Elevated nitrate concentrations in German water bodies are a widespread problem, potentially resulting from a long history of excess nitrogen (N) inputs. Here, we investigated long-term (1950-2014) N dynamics across 89 German catchments using a process-based model. Results showed that the mean fractions of N surplus (excess) exported to the river, removed by denitrification, accumulated in the soil zone, and accumulated in groundwater across all catchments are 27%, 58%, 14%, and 1%, respectively. Dissolved inorganic N in groundwater could affect the stream N levels over decades as indicated by long groundwater transit times. A cluster identified four catchment groups with distinct archetypal long-term N transport and retention dynamics, which can be partly linked to the catchments’ topographic and geological conditions. This hints at underlying mechanisms that explain spatial differences in the fate of diffuse N inputs to catchments and opens the possibility for better-targeted management

The air pollutant NO is derived largely from transportation sources, and is known to cause various respiratory diseases. Substantial reduction in transport and industrial processes around the globe stemming from the novel SARS-CoV-2 coronavirus and subsequent pandemic resulted in sharp declines in emissions, including for NO. Additionally, the COVID-19 disease that results from the coronavirus may present in its most severe form in those who have been exposed to high levels of air pollution and thus have various co-morbidities. To explore these links, we compared ground-based NOsensor data from 15 US cities from a one month window in 2019 versus the same window during shutdown in 2020. Levels of NO declined roughly 20-60% in 13 of the 15 cities in 2020, linked to similar declines in traffic volume in those cities. To broaden the spatial analysis beyond the individual ground-based monitors, satellite data for tropospheric NO was also analyzed, and was largely consistent with the ground measurements. Many of the cities studied had a substantial percentage of the population with various pre-existing conditions, and a relationship was found between NO levels, respiratory disease, and COVID-19 case counts. This finding indicates that substantial improvements in air pollution and health outcomes can be achieved quickly with local and state policy directives, perhaps leading to more population-level health resilience in the face of future pandemics.

The Filchner-Ronne Ice Shelf (FRIS) is characterized by moderate basal melt rates due to the near-freezing waters that dominate the wide southern Weddell Sea continental shelf. We revisited the region in austral summer 2018 with detailed hydrographic and noble gas surveys along FRIS. The FRIS front was characterized by High Salinity Shelf Water (HSSW) in Ronne Depression, Ice Shelf Water (ISW) on its eastern flank and an inflow of modified Warm Deep Water (mWDW) entering through Central Trough. Filchner Trough was dominated by Ronne HSSW-sourced ISW, likely forced by a recently intensified circulation beneath FRIS due to enhanced sea ice production in the Ronne polynya since 2015. Glacial meltwater fractions and tracer-based water mass dating indicate two separate ISW outflow cores, one hugging the Berkner slope after a two-year travel time, and the other located in the central Filchner Trough following a ~six year-long transit through the FRIS cavity. Historical measurements indicate the presence of two distinct modes, in which water masses in Filchner Trough were dominated by either Ronne HSSW-derived ISW (Ronne-mode) or more locally-derived Berkner-HSSW (Berkner-mode). While the dominance of these modes has alternated on interannual time scales, ocean densities in Filchner Trough have remained remarkably stable since the first surveys in 1980. Indeed, geostrophic velocities indicated outflowing ISW-cores along the trough’s western flank and onto Berkner Bank, which suggests that Ronne-ISW preconditions Berkner-HSSW production. The negligible density difference between Berkner- and Ronne-mode waters indicates that each contribute cold dense shelf waters to protect FRIS against inflowing mWDW.

Identifying fluid flow maldistribution in planar geometries is a well-established problem in subsurface science/engineering. Of particular importance to the thermal performance of Engineered (or “Enhanced”) Geothermal Systems (EGS) is identifying the existence of non-uniform (i.e., heterogeneous) permeability and subsequently predicting advective heat transfer. Here, machine learning via a Genetic Algorithm (GA) identifies the spatial distribution of an unknown permeability field in a two-dimensional Hele-Shaw geometry (i.e., parallel-plates). The inverse problem is solved by minimizing the L2-norm between simulated Residence Time Distribution (RTD) and measurements of an inert tracer breakthrough curve (BTC) (C-Dot nanoparticle). Principal Component Analysis (PCA) of spatially-correlated permeability fields enabled reduction of the parameter space by more than a factor of ten and restricted the inverse search to reservoir-scale permeability variations. Thermal experiments and tracer tests conducted at the mesoscale Altona Field Laboratory (AFL) demonstrate that the method accurately predicts the effects of extreme flow channeling on heat transfer in a single bedding-plane rock fracture. However, this is only true when the permeability distributions provide adequate matches to both tracer RTD and frictional pressure loss. Without good agreement to frictional pressure loss, it is still possible to match a simulated RTD to measurements, but subsequent predictions of heat transfer are grossly inaccurate. The results of this study suggest that it is possible to anticipate the thermal effects of flow maldistribution, but only if both simulated RTDs and frictional pressure loss between fluid inlets and outlets are in good agreement with measurements.

