Aerosols interact with radiation and clouds. Substantial progress made over the past 40 years in observing, understanding, and modeling these processes helped quantify the imbalance in the Earth’s radiation budget caused by anthropogenic aerosols, called aerosol radiative forcing, but uncertainties remain large. This poster presents the outcome of an international workshop and subsequent review paper, which quantify the likely range of aerosol radiative forcing over the industrial era based on multiple lines of evidence, including modelling approaches, theoretical considerations, and observations. Improved understanding of aerosol absorption and the causes of trends in surface radiative fluxes narrow the range of the forcing from aerosol-radiation interactions compared to the latest assessment by the Intergovernmental Panel on Climate Change (IPCC). A robust theoretical foundation and convincing evidence constrain the forcing caused by aerosol-driven increases in liquid cloud droplet number concentration. However, the influence of anthropogenic aerosols on cloud liquid water content and cloud fraction and on mixed-phase and ice clouds remains poorly constrained. Observed changes in surface temperature and radiative fluxes provide additional constraints. These multiple lines of evidence lead to total aerosol radiative forcing ranges that are of similar width to the last IPCC assessment but more clearly based on physical arguments.

Low-cost air quality monitors (LCAQMs) are promising supplements to regulatory monitors for PM2.5 exposure assessment. However, the application of LCAQM in spatially extensive exposure modeling is hindered by the difficulty in performing calibration at large spatial scales and the adverse influence of LCAQM residual uncertainty after calibration. We aimed to develop an efficient spatially scalable calibration method for LCAQM and design a residual uncertainty-derived down-weighting strategy to optimize the use of LCAQM data with regulatory monitoring data in PM2.5 modeling. In California, for each monitor from PurpleAir, a global LCAQM network, we identified a station within a 500-m radius from the Air Quality System (AQS), a U.S. regulatory monitoring network. Regional calibration of PurpleAir to AQS was performed at the hourly level with Geographically Weighted Regression (GWR). The calibrated PurpleAir measurements were down-weighted according to their residual uncertainty and then incorporated into a Random Forest (RF) prediction model as a dependent variable to generate 1-km daily PM2.5 exposure estimates. The state-level PurpleAir calibration reduced the systematic bias to ~0 ug/m3 and decreased the random error by 38%. The considerably large samples also enabled quantitative analyses regarding potential factors related to the PurpleAir bias. The RF-based model with both AQS and down-weighted PurpleAir data outperformed the RF model based solely on AQS with an improved CV R2 of 0.86, an improved spatial CV R2 of 0.81, and a lower prediction error of 5.40 ug/m3. The down-weighting allowed the prediction model to show more spatial details of PM2.5 and to better detect pollution hot-spots. Our spatially scalable calibration and down-weighting strategies, for the first time, allowed an effective application of a state-level LCAQM network in high-resolution PM2.5 exposure modeling. The proposed framework can be generalized to regions worldwide for advancing the evaluation of heavy PM2.5 episodes and health-related applications.

Within a year of its launch date, the ATLAS altimeter on board ICESat-2 is already providing a wealth of critical data of interest across and beyond the cryospheric sciences. With the satellite returning nearly 1 TB of raw data per day, traditional practices of individual research groups downloading large granules of data and then subsetting, processing, and storing them locally are ultimately impractical. We are leading the development of icepyx (formerly icesat2py), an open source Python library designed to easily query, filter, download, and pre-process ICESat-2 datasets. The project’s documentation will include interactive Jupyter Notebook examples, providing a starting point for researchers to create and customize workflows to address their research questions. We actively invite contributions from the community to ensure the project develops in a way that meets a wide range of research needs. The project aims to leverage existing libraries that enable easy parallelization and can be run locally or on cloud-based platforms. As a result, researchers will ultimately download and store minimally-sized subsets of ICESat-2 data and have the opportunity to contribute their code to an established open science project. This presentation will serve to introduce icepyx to the cryosphere community and encourage early adoption of the library by researchers with all levels of coding experience.

