This paper introduces the special collection in Geophysical Research Letters and Journal of Geophysical Research: Atmospheres on the exceptional stratospheric polar vortex in 2019/2020. Papers in this collection show that the 2019/2020 stratospheric polar vortex was the strongest, most persistent, and coldest on record in the Arctic. The unprecedented Arctic chemical processing and ozone loss in spring 2020 has been studied using numerous satellite and ground-based datasets and chemistry-transport models. Quantitative estimates of chemical loss are broadly consistent among the studies and show profile loss of about the same magnitude as in the Arctic in 2011, but with most loss at lower altitudes; column loss was comparable to or larger than that in 2011. Several papers show evidence of dynamical coupling from the mesosphere down to the surface. Studies of tropospheric influence and impacts link the exceptionally strong vortex to reflection of upward propagating waves, and show coupling to tropospheric anomalies including extreme heat, precipitation, windstorms, and marine cold air outbreaks. Predictability of the exceptional stratospheric polar vortex in 2019/2020 and related predictability of surface conditions are explored. The exceptionally strong stratospheric polar vortex in 2019/2020 highlights the extreme interannual variability in the Arctic winter/spring stratosphere and the far-reaching consequences of such extremes.

Taking machine learning models from conceptualization to production is a complex and often time-consuming practice. Solar flare prediction is a central problem in space weather forecasting and has piqued the interest of many researchers in recent years. The prediction efforts have been catalyzed by the recent advancements in machine learning and deep learning methods and the experimental results show notable performance improvements. On the other hand, operationalizing these models and building well-documented, reliable cyberinfrastructure from them remains to be a challenging issue. We will present an example training and deployment scenario for a solar flare prediction system prototype with two different modes of prediction: full-disk and active region-based. We will demonstrate the challenges we faced during the development lifecycle including the data preprocessing and integration, model training and optimization, validation, and reporting. We will also show the results from our hybrid-mode flare prediction method and factors impacting the real-life performance of our cyberinfrastructure services. The poster is available here: AGU Fall Meeting 2021.

The Eocene-Oligocene Transition (EOT) marks the shift from greenhouse to icehouse conditions at 34 Ma, when a permanent ice sheet developed on Antarctica. Climate modeling studies have recently assessed the drivers of the transition globally. Here we revisit those experiments for a detailed study of the southern high latitudes in comparison to the growing number of mean annual sea surface temperature (SST) and mean air temperature (MAT) proxy reconstructions, allowing us to assess proxy-model temperature agreement and refine estimates for the magnitude of the pCO2 forcing of the EOT. We compile and update published proxy temperature records on and around Antarctica for the late Eocene (38-34 Ma) and early Oligocene (34-30 Ma). Compiled SST proxies cool by up to 3°C and MAT by up to 4°C between the timeslices. Proxy data were compared to previous climate model simulations representing pre- and post-EOT, typically forced with a halving of pCO2. We scaled the model outputs to identify the magnitude of pCO2 change needed to drive a commensurate change in temperature to best fit the temperature proxies. The multi-model ensemble needs a 30 or 33% decrease in pCO2, to best fit MAT or SST proxies respectively, a difference of just 3%. These proxy-model intercomparisons identify pCO2 as the primary forcing of EOT cooling, with a magnitude (-200 or -243 ppmv) approaching that of the pCO2 proxies (-150 ppmv). However individual model estimates span -66 to -375 ppmv, thus proxy-model uncertainties are dominated by model divergence.