The Pacific Meridional Mode (PMM) exhibits a marked seasonal variability, with the strongest (weakest) variance in northern spring (fall). Such a phase locking feature is investigated through a combined observational and modeling study. Given the PMM perturbation, the wind induced latent heat flux anomaly leads to a strongest (weakest) heating on local sea surface temperature anomaly (SSTA) in MAM (SON) through positive wind-evaporation-SST feedback. The difference between MAM and SON lies on the strength and area of mean northeasterly trades. Experiments with a simple air-sea coupled model further demonstrate that a PMM-like SSTA perturbation grows much faster in MAM than in SON. The difference is primarily attributed to the seasonal mean wind, not mean SST condition. It is greatest strength and area of the mean northeasterly trade in MAM that leads to most efficient wind-evaporation-SST feedback and thus fastest PMM growth rate.

Effectively quantifying hot moments of nitrous oxide (N2O) emissions from agricultural soils is critical for managing this potent greenhouse gas. However, we are challenged by a lack of standard approaches for identifying hot moments, including (1) determining thresholds above which emissions are considered hot moments, and (2) considering seasonal variation in the magnitude and frequency distribution of net N2O fluxes. We used one year of hourly N2O flux measurements from 16 autochambers that varied in flux magnitude and frequency distribution in a conventionally tilled maize field in central Illinois, USA to compare three approaches to identify hot moment thresholds: 4x the standard deviation (SD) above the mean, 1.5x the interquartile range (IQR), and isolation forest (IF) identification of anomalous values. We also compared these approaches on seasonally subdivided data (early, late, non-growing seasons) vs. the whole year. Our analyses of the datasets revealed that 1.5x IQR method best identified N2O hot moments. In contrast, the 4 SD method yielded hot moment threshold values too high, and the IF method yielded threshold values too low, leading to missed N2O hot moments or low net N2O fluxes mischaracterized as hot moments, respectively. Furthermore, seasonally subdividing the dataset facilitated identification of smaller hot moments in the late and non-growing seasons when N2O hot moments were generally smaller, but it also increased hot moment threshold values in the early growing season when N2O hot moments were larger. Consequently, we recommend using the 1.5x IQR method on whole year datasets to identify N2O hot moments.

With the increasing volume of data generated in agriculture, developing guidelines for appropriate data sharing and management is essential. Founded in 2015, the AgBioData Consortium aims to identify the current major issues in data curation and management and ensure more Findable, Accessible, Interoperable, and Reusable (FAIR) data. In 2021 we received funding from the National Science Foundation for a Research Coordination Network grant, whose main goals are: (1) make recommendations and implementation plans for FAIR data in agriculture; (2) expand the AgBioData network by recruiting key stakeholders in agricultural research; (3) provide educational material to train researchers on FAIR data sharing; and (4) develop a roadmap for a sustainable genomic, genetic and breeding (GGB) Database Ecosystem. We have started by launching nine working groups covering different aspects of data management in agriculture and recruitment. We also plan new groups focused on sharing and archiving new types of data, such as those generated with high-throughput phenotyping platforms. As plant phenomics becomes a major part of agriculture, we foresee opportunities to collectively identify best data management practices in current and future phenomics repositories to ensure that phenomics data is FAIR from the start. We are welcoming new members with expertise in the field. If you are interested in joining, please visit our website (https://www.agbiodata.org).

Observations of atmospheric neutral density and associated dynamics within the lower thermosphere has been a challenge to the atmospheric science and space community due to its inaccessibility to balloon-borne or orbital in situ instruments. We present a methodology to study the latitudinal and temporal variation of neutral density in this region through a simultaneous campaign at geographically distinct high-power large-aperture (HPLA) radar facilities to observe meteor trajectories. These meteors are formed by meteoroids entering the Earth’s atmosphere; the deceleration they undergo due to atmospheric drag provides a source of information to determine the neutral density of the atmosphere at altitudes of 80 to 140 km. Through the measurement of meteor head echo trajectories using specialized radar waveforms and signal processing to enhance the range resolution, combined with novel statistical techniques to account for the distribution of meteoroid properties, the temporal evolution of atmospheric neutral density from 70 to 140 km can be characterized with sub-km altitude resolution. Initial results will be presented from the first of four observation campaigns planned during the 2019-2021 period using the HPLA radar sites at Jicamarca, Millstone Hill, and Resolute Bay, which span equatorial to polar latitudes at similar longitude. The simultaneous measurements across facilities complements other measurements of atmospheric composition and structure at similar altitudes to provide improved identification of latitudinal coupling and forcing from lower altitudes into the magnetosphere-ionosphere-thermosphere (MIT) system.