We describe a new method for the reconstruction (or forecast) of probabilities that distal geographic locations were inundated by a large pyroclastic density current (PDC) in terms of the flow mass and related uncertainties. Using appropriate model input uncertainty distributions, derived from expert judgements using the equal weights combination rule, we can estimate the mass amount needed to reach a marginal locality at any given confidence level and compare this with ambiguous or inexact peripheral field data. Our analysis relies on different versions of the Huppert and Simpson (1980) integral formulation of axisymmetric gravity-driven particle currents. We focus on models which possess analytical solutions, enabling us to utilize a very fast functional approach for enumerating results and uncertainties. In particular, we adapt the ‘energy conoid’ approach to generate inundation maps along radial directions, based on comparison of the mass-dependent kinetic energy of the flow with the potential energy control by topography in the direction of flow at distal ranges. We focus on two alternative conceptual models: (i) Model 1 assumes the entire amount of solid material originates from a prescribed height above the volcano and flows as a granular current slowed by constant friction; (ii) Model 2 is a multi-phase formulation and includes, in addition to suspended particles, interstitial gas thermally buoyant with respect to surrounding cold air. In the latter case, the flow stops propagating at the surface when the solid fraction becomes less than a critical value, and there is lift-off of the remaining mixture of gas and small particulates. Our model parameters can be further constrained where there is reliable field data or information from analogue eruptions. Finally, we used a Bayes Belief Network related to each inversion model to evaluate probabilistically the uncertainties on the mass required, estimating correlation coefficients between input variables and the calculated mass. For any major magnitude ignimbrite PDC scenario, our method provides a rational basis for assessing the probability of distal flow inundation at critical peripheral locations when there is major uncertainty about the actual or predicted extent of flow runout. Example case histories are illustrated.

Wildland fire smoke exposure affects a broad proportion of the U.S. population and is increasing due to climate change, settlement patterns and fire seclusion. Significant public health questions surrounding its effects remain, including the impact on cardiovascular disease and maternal health. Using atmospheric chemical transport modeling, we examined general air quality with and without wildland fire smoke PM2.5. The 24-hour average concentration of PM2.5 from all sources in 12-km gridded output from all sources in California (2007–2013) was 4.91 μg/m3. The average concentration of fire-PM2.5 in California by year was 1.22 μg/m3 (~25% of total PM2.5). The fire-PM2.5 daily mean was estimated at 4.40 μg/m3 in a high fire year (2008). Based on the model-derived fire-PM2.5 data, 97.4% of California’s population lived in a county that experienced at least one episode of high smoke exposure (“smokewave”) from 2007–2013. Photochemical model predictions of wildfire impacts on daily average PM2.5 carbon (organic and elemental) compared to rural monitors in California compared well for most years but tended to over-estimate wildfire impacts for 2008 (2.0 µg/m3 bias) and 2013 (1.6 µg/m3 bias) while underestimating for 2009 (−2.1 µg/m3 bias). The modeling system isolated wildfire and PM2.5 from other sources at monitored and unmonitored locations, which is important for understanding population exposure in health studies. Further work is needed to refine model predictions of wildland fire impacts on air quality in order to increase confidence in the model for future assessments. Atmospheric modeling can be a useful tool to assess broad geographic scale exposure for epidemiologic studies and to examine scenario-based health impacts.

The Advanced Topographic Laser Altimeter System (ATLAS) on ICESat-2 offers a new remote sensing capability to measure complex sea ice surface topography. We demonstrate the retrieval of six sea ice parameters from ICESat-2/ATLAS data: surface roughness, ridge height, ridge frequency, melt pond depth, floe size distribution and lead frequency. Our results establish that these properties can be observed in high fidelity, across broad geographic regions and ice conditions. We resolve features as narrow as 7 m, and achieve a vertical height precision of 0.01 m, representing a significant advance in resolution over previous satellite altimeters. ICESat-2 employs a year-round observation strategy spanning all seasons, across both the Arctic and Southern Oceans. Because of its higher resolution, coupled with the spatial and temporal extent of data acquisition, ICESat-2 observations may be used to investigate time-varying, dynamic and thermodynamic sea ice processes.