The Nordland region, Northern Norway, situated in an intraplate continental setting, has the highest seismicity rate in mainland Norway. Better understanding of factors that influence the seismicity in Nordland can help increase knowledge of intraplate seismicity in general. However, the exact cause of seismicity in this region is still debated. Here, we address this problem with using a high-resolution 3-D VPand VP/VS ratio images of the crust in Nordland using seismic travel-time tomography. These images show the existence of a localized, 10 - 15 km Moho step that runs parallel to the coast. The north-south extent of this step coincides with the region that exhibits the highest rates of seismicity. Focal mechanisms of selected earthquakes computed in this study are dominated by normal and oblique-normal, indicating a coast-perpendicular extension. The coast-perpendicular extensional stress regime deviates from the regional compression imposed by the ridge push from the North Atlantic. This deviation is thought to stem from the additional interference with local flexural stress caused by sediment redistribution and glacial isostatic adjustment, and possibly exacerbated by gravitational potential energy stress associated with the Moho jump. The deformation due to the extensional regime is localized on pre-existing faults and fractures along the coastline. The tomography result shows that two distinct seismic swarms occurred in the coastal area with low VP and variable VP/VS ratio anomalies, pointing towards fractured crust and possibly the presence of fluids. The existence of fluids here can change the differential stress and promote seismic rupture.

Hydrologic signatures are quantitative metrics that describe a streamflow time series. Examples include annual maximum flow, baseflow index and recession shape descriptors. In this paper, we use machine learning (ML) to learn an optimal equivalent of hydrologic signatures, and use the learnt signatures to build rainfall-runoff models in otherwise ungauged watersheds. Our model has an encoder-decoder structure. The encoder is a convolutional neural net mapping historical flow and climate data to a low-dimensional vector encoding describing watershed function. The encodings are analogous to hydrological signatures. The decoder uses a process-informed network structure to predict streamflow based on current climate data, stored watershed state, static watershed attributes and the encoding. The decoder structure includes stores and fluxes similar to a classical hydrologic model. For each timestep, the decoder predicts coefficients that distribute precipitation between stores and store outflow coefficients. The model is trained end-to-end on the U.S. CAMELS watershed dataset to minimize streamflow error . Using learnt signatures as input to the process-informed model improves prediction accuracy over benchmark configurations that use classical signatures or no signatures. Median NSE performance on 100 watersheds excluded from the training set was 0.69. The process-informed model structure simulates hydrologic dynamics such as snow accumulation and melt, quickflow and baseflow. We interpret learnt signatures by correlation with classical signatures, and by using sensitivity analysis to assess their impact on modeled store dynamics. We conclude that process-informed ML models and other applications using hydrologic signatures may benefit from replacing expert-selected signatures with learnt signatures.

Laboratory studies have shown that rhizodeposits could lead to either soil structural formation or dispersion depending on plant species, soil conditions, and microbial activity. However, these studies have usually been conducted in dry soils and rarely considered the combined effect of rhizodeposit and organic residues on soil structure. This study hypothesizes that root exudates promote soil dispersion initially, but over time decomposition of root exudates produce binding agents that promote stable soil structure in the rhizosphere. To test this hypothesis, a sandy loam soil sieved to < 500 µm particle size was first amended with root exudate compounds (14.4 mg C g-1), δ13C-barley residue (0.44 mg C g-1 soil), or both. Six replicate samples per treatment were packed in cores to a bulk density of 1.27 g cm-3 and then equilibrated on a tension table at -2 kPa matric potential. Rheological measurements of flow characteristics (dynamic viscosity) and strength (storage modulus, loss modulus, tan δ, and yield stress) of the control and amended soils were obtained immediately after amendment and after twelve days of incubation at 20 oC. Only root exudate compounds initially decreased the capacity of soil to retain water at -2 kPa by 21% and by 49% after incubation. Likewise, the yield stress of root exudate amended soil was significantly (P < 0.05) lower than that of the unamended soil, reflecting dispersion of soil. However, microbial decomposition/activities significantly (P < 0.05) increased yield stress over the corresponding pre-incubation values for these treatments by 200% (root exudate) and 230% (root exudate + δ13C-barley residue). These results confirmed the hypothesized dual effect of root exudates on rhizosphere structure. The initial soil dispersion may facilitate root growth by augmenting soil penetrability and releasing nutrients that were occluded in soil aggregates, whereas stable soil structure is achieved upon decomposition of root exudates.

