goethite aliquots with independently determined (U-Th)/He ages of 10-1761 Ma in which the helium release was determined by isotope-dilution mass spectrometry and phase change was monitored by infrared spectroscopy. As hematite is heated, reduction of Fe3+ causes a phase change to magnetite and then to elemental Fe. We correlate the onset of U-loss to the phase change from hematite to magnetite. By raising the temperature of this phase transition using a high oxygen partial pressure (pO2) in the sample chamber during laser-heating, we show that the onset temperature of U-loss is equally raised. Samples can therefore be safely degassed at higher temperatures without any detectable U-loss. In our implementation of this technique, the O2 in the sample chamber is being withdrawn from a tank using a pipette and it is being released before and captured after the degassing process on activated charcoal in a cold finger with a movable LN2 Dewar flask. We describe the automation of this process for routine degassing of hematite samples. We show that an average age calculated on a reference hematite sample from replicate aliquots (n=12), which were analyzed using this procedure, has a relative uncertainty of 2% (1σ), and is within uncertainty of the previously measured two-aliquot age. We suggest this high-pO2 degassing procedure as a way to precisely and reproducibly determine single-aliquot hematite and goethite (U-Th)/He ages.

We investigate the role of pre-eruptive magma conditions in controlling eruption styles at basaltic andesite volcanoes, and their relationship to monitoring data. We focus on a cycle of explosive (1990, sub-Plinian) => effusive (2007 dome) => explosive (2014, sub-Plinian) eruptions at Kelud volcano, Indonesia. We conducted petrological analyses of eruption products and phase equilibria experiments using a pumice from the 1990 eruption across a wide range of conditions (temperature, pressure, oxygen fugacity, and volatile contents). We are able to reproduce the main mineral assemblage of all three magmas (plagioclase + pyroxenes + amphibole + olivine + magnetite) at T = 975 ± 39°C, P = 175 ± 25 MPa, NNO, and 4-6 wt. % water in the melt, which is also consistent with the phenocryst contents for explosive eruptions (30-50 wt. %). The high phenocryst content of the 2007 dome (~ 70 wt. %) implies a lower volatile content (~1 wt. % water in the melt), which likely resulted from slow magma ascent towards the surface alongside progressive equilibration. Mass balance calculations on the sulfur budget of the 1990 and 2014 magmas, show that they contained an excess fluid phase that was stored with the magma, and we propose that this led to their more explosive eruption style compared to the 2007 dome. Our hypothesis of accumulation of excess fluids could perhaps be recorded as changes in gravity data over time, and may open doors to better anticipate eruption styles.

Numerical thermal history modelling has become a core approach used for interpretation of low-temperature thermochronometry data. Modelling programs can find rock time-temperature (t-T) paths that fit the input data while incorporating independent geologic information about a sample’s history and leveraging the factors that impact the kinetics of each thermochronometric system (e.g., grain size, radiation damage, and composition). HeFTy (Ketcham, 2005) and QTQt (Gallagher, 2012) are two of the commonly used tools for both forward and inverse t-T modeling. The modelling process involves making key decisions about (i) data input, (ii) initial set-up of model space and parameters, (iii) kinetic model(s) (i.e. annealing, diffusion, radiation damage), and (iv) additional t-T constraints. In addition, users need to have an understanding of the statistical methods underlying the modelling approach to be able to interpret the model outputs and the relationship between the observed and predicted data. However, these modeling tools currently lack clear and accessible entry-points for all users—experienced and new thermochronologists alike—and thus for many geoscientists, there is a substantial barrier to the modeling, interpretation, and publication of thermochronologic datasets. Here we present a suite of simple forward and inverse models that we recommend everyone perform before embarking on t-T modeling in HeFTy and/or QTQt for the first time. At the core of the exercises are the six different t-T paths used by Wolf et al. (1998) to illustrate the partial-retention behavior of the apatite He system; however, this approach can easily be applied to other systems as well. This exercise not only illustrates the fundamental behavior of thermochronologic systems but also guides users through the main functionality of the modelling programs. Despite the apparent simplicity of this exercise, users will experience most of the challenges and opportunities common to thermal history modeling, including: how to enter data; error handling; how to use geologic constraints in t-T space; the non-unique nature of cooling ages; the power of grain size and eU variability; the limitations on a model’s ability to resolve the ‘right’ rock thermal history; and how to evaluate the sensitivity of model results to all these factors. These exercises were introduced in the Thermo2020/1 Sunday workshops for both QTQt and HeFTy and are more fully fleshed out in two publications currently in preparation.