In past global dust storms, no long lasting anomalies in the pressure cycle had been observed. The Global Dust Storm of Mars Year 34 (MY34), however, left behind an average surface pressure lower than what was expected based on the the values recorded on previous years by the Rover Environmental Monitoring Station (REMS) on Curiosity. The main signal contribution to the daily average surface pressure is the CO2 cycle, which is controlled by the Polar ice sublimation and freezing cycles. We used REMS and Mars Climate Sounder (MCS) data to search for correlations between the REMS anomaly and anomalies in the circulation compared to MCS observations from previous years. The findings include an early start of the retreat season for the Northern Polar cap, followed by the longest period of growth for the Southern Polar (SP) cap ice expansion since Curiosity had landed and then, during the dust storm, the longest retreat season of the Southern Polar cap. We also find a larger Northern Polar Cap extension after the storm, suggestive of a larger deposition of CO2 ice. The changes in length of the SP growth and retreat seasons might be consequence of the response of the zonal mean circulation to the dust storm. Changes in the structure of the zonal mean circulation compared to previous years are found in MCS data and presented. The combination of these anomalies constraint what physical processes may have caused this response in surface pressure after the dust storm.

Recent activity has provided new insights into the causes of surface deformation in and around the Yellowstone Caldera, a topic that has been debated since the discovery of caldera-floor uplift more than four decades ago. An episode of unusually rapid uplift (>15 cm/yr) centered near Norris Geyser Basin along the north caldera rim began in late 2013 and continued until a Mw 4.9 earthquake on 30 March 2014; thereafter, uplift abruptly switched to subsidence. Uplift at rates of several cm/yr resumed in 2016 and continued at least through the end of 2018. Modeling of Global Positioning System (GPS) and interferometric synthetic aperture radar (InSAR) data suggests an evolving process of deep magma intrusion during 1996-2001 followed by volatile ascent and accumulation at shallow levels, perhaps as shallow as a few hundred meters depth. The preferred deformation model in which the volatiles accumulated is a shallow uplifted (domed) reservoir. The depth of shallow volatile accumulation appears to have shallowed from the 2014 to the 2016 deformation episode, from 3.2 km depth to 1.8 km depth respectively, and frequent eruptions of Steamboat Geyser since March 2018 might be a surface manifestation of this ongoing process. Hydrothermal explosion features are prominent in the Norris Geyser Basin area, and the apparent shallow nature of the inferred volatile accumulation might represent an increased risk of hydrothermal explosions in the vicinity of Norris Geyser Basin.

In 2007, the National Academies designated “understanding the structure & composition of the lunar interior” (to provide fundamental information on the evolution of a differentiated planetary body) as the second highest lunar science priority that needed to be addressed. Here we present the current status of the planned response of the Lunar Geophysical Network (LGN) team to the upcoming New Frontiers-5 AO. The Moon represents an end-member in the differentiation of rocky planetary bodies. Its small size (and heat budget) means that the early stages of differentiation have been frozen in time. But despite the success of the Apollo Lunar Surface Experiment Package (ALSEP), significant unresolved questions remain regarding the nature of the lunar interior and tectonic activity. General models of the processes that formed the present-day lunar interior are currently being challenged. While reinterpretation of the Apollo seismic data has led to the identification of a lunar core, it has also produced a thinning of the nearside lunar crust from 60-65 km in 1974 to 30-38 km today. With regard to the deep mantle, Apollo seismic data have been used to infer the presence of garnet below ~500 km, but the same data have also been used to identify Mg-rich olivine. A long-lived global lunar geophysical network (seismometer, heat flow probe, magnetometer, laser retro-reflector) is essential to defining the nature of the lunar interior and exploring the early stages of terrestrial planet evolution, add tremendous value to the GRAIL and SELENE gravity data, and allow other nodes to be added over time (ie, deliver the International Lunar Network). Identification of lateral and vertical heterogeneities, if present within the Moon, will yield important information about the early presence of a global lunar magma ocean (LMO) as well as exploring LMO cumulate overturn. LGN would also provide new constraints on seismicity, including shallow moonquakes (the largest type identified by ALSEP with magnitudes between 5-6) that have been linked to young thrust fault scarps, suggesting current tectonic activity. Advancing our understanding of the Moon’s interior is critical for addressing these and many other important lunar and Solar System science and exploration questions, including protection of astronauts from the strong shallow moonquakes.