Evapotranspiration (ET) is a critical process that regulates the heat and water transfer between the land and atmosphere. Instantaneous satellite data can provide area-wide daily ET measurements. Surface energy budgets used in satellite-based calculations generally overpredict daily net radiation and the related ET. Our objective was to improve the accuracy of satellite-based daily evapotranspiration by correcting the overprediction of net radiation. To do so, we introduced a routine for calculating the downward longwave radiation on cloudy days. This new algorithm removed the upward bias in the predicted net radiation at seven ChinaFlux sites. Then, using previously developed methods for converting instantaneous measurement into daily averages, we found that evapotranspiration rates were predicted more accurately than existing methods at the same ChinaFlux sites. In addition, our evapotranspiration rates compared well with watershed ET and other calibrated and validated spatiotemporal gridded data products in China

Water is scarce in semi-arid and arid areas where urban irrigation consumes a large portion of city water. It is important to manage and conserve water properly to meet the growing demand, especially in the summer season. The purpose of this research is to identify the major contributors to streamflow in the semi-arid Denver metropolitan area, CO, USA, and analyze the temporal variation of streamflow sources using two-year data. In this study, water-stable isotopes (δ18O and δ2H) were used as the tracer to identify the contribution of different sources such as tap water, precipitation, or irrigation water in Denver urban streamflow. Stream and tap water were sampled every other week and precipitation samples were collected once a month. There were 13 urban and 6 grassland streams, and tap water was collected from 6 different water providers. The USGS real-time streamflow data and BaseflowSeparation function in R package ‘EcoHydRology’ were used to select the baseflow condition in the streams for sampling. Picarro L2130i Laser Water Isotope Analyzer was used for oxygen and hydrogen isotope (δ18O and δ2H) analysis of stream, tap, and precipitation water. Results showed that precipitation samples were heavier in earlier summertime than the late summer and 2019 showed greater variability than 2021. Tap samples showed temporal and spatial variability in δ18O and δ2H values. Less variability in tap isotopic data could be observed in 2021 than in 2019. Centennial Water and Sanitation District showed a decreasing trend. In the δ18O vs. δ2H plot, stream and tap water followed local meteoric water line (LMWL) and global meteoric water line (GMWL) well. But precipitation sample exhibited a slight deviation from the LMWL and GMWL. The similar isotopic range in tap water and stream water supported that tap water was the main source of water during the summertime. In the future, the percent contribution of different sources will be evaluated. Furthermore, the effect of reduced urban irrigation by using efficient irrigation, landscape, or conservation techniques will be analyzed to achieve water security for sustainable urban development.

On Mars, in the present or past, highly concentrated liquid brine is suggested to have formed repeatedly through ice melting and/or deliquescence. Such repeated seepages of brine could generate characteristic geomorphic features on Mars; however, the dynamics of repeated brine flows have been little investigated. Here we report results of laboratory experiments that investigate the flow behavior of brine (MgCl2 solution) upon repeated cycles of seepage and drying in glass beads on slopes. We compare the flow behavior with those of ultrapure water and ethylene glycol. We found that at a low flow rate, comparable to ice melting on Mars, both ultrapure water and ethylene glycol infiltrate radially into glass beads as infiltration flows. In contrast, even at a low flow rate, surface flows appear due to repeated seepages of MgCl2 solution. The surface flows move downward on slopes, forming elongated flow features. This happens because a decrease in the porosity and permeability of the glass beads caused by precipitated salts prevents infiltration in subsequent brine seepages. We suggest that precipitated salts have a role in the formation of transient surface flows on Mars, even at low seepage rates.