Harmful algal blooms (HAB; Karenia brevis) occurrences have been reported from the coastal waters of Charlotte County in southwest Florida. We developed multivariate regression models that relate reported (January 2010 to October 2017) bloom occurrences to observations extracted from archival remote sensing data (Moderate Resolution Imaging Spectroradiometer [MODIS]) to accomplish the following: (1) identify factors controlling HAB propagation, (2) predict algal bloom distribution (same day, and 1, 2, and 3 days in advance), and (3) develop fully automated system for data distribution via a web-based GIS platform. These tasks were accomplished through three main steps: (1) automatic downloading and processing of daily MODIS products using SeaDAS software to extract relevant remote sensing variables (euphotic depth, wind direction, ocean chlorophyll three-band algorithm for MODIS [Chlorophyll a OC3M], wind speed, chlorophyll a Generalized Inherent Optical Property [GIOP], Fluorescence Line Height [Flh], diffused attenuation coefficient for downwelling irradiance at 490 nanometer [Kd_490], chlorophyll a Garver-Siegel- Maritorena [GSM], Turbidity index, Particulate backscattering coefficient at 547 nm [bbp_547_giop] and sea surface temperature [SST]), (2) development and calibration of multivariate regression models using relevant remote sensing and static variable (distance from river mouth, bathymetry) inputs for same day mapping and forecasting of HAB occurrences, and (3) automated posting of model outputs on a web-based GIS (http://mgs.geology.wmich.edu/bloom/). Findings include: (1) the variables most indicative of the timing of bloom propagation are bathymetry, euphotic depth, wind direction, sea surface temperature [SST], chlorophyll a [OC3M] and distance from the river mouth, and (2) the model predictions were successful at 90% for same day mapping and 65%, 72% and 71% for the one, two and three days in advance predictions, respectively.

We present a comparison of atmospheric transport model simulations for carbonyl sulfide (COS), within the framework of the ongoing atmospheric tracer transport model intercomparison project “TransCom”. Seven atmospheric transport models participated in the inter-comparison experiment and provided simulations of COS mixing ratios in the troposphere over a 9-year period (2010–2018), using prescribed state-of-the-art surface fluxes for various components of the atmospheric COS budget: biospheric sink, oceanic source, sources from fire and industry. Since the biosphere is the largest sink of COS, we tested sink estimates produced by two different biosphere models. The main goals of TransCom-COS are (a) to investigate the impact of the transport uncertainty and emission distribution in simulating the spatio-temporal variability of COS mixing ratios in the troposphere, and (b) to assess the sensitivity of simulated tropospheric COS mixing ratios to the seasonal and diurnal variability of the COS biosphere fluxes. To this end, a control case with state-of-the-art seasonal fluxes of COS was constructed. Models were run with the same fluxes and without chemistry to isolate transport differences. Further, two COS flux scenarios were compared: one using a biosphere flux with a monthly time resolution and the other using a biosphere flux with a three-hourly time resolution. In addition, we investigated the sensitivity of the simulated concentrations to different biosphere fluxes and to indirect oceanic emissions through dimethylsulfide (DMS) and carbon disulfide (CS2). The modelled COS mixing ratios were assessed against in-situ observations from surface stations and aircraft.

Sea ice production within polynyas, an outcome of the atmosphere - ice - ocean interaction, is a major source of dense water and hence key to the global overturning circulation, but is poorly quantified over open-ocean polynyas. Using the two recent extensive open-ocean polynyas within the wider Maud Rise region of the Weddell Sea in 2016 and 2017, we here explore the surface ice energy budget and estimate their ice production based on satellite retrievals, in-situ hydrographic observations, and the Japanese 55-year Reanalysis (JRA55). We find that the oceanic heat flux amounts to 36.1 and 30.7 W m-2 within the 2016 and 2017 polynyas, respectively. We find that the 2017 open-ocean polynya produced nearly 200 km3 of new sea ice, which is comparable to the production in the largest Antarctic coastal polynyas. Finally, we find that ice production is highly correlated with the 2 m air temperature and wind speed, which affect the turbulent fluxes. It is also highly sensitive to uncertainties in the atmospheric air temperature and mixed layer depth, which urgently need to be better monitored at high latitudes.