The boreal forests of Alaska have been experiencing a changing fire regime which threatens human lives and vulnerable ecosystems. Given expected increases in fire activity with climate warming, insight into the controls on fire size from the time of ignition could provide guidance for decision support. Such insight may be especially useful in cases where many ignitions occur in a short time period. Here we investigated the controls and predictability of final fire size at the time of ignition. Using decision trees, we show that ignitions can be classified as leading to small, medium, or large fires with 50.4 ± 5.2% accuracy in cross-validation. This was accomplished using two variables: vapor pressure deficit (VPD) and the fraction of spruce cover near the ignition point. The model predicted that 40% of ignitions would lead to large fires, which accounted for 75% of the total burned area. Other machine learning classification algorithms, including random forests and multi-layer perceptrons, were tested but did not outperform the simpler decision tree model. Applying the model to areas with intensive human management resulted in overprediction of large fires. The overprediction is explained by (1) suppression of those fires and (2) the fact that ignitions in more human-influenced areas occurred during periods of higher VPD on average. Overall, this type of simple classification system could offer insight into optimal resource allocation, helping to maintain a historical fire regime and protect Alaskan ecosystems.

The National Geoscience Faculty Survey (NGFS) was designed to probe how faculty teach in undergraduate geoscience courses, learn about pedagogy and instructional content, and participate in the geoscience education and research communities. The survey has been administered four times in 2004 (n = 2207), 2009 (n = 2874), 2012 (n = 2466), and 2016 (n = 2615). The original survey was developed as part of On the Cutting Edge, a National Science Foundation (NSF)-funded professional development program for geoscience faculty sponsored by the National Association of Geoscience Teachers (NAGT). The 2016 survey was developed by a research team involving leadership of On the Cutting Edge, InTeGrate, and SAGE 2YC, with support from their NSF grants. The NGFS dataset is a unique and valuable longitudinal resource. One of our strategies for providing access to NGFS data is to publish a free-to-download report with response frequencies for all questions in order to make aggregate, baseline data available to the community and to place the NGFS in the context of other national datasets. The report consists of five chapters: 1) An introduction to the survey, including how it was designed and administered; 2) A description of survey respondents, including demographic data, the type and amount of teaching they do, and how they engage with the geoscience education and research communities; 3) The characteristics and topics of courses taught; 4) The teaching strategies used in introductory geoscience courses and courses for geoscience majors; and 5) How and why instructors learn about and change their teaching practices. Our demographic data suggest that the 2016 survey respondents represent about 25% of the population of college-level geoscience instructors in the US. The 2012 and 2016 respondents are a representative sample of the range of disciplines, institution types, and geographic locations of the larger population, but slightly overrepresent more senior faculty (professors and associate professors). Our analyses highlight differences in the nature of teaching and classroom practices between introductory and majors-level geoscience courses, and differences in participation in the geoscience community between instructors at different institution types (two-year vs. four-year).