The NASA/AERONET field campaign DRAGON/J-ALPS (Distributed Regional Aerosol Gridded Observation Networks/Joint work to the AerosoL Properties and Process Simulations) was conducted from March 2020 to May 2021 in Nagano, Japan. Twelve sun photometers were installed around Nagano prefecture. The effects of topography on aerosols were studied using observations and simulations. In this study, a regional chemical transport model (SCALE-Chem) was employed. Three numerical experiments were conducted: E1 (control experiment), E2 (E1 without topography), and E3 (E1 with removal of all anthropogenic emissions over Nagano prefecture). In E2, the terrain effect was not considered; the difference between E1 and E2 indicated the influence of mountains. The differences between E1 and E3 evaluate the local emission effect. In some cases, the mountainous terrain seemed to have suppressed aerosol inflow (i.e., reduced aerosol concentration), while in other cases, the mountains contributed to aerosol retention on days when aerosols tended to accumulate in mountain basins due to local emissions. Thus, while mountains prevent the inflow of aerosols from outside, they also contribute to increased aerosol concentration in the basin. Naturally, more significant effects are produced by meteorological conditions and the presence or absence of transboundary pollution from the outside. From observations and model simulations, we found that the aerosol concentration was not high around the J-ALPS site because of the mountain effect that prevents advection from the outside, even when transboundary pollution was observed in Japan in March 2020.

Unlike the traditional subgrid scale parameterizations used in climate models, current machine learning (ML) parameterizations are only tuned offline, by minimizing a loss function on outputs from high resolution models. This approach often leads to numerical instabilities and long-term biases. Here, we propose a method to design tunable ML parameterizations and calibrate them online. The calibration of the ML parameterization is achieved in two steps. First, some model parameters are included within the ML model input. This ML model is fitted at once for a range of values of the parameters, using an offline metric. Second, once the ML parameterization has been plugged into the climate model, the parameters included among the ML inputs are optimized with respect to an online metric quantifying errors on long-term statistics. We illustrate our method with a simple dynamical system. Our approach significantly reduces long-term biases of the ML model.

On January 2017, a snow avalanche devastated a Resort-hotel in the municipality of Rigopiano in Abruzzo (Central Italy), unfortunately, burying alive 40 people. In a dramatic rescue operation only 11 people could be recovered. Due to the bad weather conditions, no visual observation was made, thus making it impossible to determine the exact moment of the avalanche and to report necessary observations of the dramatic event. Many are the questions and hypotheses around this tragic event. On-site inspections revealed that the hotel was horizontally cut by shear forces and dislocated by 48 m in 70°deg;N direction, once the increasing avalanche pressure exceeded the structural shear strength of the building. Analyses of phone calls revealed that the avalanche struck sometime before 16:40, when the first emergency call was received, while the last phone call from Hotel Rigopiano before the avalanche was taken at 15:30. Subsequent inspections of the victims’ mobile phones indicates the latest possible event time as 15:54 (all times in UTC). Within this eligible 24 min time window, we scanned regional seismograms for any “suspicious” signal that could have been generated by the avalanche and found three weak seismic transients, starting at 15:42:38 UTC, recorded by the nearest operating station GIGS located in the Gran Sasso underground laboratory at a distance of approximately 17 km from Rigopiano. Particle motion analysis of the strongest seismic avalanche signal, as well as of the synthetic seismograms match best when assuming a single force seismic source, attacking in direction of 120°deg;N. Hundreds of simulations of the avalanche dynamics – calculated by using a 2D rapid mass movement simulator – indicate that the seismic signals were rather generated as the avalanche flowed through a narrow and twisting canyon directly above the hotel. Once the avalanche enters the canyon it is travelling at maximum velocity (37 m/s) and is twice strongly deflected by the rock sidewalls. These impacts created a distinct linearly polarized seismic “avalanche transient”; that can be used to time the destruction of the hotel. Our results demonstrate that seismic recordings combined with simulations of mass movements are indispensable to remotely monitor snow avalanches.