Key Points: 8 • The change in electron kinetic entropy per particle is calculated for 22 shock cross-9 ings having wide range of shock conditions 10 • The entropy change displays a strong dependence on the electron beta parame-11 ter 12 • The entropy change corresponds to an adiabatic index γ e = 1.595 ± 0.036 13 Corresponding author: Martin Lindberg, [email protected] 14 We use Magnetospheric Multiscale (MMS) data to study electron kinetic entropy across 15 Earth’s quasi-perpendicular bow shock. We have selected 22 shock crossings covering a 16 wide range of shock conditions. Measured distribution functions are calibrated and cor-17 rected for spacecraft potential, secondary electron contamination, lack of measurements 18 at the lowest energies and electron density measurements based on the plasma frequency 19 measurements. The change in electron kinetic entropy per particle is calculated for 22 20 shock crossings. 20 out of 22 crossings display an increase in the electron kinetic entropy 21 per particle ranging between 0.1-1.4 k B while two crossings display a slight decrease of 22-0.06 k B. We observe that the change in electron kinetic entropy, ∆S e , displays a strong 23 dependence on the change in electron temperature, ∆T e , and the upstream electron plasma 24 beta, β e. Shocks with high ∆T e are found to have high ∆S e. Shocks with low upstream 25 electron plasma betas are associated to higher ∆S e than shocks with large electron plasma 26 beta. We show that the calculated entropy per particle is strictly less than the maximum 27 state of entropy obtained using a Maxwellian distribution function. The resulting change 28 in electron kinetic entropy per particle ∆S e , density ∆n e and temperature ∆T e is used 29 to determine a value for the adiabatic index of electrons. We find that an adiabatic in-30 dex of γ e = 1.595 ± 0.036 describes the observations best.

In this study, we examine the scaling of earthquake waiting time distributions in Northern Chile. For this purpose, we analyzed 7-yr high spatial resolution and low complete- ness magnitude IPOC seismic catalog and 43-yr USGS catalog. A unified earthquake moment-space-time scaling relation is empirically evaluated by calculating waiting times for different ranges of magnitude and epicentral area linear dimension and estimating the associated scaling coefficients, β analog to b-value and γ, the correlation fractal dimension. We find a scaling function that can be characterized with 3 distinct regions, regions whose behaviour depends on whether seismicity is in the coastal area or at intermediate depth. Moreover, high resolution localizations from the IPOC catalog allows us to further observe differences: the lower plane seismicity of the double seismic zone behaves just like that at intermediate depth , while the crustal upper plate and the upper plane from the double seismic zone seismicity behaves more like interface seismicity. Thus, the earthquake waiting time distribution primarily depends on whether seismicity is located in a crust-on-crust or in a crust-mantle system: having respectively high/low correlated behaviour in the short scale region, non-exponential/exponential decay in the transition middle region and, in all cases, long-term clustering with a slower than exponential decay in the long scale. 

The COVID-19 pandemic affected the scientific workforce in many ways. Many worried that stay-at-home orders would disproportionately harm the productivity and well-being of women and early-career scientists, who were expected to shoulder more childcare, homeschooling, and other domestic duties while also interrupting field and lab research, essential for career advancement. AGU journal submission and author and reviewer demographic data allowed us to investigate the effect the pandemic may have had on many Earth and space scientists, especially on women and early career scientists. However, we found that submissions to AGU journals increased during the pandemic as did total submissions from women (with no difference in the proportion). Although the rate at which women agreed to review decreased slightly (down 0.5%), women still made up a larger proportion of agreed reviewers during the pandemic compared to two years earlier. Little difference was seen overall in median times to complete reviews except with women in their 40s and 70s, suggesting that they were affected more during the pandemic than other age and gender groups. Although AGU’s data do not show that the effects of the pandemic decreased the participation of women in AGU journals, the lag between research and writing/submitting may still be seen in later months. The stay-at-home orders may also have allowed people to devote time to writing up research conducted pre-pandemic; writing too can be done at down-time hours, which may have supported the increase in submissions to and reviews for AGU journals.