The Cabled Observatory Vent Imaging Sonar (COVIS) was initially installed on the Ocean Observatories Initiative’s Cabled Array (OOI-CA) observatory at ASHES hydrothermal vent field on Axial Seamount in July 2018. COVIS recorded the acoustic backscatter from the water-column plumes formed above hydrothermal sources and the seafloor within the sonar’s field-of-view until Oct 2018, when an instrument malfunction suspended regular data-collection procedures. In July 2019, COVIS was redeployed after repairs and has since been collecting data at full capacity. Here, we present a comprehensive analysis of the acoustic backscatter data recorded by COVIS along with the in-situ temperature measurements in 2018 and 2019. The results demonstrate significant influences of ocean tides and bottom currents on diffuse hydrothermal discharge within ASHES. In addition, comparison with local seismicity shows a positive correlation between diffuse hydrothermal venting and the seismic activity in the vicinity of the vent field, which provides evidence for an intimate connection between hydrothermal activity and geological processes during the dynamic period leading up to the next eruption of Axial Seamount. Overall, our results showcase the capabilities of underwater acoustic techniques as remote-sensing tools for long-term, quantitative monitoring of seafloor hydrothermal discharge.

Magnetic reconnection is responsible for the major reconfigurations of the magnetosphere that lead to energy transport and deposition into the ionosphere. The fast rate at which magnetic energy is converted to plasma kinetic energy is likely enabled by the polarization Hall electric field that results from the separation of ions and electrons at small scales. Signatures of Hall fields have played a key role in identifying and studying reconnection, but the density of accumulated charge has not been quantified. We use the 4-point measurements of the Magnetospheric Multiscale mission to compute the divergence of the electric field and present the first observations of charge density in the diffusion region of magnetic reconnection. We show how it ties into the Hall system, discuss measurement uncertainties, analyze quality estimates, and make comparisons to 2D simulations. Charge density is briefly presented for other phenomena, and ranges from 2% or less of the background plasma density for magnetic reconnection and electron-scale magnetic holes and peaks to upwards of 4% for electron phase space holes.

In November 2017, the High Resolution Ensemble Forecast version 2 (HREFv2) system was implemented by the U.S. National Oceanic and Atmospheric Administration (NOAA). The HREFv2 is NCEP’s first operational convection-allowing model (CAM) ensemble and runs twice daily at ~3-km horizontal grid spacing. Coinciding with the operationalization of the HREFv2, the NOAA National Weather Service’s Storm Prediction Center (SPC) launched its HREF Ensemble Viewer (https://www.spc.noaa.gov/exper/href), a public web display for real-time HREF data. In addition to products traditionally used in operational forecasting, the HREF Ensemble Viewer also employs post-processing and visualization techniques which leverage the convective structures explicitly modeled in CAM ensembles. This presentation describes and illustrates products from the SPC viewer unique to CAM ensembles which are targeted at forecasting convective storm coverage, intensity, and evolution. A key aspect of HREF’s ensemble design is membership diversity with respect to model cores and parameterization schemes, in conjunction with a time-lagging approach. Verification is performed on ensemble fields related to explicitly modeled convection for various modified configurations of the HREF membership, illustrating how each dimension of membership diversity contributes to the system’s skill in forecasting convective evolution. These results help to illuminate potential tradeoffs entailed in configuring future HREF iterations beyond version 2 as CAMs using new dynamical cores and parameterization schemes become available.

Gravel-bed rivers are often interpreted as equilibrium, near-threshold channels (Parker, 1979), where channel morphology is adjusted to transmit the supply of coarse bed material with the given discharge. Theoretical analyses based on this concept predict bank sediments at the threshold of motion with bankfull Shields stresses on the bed (based on the D50) slightly in excess of this threshold, such that the bed material is fully mobile at bankfull stage. Surveys of 13 sites around the White Clay Creek, however, provide observations that are inconsistent with this concept. Bedrock is exposed along the channel and the longitudinal profile is controlled by migrating knickpoints, suggesting that the slope is imposed by bedrock erosion. Moreover, up to 50% of the bed material is immobile at bankfull stage. These observations suggest an alternate hypothesis to threshold channel theory: immobile cobble-boulder bed material is supplied locally by colluvial processes and bedrock incision, with a throughput load of sand-pebble-sized sediment readily transported by the river that is primarily stored in bars rather than on the bed. An approximate threshold condition based on the D50 of the streambed arises by averaging the grain size distribution over the immobile bed material and the finer throughput load, but this averaged bankfull Shields stress does not provide a useful measure of the mobility of all size fractions on the bed. These observations suggest that the channel morphology of the study site is decoupled from the supply of bed material, and that the White Clay Creek should not be considered an equilibrium, near-threshold channel. To test our hypothesis, we attached radio frequency identification (RFID) tags to 50 clasts in a 100 m reach. The RFID tags were installed with the gravel in situ on the bed at randomized locations in the channel; the distribution of tagged grains mirrors the grain size distribution of the bed. Since the deployment of tagged clasts in June 2019, six surveys have been accomplished and four significant flow events have occurred with the gage height reaching 2/3 of bankfull stage. Afterwards, 77% of tagged gravel remained in place during a given event, supporting our hypothesis. Numerical modeling of bed mobility under a variety of sediment supply scenarios allows us to generalize our field observations.