Mesospheric gravity-wave (GW) phase velocity spectra and total powers at two Antarctic stations, Davis and Syowa, were derived using OH airglow image data from March to October in 2016. The total powers have similar seasonal variation, that is, maxima in winter at both stations. However, the power at Davis was one standard deviation larger in winter and three times smaller in September than at Syowa. The total power at Davis in winter was mainly attributed to GWs with high eastward ( phase velocity. On the other hand, the higher total power at Syowa in September was attributed to GWs with omnidirectional phase velocity. These differences between Syowa and Davis can not be explained by the wind filtering effect, and other factors are needed. To further explorer the origin of the difference in winter, we focused on an event on August 29, 2016, in which GWs with ~100 ms-1 southeastward phase velocity appeared at Davis. The raytracing method was applied, and its result indicated that those GWs with high southeastward phase velocity propagated from ~45 km altitude over the southern ocean (~ where GWs with high amplitude and southeastward propagating emitted from the tropospheric jet appeared. These jet GWs were probably saturated in 45-50 km altitudes. Therefore, the GWs with eastward phase velocity were probably secondary gravity waves. This result suggests that the higher power in the eastward high phase velocity domain at Davis was contributed to by secondary GWs.

Aquatic vegetation modifies hydrodynamics, turbulence structure, sediment transport, and ecological processes in marine ecosystems. Recent turbulence models for vegetated flows have focused on open channel unidirectional flows. However, the unsteadiness and turbulent structure of oscillatory flows often prevent the direct application of such models in wave-dominated environments. We investigate Turbulent Kinetic Energy (TKE) connected to the flow structure in oscillatory flows through aquatic vegetation. Using an oscillatory tunnel, we test vegetation densities up to $\phi=0.10$ with wave periods between 2.1-5.3 s and wave amplitudes between 2-10 cm. Our measurements show a nonlinear relation between the TKE inside the canopy and vegetation density due to the change from the stem- to canopy-scale dominated regime. We observe that $ah\geq 0.8$ marks a threshold for this transition: a reduction of wake TKE inside the canopy and an increase of shear TKE at the top of the canopy. This transition is characterized by increasing frequency and intensity of sweeps and ejections near the bed and at the canopy top. We developed a two-equation predictor for TKE at the top of the canopy using the “short-cut” TKE transfer first proposed by \citeA{finnigan2000turbulence} where canopy-scale eddies convert TKE into stem-scale eddies via the work against vegetation drag. For near-bed TKE, we adapt \citeA{tanino2008lateral}’s model to predict the maximum TKE values on oscillatory flows. These two predictors provide easy-to-use tools suitable for wave-dominated environments to accurately estimate TKE levels inside the canopy for estimating sediment transport rates and mass exchange across the canopy.

Identification of repeating earthquakes (repeaters) usually depends on waveform similarity expressed as the corresponding cross-correlation coefficient (CC) above a prescribed threshold, typically ranging from 0.70 to 0.98. However, the robustness and effectiveness of such a strategy have never been thoroughly examined. In this study, we examine whether CC is a valid proxy for repeater identification through both synthetic and real earthquake experiments. We reveal that CC is controlled by not only the inter-event distance but also many other factors, including station azimuth, epicentral distance, velocity structure, etc. Consequently, CC lacks the resolution in identifying true repeaters. We propose a physics-based approach that considers both inter-event separation and rupture radius. For an event pair to be true repeaters, their inter-event separation must be smaller than the rupture radius of the larger event. Our results imply that a systematic recheck of previously identified repeaters and associated interpretations/hypotheses may be important and necessary.