Sustainable aviation fuels (SAF) produced from lipid feedstocks are an increasingly mature and low-cost option for aviation sector decarbonization. Ethiopian mustard (Brassica carinata) is a non-food oilseed crop that can be grown on winter fallow land in the southeastern US and used as a feedstock for SAF, with a high-protein livestock feed co-product. Integrating carinata into existing annual crop rotations produces an additional revenue stream for landowners, with potential co-benefits for soil carbon and other ecosystem services. The Southeast Partnership for Advanced Renewables from Carinata (SPARC) is a USDA-funded research consortium to advance carinata production and associated SAF and bioproduct supply chains in the region. A SPARC research team used the DayCent ecosystem model to estimate the potential production of carinata across the tri-state region of Alabama, Florida, and Georgia, and assess associated changes in soil carbon storage and emissions of nitrous oxide (N2O), the main biogenic greenhouse gas (GHG) emissions from agriculture. First, we calibrated DayCent to reproduce the phenology, harvest index, productivity response to nitrogen application, root-to-shoot biomass ratio, and tissue nitrogen content data observed for a set of carinata field trials in the region. Next, we simulated the integration of carinata into a typical cotton/peanut rotation across the 2.3 million hectares of annual cropland within the climate suitability range for this crop, grown once every third winter. We show an annual production potential of greater than 1 billion liters of SAF from this feedstock in the region. Our base carinata management case is approximately neutral in biogenic GHG emissions, with modest soil carbon sequestration that offsets the associated small increase in N2O emissions. However, adopting conservation management practices such as no-till establishment or poultry litter soil amendments results in a more substantial net soil carbon sink, reducing the GHG footprint of carinata-derived SAF by up to 20 grams of CO2-equivalent per megajoule of fuel. This work supports SPARC’s ongoing efforts to develop improved crop varieties and management practices that simultaneously improve the economics and ecosystem service value of carinata production.

Numerical mixing, defined here as the physically spurious diffusion of tracers due to the numerical discretization of advection, is known to contribute to biases in ocean circulation models. However, quantifying numerical mixing is non-trivial, with most studies utilizing specifically targeted experiments in idealized settings. Here, we present a precise, online water-mass transformation-based method for quantifying numerical mixing that can be applied to any conserved variable in global general circulation models. Furthermore, the method can be applied within individual fluid columns to provide a spatially-resolved metric. We apply the method to a suite of global ocean-sea ice model simulations with differing grid spacings and sub-grid scale parameterizations. In all configurations numerical mixing drives across-isotherm heat transport of comparable magnitude to that associated with explicitly-parameterized mixing. Numerical mixing is prominent at warm temperatures in the tropical thermocline, where it is sensitive to the vertical diffusivity and resolution. At colder temperatures, numerical mixing is sensitive to the presence of explicit neutral diffusion, suggesting that much of the numerical mixing in these regions acts as a proxy for neutral diffusion when it is explicitly absent. Comparison of equivalent (with respect to vertical resolution and explicit mixing parameters) $1/4^\circ$ and $1/10^\circ$ horizontal resolution configurations shows only a modest enhancement in numerical mixing at $1/4^\circ$. Our results provide a detailed view of numerical mixing in ocean models and pave the way for future improvements in numerical methods.

Subglacial models represent moulins as cylinders or cones, but field observations suggest the upper part of moulins in the Greenland Ice Sheet have more complex shapes. These more complex shapes should cause englacial water storage within moulins to vary as a function of depth, a relationship not currently accounted for in models. Here, we use a coupled englacial--subglacial conduit model to explore how moulin shape affects depth-dependent moulin water storage and water pressure dynamics within a subglacial channel. We simulate seven different moulin shapes across a range of moulin sizes. We find that the englacial storage capacity at the water level is the main control over the daily water level oscillation range and that depth-varying changes in englacial water storage control the temporal shape of this oscillation. Further, the cross-sectional area of the moulin within the daily oscillation range, but not above or below this range, controls pressures within the connected subglacial channel. Specifically, large cross-sectional areas can dampen daily to weekly oscillations that occur in the surface meltwater supply. Our findings suggest that further knowledge of the shape of moulins around the equilibrium water level would improve englacial storage parameterization in subglacial hydrological models and aid predictions of hydro-dynamic coupling.