Impact craters in the Pluto system as observed by New Horizons in July of 2015 revealed a previously-unknown deficit of small Kuiper belt objects (KBOs) less than ~1–2 km in diameter (Singer et al., 2019, Science) . New Horizons was poised to test this observation 3.5 years later with its next KBO close flyby of the cold classical KBO (486958) 2014 MU69 (nicknamed Ultima Thule). The images returned by New Horizons in early 2019 show MU69 is only modestly cratered, and potential craters on the surface show a shallow size-frequency distribution (SFD) similar to that of craters on Pluto and Charon (Stern et al. 2019, Science; Singer et al., 2019, EPSC-DPS; Spencer et al. 2020, Science). Some features on MU69 do have multiple morphologic indicators suggesting they are impact craters. There are regions on MU69 with few-to-no craters, even where the lighting is favorable for recognizing topographic features. The ~7-km-diameter depression (informally called Maryland) on the small lobe of MU69 is the largest easily observed feature that is likely to be an impact crater. There are also craters under 1 km in diameter, but no obvious craters between 1 and 7 km in diameter. The possible impact craters < ~1 km in diameter were divided into subgroups based on morphology, likelihood of being a crater , lighting geometry, and also into one possible geologic unit. Measurements/ratings were provided by several team members. All subgroups produced shallow-sloped SFDs (differential power-law slope less than approximately −2) similar to those seen on Pluto and Charon for the same size craters (when scaled for gravity and impact velocity). Whole-sale resurfacing through geologic processes is not expected for MU69. Thus many more craters and at least some heavily cratered terrains would be predicted if the impactor SFD slope were steeper (an average differential slope closer to −3; Greenstreet et al. 2019, ApJ). Both the apparent lack of craters overall, and the shallow SFD slopes, are consistent with a relatively benign collisional environment for MU69 (McKinnon et al., 2020, Science).

We employ Magnetospheric Multiscale, Geostationary Operational Environmental and Los Alamos National Laboratory satellites, as well as the ground magnetometer networks over Greenland and North America to study a substorm on 9 August 2016 between 9 and 10 UT.We found that during the substorm two earthward flows, whose dipolarization-injection fronts exceeded 6.5 and 4 Earth’s radii (RE) in YGSM, impinged and rebounded from Earth’s dipolar field lines at L = 6–7 downtail, where L is the McIlwain number. The impingements and rebounds ended with a substorm current system of downward R1 and upward R2 currents which grew to azimuthally cover the whole North American continent. At the fronts, regions of enhanced negative j·E′ were formed and peaked toward the end of the impingements. These regions appeared to be conjugate with eastward moving aurora (along the growth phase arc and together with eastward drifting energetic electrons at geosynchronous equatorial orbit), which manifests ionospheric Ohmic losses.

We present a book entitled “Machine Learning, Statistics, and Data Mining for Heliophysics,” an online and open source book available at helioml.org. This book includes a collection of interactive Jupyter notebooks, written in the programming language Python, that walks the reader through the process of applying machine learning, statistics, and data mining techniques on various kinds of solar and space physics data sets to reproduce published results. We consider this book to be a living document with frequent updates. Please contact us if you’d like to submit a chapter!