Past temperature reconstructions from polar ice sheets are commonly based on stable water isotope records in ice-cores. However, despite major efforts in the understanding of the ice-core signal formation, the temperature reconstructions of the last millennium in Antarctica remain highly uncertain. Here, using a 100 km scale representative surface water isotope dataset, we show that the spatial variability of local surface topography and accumulation rate anomalies influences the isotopic composition of the upper-meter snowpack. The magnitude of this non-temperature effect on water isotopes is similar to changes of the last millennium. We demonstrate that these spatial anomalies are advected into the deeper firn and ice column, and are translated into an artificial centennial to millennial scale variability in the isotope record. Additionally, we provide an estimation of areas where this effect is relevant for last millennium temperature reconstructions.

A series of survey protocol for deep sea polymetallic sulfides using not electromagnetic waves but acoustic, electromagnetic, gravity and magnetic, and geochemical methods has been proposedby Japanese national project, the Next-Generation Technology for Ocean Resources Exploration (Zipangu in the Ocean Program) launched in 2014, which enables effective and efficient explorations. The first version of the survey protocol has been made to determine the potential polymetallic sulfides areas using detailed scientific researches on their genesis. Deep sea polymetallic sulfides are produced beneath the sea floor because of the interaction between sea water and sub-bottom hydrothermal solutions driven from volcanic magma, indicating that they must be discovered at mid-ocean ridges, back-arc and arc regions, and hot spots. Recent studies further show that back-arc basins such as Okinawa Trough probably produce deep sea polymetallic sulfides bodies much larger than other regions (Tsuji et al., 2012). Some interesting topics on the survey protocol which further constrains the candidate areas, include acoustic, natural electric potential and seismic survey methods, that are 1) the shipboard Multi Beam Echo Sounding (MBES) survey that can acoustically detect hydrothermal plume including abundant CO2 for regional survey (100km2 <), 2) continuous self-potential measurements with tremendously low noise in the sea water that can identify individual sulfide deposit for semi-detailed survey (10km2- 100km2), and 3) state of the art vertical cable seismic (VCS) system which enables to delineate detailed structure of hydrothermal ore deposit body for detailed survey (< 10km2). These three methods, in combination with other geophysical and geochemical surveys, have been turned out to be effective and efficient for surveying deep sea polymetallic sulfides. Multiple types of data would be integrated into a single visualization to gain a clear view of the structure of the sulfides. Integrating core lithologies obtained via drilling with the seismic reflection structure data has been found to show the seismic velocity structure along with the extent of the polymetallic sulfides bodies.

The Earth’s outer radiation belt response to geospace disturbances is extremely variable spanning from a few hours to several months. In addition, the numerous physical mechanisms, which control this response, depend on the electron energy, the time-scale and the various types of geospace disturbances. As a consequence, the various models that currently exist are either specialized, orbit-specific data-driven models, or sophisticated physics-based ones. In this paper we present a new approach for radiation belt modelling using Machine Learning methods driven solely by solar wind speed and pressure, Solar flux at 10.7 cm and the $\theta$ angle controlling the Russell-McPherron effect. We show that the model can successfully reproduce and predict the electron fluxes of the outer radiation belt in a broad energy (0.033–4.062 MeV) and L-shell (2.5–5.9) range and, moreover, it can capture the long-term modulation of the semi-annual variation. We also show that the model can generalize well and provide successful predictions, even outside of the spatio-temporal range it has been trained with, using >0.8 MeV electron flux measurements from GOES-15/EPEAD at geostationary orbit.