River channels store large volumes of water globally, critically impacting ecological and biogeochemical processes. Despite the importance of river channel storage, there is not yet an observational constraint on this quantity. We introduce a 26-year record of entirely remotely sensed volumetric channel water storage anomaly (VCWS) on 26 major world rivers. We find mainstem VCWS climatology amplitude (VCWSCA) represents an appreciable amount of basin-wide terrestrial water storage variability (median 2.2%, range 0.05-13.8% across world rivers), despite the fact that mainstem rivers themselves represent an average of just 0.2% of basin area. We find that two global river routing schemes coupled with land surface models reasonably approximate VCWSCA (within {plus minus}50%) in only 19.2 % and 23.1 % of rivers considered (by model). These findings demonstrate VCWS is a useful measurement for assessing global hydrological model performance, and for advancing understanding of spatial patterns in global hydrology.

Cyclostratigraphy’s near 100% success rate in statistical cycle identification suggests confirmation bias; absence of cyclicity is not regarded as a possible outcome. Vaughan et al 2011 (VBS) showed that the usual methods of estimating confidence levels (CLs) admit numerous false cycle detections, but in subsequent debate it is asserted that the corrections recommended by VBS do not apply in cyclostratigraphy because they lead to rejection of the expected orbital periods. Is there a deeper problem? VBS particularly criticised universal failure to correct CLs for the unavoidably multiple nature of significance tests of power spectra. However, the multiple-test problem is compounded by assumptions of unlimited freedom to vary procedures to allow for properties of individual datasets. Statistical analysis in cyclostratigraphy operates in a large variable-space, both of target hypotheses (many orbital cycles and combinations thereof), and of procedures (many pre- and syn-processing options). Each of the many data-contingent choices made before and during spectral analysis and significance-testing implies the existence of alternatives: in effect, the reported analysis is only one of many. Given that multiple experiments will eventually achieve a positive result purely by chance, unadjusted significance thresholds will result in large numbers of spurious cycle identifications, a possible explanation for observed success rates. Additional multiplicity is implied by the practice of treating CLs as a guide, rather than as a definitive signal:noise discriminator; treating CLs as movable (or even optional) negates the concept that the particular dataset is just one realisation of many permitted by the noise model; without pre-selection of a CL the statistics are meaningless. Suggestions for practical improvements include: better hypothesis formulation (with attention to the prior probability of signal preservation in an unreliable recording medium); more care in discriminating between the exploratory (hypothesis-setting) and confirmatory (hypothesis-testing) modes of data analysis; advance definition of analytical protocols; and publication of all results whether positive or negative. Reference: Vaughan et al 2011: doi:10.1029/2011PA002195.

We examine the wave coupling efficiency of solar wind to magnetospheric fluctuations in the ULF frequency range using an advanced full-wave simulation code, Petra-M. Earth’s magnetic field is tilted to the ecliptic plane; thus, compressional wave sources can be incident over a wide range of magnetic latitudes. When compressional waves are incident at a low latitude, very little wave power can reach the inner magnetosphere. On the other hand, waves incident from a high latitude source can propagate efficiently into the inner magnetosphere and reach the ground near the cusp region. The mode-conversion and polarization reversal at the crossover plays a critical role in compressional wave propagation. The mode-converted linearly polarized electromagnetic ion cyclotron waves also occur at the Alfvén and ion-ion hybrid resonances. Therefore, the results suggest that solar wind compression can drive the linearly polarized EMIC waves, and the wave occurrence can have seasonal and diurnal dependence.

Previous studies have shown that Strong Thermal Emission Velocity Enhancement (STEVE) events occur at the end of a prolonged substorm expansion phase. However, the connection between STEVE occurrence and substorms and the global high-latitude ionospheric electrodynamics associated with the development of STEVE and non-STEVE substorms are not yet well understood. The focus of this paper is to identify electrodynamics features that are unique to STEVE events through a comprehensive analysis of ionospheric convection patterns estimated from SuperDARN plasma drift and ground-based magnetometer data using the Assimilative Mapping of Geospace Observations (AMGeO) procedure. Results from AMGeO are further analyzed using principal component analysis and superposed epoch analysis for 32 STEVE and 32 non-STEVE substorm events. The analysis shows that the magnitude of cross-polar cap potential drop is generally greater for STEVE events. In contrast to non-STEVE substorms, the majority of STEVE events investigated accompany with a pronounced extension of the dawn cell into the pre-midnight subauroral latitudes, reminiscent of the Harang reversal convection feature where the eastward electrojet overlaps with the westward electrojet, which tends to prolong over substorm expansion and recovery phases. This is consistent with the presence of an enhanced subauroral electric field confirmed by previous STEVE studies. The global and localized features of high-latitude ionospheric convection associated with optical STEVE events characterized in this paper provide important insights into cross-scale magnetosphere-ionosphere coupling mechanisms that differentiate STEVE events from non-STEVE substorm events.