For the first time, model-derived and imagery-derived wind directions and speeds have been compared in Mars’ south polar region. Seasonal fan-shaped deposits are routinely observed by HiRISE in the polar regions. They are widely accepted to result from CO 2 gas jet eruptions. Fan lengths, sizes, and shapes can provide information about wind directions and strengths at the times such eruptions occur. We utilize a catalog of those fan-shaped deposits, marked by citizen scientists within the framework of the Planet Four (P4) project, at 27 regions of interest (ROIs) for two spring seasons (Mars Year 29 and 30). Fans change considerably from one HiRISE image to another at most of these ROIs as wind direction changes over the spring season. Leveraging this characteristic, intraseasonal variations in near-surface wind speeds and directions were retrieved and compared to near-surface winds predicted by a mesoscale atmospheric model (MRAMS) at the same ROIs. At most ROIs P4-inferred wind directions are consistent with those from MRAMS. The P4-derived wind speeds are less constrained, but are consistent with MRAMS wind speeds at the majority of ROIs. The overall consistency between the P4-inferred and MRAMS wind directions supports the underlying assumption that fan formation is controlled by the wind, and is not simply due to ballistic trajectories of material exiting suitably non-vertical vents. Measurements of seasonal fan-shaped deposits in HiRISE imagery can thus provide important intraseasonal information about near-surface winds-invaluable for both validating climate modeling and quantitatively investigating Mars polar processes.

The Southern Ocean (SO) provides the largest oceanic sink of carbon. Observational datasets highlight decadal-scale changes in SO CO2 uptake, but the processes leading to this decadal-scale variability remain debated. Here, using an eddy-permitting ocean, sea-ice, carbon cycle model, we explore the impact of changes in Southern Hemisphere (SH) westerlies on contemporary (i.e. total), anthropogenic and natural CO2 fluxes using idealised sensitivity experiments as well as an interannually varying forced (IAF) experiment covering the years 1948 to 2007. We find that a strengthening of the SH westerlies reduces the contemporary CO2 uptake by leading to a high southern latitude natural CO2 outgassing. The enhanced SO upwelling and associated increase in Antarctic Bottom Water decrease the carbon content at depth in the SO, and increase the transport of carbon-rich waters to the surface. A poleward shift of the westerlies particularly enhances the CO2 outgassing south of 60S, while inducing an asymmetrical DIC response between high and mid southern latitudes. Changes in the SH westerlies in the 20th century in the IAF experiment lead to decadal-scale variability in both natural and contemporary CO2 fluxes. The ~10% strengthening of the SH westerlies since the 1980s led to a 0.016 GtC/yr^2 decrease in natural CO2 uptake, while the anthropogenic CO2 uptake increased at a similar rate, thus leading to a stagnation of the total SO CO2 uptake. The projected poleward strengthening of the SH westerlies over the coming century will thus reduce the capability of the SO to mitigate the increase in atmospheric CO2.

The oxygen and hydrogen isotopic composition in snow and ice have long been utilized to reconstruct past temperatures of polar regions, under the assumption that post-depositional processes such as sublimation do not fractionate snow. In low-accumulation (<0.01 m yr-1) areas near the McMurdo Dry Valleys in Antarctica surface snow and ice samples have negative deuterium excess values (δD - 8*δ18O). This unique phenomenon, only observed near the Dry Valleys, is not fully understood. Here we use both an isotope-enabled general circulation model and an ice physics model and establish that negative deuterium excess values can only arise from precipitation if the majority of the moisture is sourced from the Southern Ocean. However, the model results show that moisture sourced from oceans north of 55°S contributes significantly (>50%) to precipitation in Antarctica today. We thus propose that sublimation must have occurred to yield the negative deuterium excess values in snow observed in and near the Dry Valleys and that solid-phase-diffusion in ice grains is sufficiently fast to allow Rayleigh-like isotopic fractionation in similar environments. We calculate that under present-day conditions at the Allan Hills outside the Dry Valleys, 3 to 24% of the surface snow is lost due to sublimation. Because a higher fraction of snow is expected to be sublimed when accumulation rates are lower, the magnitude of δ18O and δD enrichment due to sublimation will be higher during past cold periods than at present, altering the relationship between the snow isotopic composition and polar temperatures.