Whole atmosphere models that fully capture the propagation of wave dynamics from lower to upper atmosphere are believed sufficient to reproduce the type of short-term variability in the neutral upper atmosphere that produces observed variations in ionospheric parameters. However, recent studies suggest that upper atmospheric observations are needed to accurately represent short-term variability in both planetary-scale mass transport and tidal behavior crucial to representing the structure of the thermosphere and the wind-dynamo coupling in the ionosphere. To address this, we use atmospheric specifications from the prototype High-Altitude Navy Global Environmental Model (HA-NAVGEM) from the ground to 92 km to nudge the Whole Atmosphere Community Climate Model extended version (WACCM-X) coupled to the Navy Highly Integrated Thermosphere Ionosphere Demonstration System (Navy-HITIDES) ionospheric model. The HA-NAVGEM data assimilation/forecast system is run in two configurations: a reference experiment for the time period December 2012-March 2013, where satellite-based middle atmospheric observations (SABER temperature retrievals; Aura MLS temperature, ozone, and water vapor retrievals; and SSMIS microwave radiances) are included between 20-90 km; and a perturbed experiment, during the same time period, in which the middle atmospheric observations are removed. The resulting nudged simulations using WACCM-X coupled to Navy-HITIDES are used to study the impact of upper atmospheric observations in reproducing the observed short-term variability in the thermosphere-ionosphere system, both in terms of the thermospheric structure and the ionospheric response via wind-dynamo coupling. The role of solar thermal and lunar gravitational tides is discussed, as well as the impact of observations on the weather of the day in the lower thermosphere.

Climate change and nitrogen (N) pollution are altering biogeochemical and ecohydrological processes in dryland watersheds, increasing N export, and threatening water quality. While simulation models are useful for projecting how N export will change in the future, most models ignore biogeochemical “hotspots” that develop in drylands as moist microsites become hydrologically disconnected from plant roots when soils dry out. These hotspots enable N to accumulate over dry periods and rapidly flush to streams when soils wet up. To better project future N export, we developed a framework for representing hotspots using the ecohydrological model RHESSys. We then conducted a series of virtual experiments to understand how uncertainties in model structure and parameters influence N export. Modeled export was sensitive to the abundance of hotspots in a watershed, increasing linearly and then reaching an asymptote with increasing hotspot abundance. Peak streamflow N was also sensitive to a soil moisture threshold at which subsurface flow from hotspots reestablished, allowing N to be transferred to streams; it increased and then decreased with an increasing threshold value. Finally, N export was generally higher when water diffused out of hotspots slowly. In a case study, we found that when hotspots were modeled explicitly, peak streamflow nitrate export increased by 29%, enabling us to better capture the timing and magnitude of N losses observed in the field. This modeling framework can improve projections of N export in watersheds where hotspots play an increasingly important role in water quality.

Field-aligned currents (FACs), or the system of currents flowing along Earth’s magnetic field lines, are the dominant form of energy and momentum exchange between the magnetosphere and ionosphere. FACs are ubiquitous across the high-latitude region and have unique characteristics depending on the magnetospheric or solar wind source mechanism, and, therefore, mapping location in the ionosphere (i.e. auroral zone, polar cap, cusp). Further complicating the picture, FACs also exhibit a large range of spatial and temporal scales. In order to create new understanding of FAC spatial and temporal scales, their cross-scale effects, and the impact on the polar region, including on critical technologies, new data analysis approaches are required. This talk addresses a coherent progression of investigation in three parts: 1) an exploration of the characteristics, controlling parameters, and relationships of multiscale FACs using a rigorous, comprehensive analysis across multiple spacecraft observations; 2) augmentation of these statistical results with detailed case studies, fusing observations from diverse platforms and incorporating critical information about the high-latitude electrodynamics across scales; and 3) a quantitative investigation of the impact on Global Navigation Satellite System (GNSS) signals. We find that the relationships between FAC scales are complex and reveal new information about the connection between multiscale FACs and irregular space weather activity. Additionally, there are observable signatures of multiscale FACs and resultant electrodynamic activity in ionospheric data from GNSS signals, suggesting that these signals are affected distinctly according to scale size of the coupling process. Our results indicate that GNSS data may be a powerful source of information about the multiscale near Earth space environment.

In 2013, the Office of Science and Technology Policy (OSTP) issued a memorandum directing Federal agencies with over $100 million in annual research and development expenditures to develop a plan to support increased access to federally funded research results. In response, the US Geological Survey developed a Public Access Plan and published four new data management policies. The policies focus on review and approval of scientific data supporting scholarly conclusions, requirements for metadata, preservation, and data management planning. The new policies, in conjunction with the Public Access Plan, represent a shift in culture in how the USGS manages and provides access to its science data. The USGS recognizes that successful implementation of these new policies requires multiple pillars of support, from USGS leadership and staff buy-in, to effective tools. Active community engagement in the Bureau is stimulated through the Community for Data Integration (CDI), an open forum for community discussion and engagement, and an important component creating buy-in and contributing to the success of the new policies. Also critical are a suite of tools available to scientists to ensure their ability to implement the policies. Finally, support from leadership that manifests in the Fundamental Science Practices Advisory Council (FSPAC), a committee of representatives from across the Bureau who preside over policies and guidance is a critical component. While far from complete, the USGS has shifted its approach to science data management by engaging the community, offering tools to support policy, and providing leadership support for the quality and scientific integrity of USGS science data.

This work presents Bayesian network-informed conditional random forests (BNICRF), a novel multiresponse classification technique able to downscale the joint probability distribution of precipitation occurrence at a set of geographical locations of interest from large-scale predictors. BNICRFs combine a Bayesian network, which provides a description of the multisite/spatial dependence structure of the target locations; with an ensemble of conditional random forests, which extract the relevant information from the large-scale predictors to produce accurate probabilistic predictions. This work extends two previous articles that explored Bayesian networks (Legasa & Gutiérrez, 2020) and random forests (Legasa et al., 2022) in the context of statistical climate downscaling. Building on the experimental and validation framework proposed in the experiment 1c of the COST action VALUE (the largest, most exhaustive intercomparison study of statistical downscaling methods to date) and under the perfect prognosis approach, we thoroughly assess the performance of the proposed methodology focusing on its capability to reproduce several multisite and single-site statistics of the observed series. BNICRFs accurately capture the relevant spatial relationships while keeping the same single-site predictive performance than both single-site random forests and a Generalized Linear Model that exhibited a good performance in VALUE. Moreover, we compare BNICRFs against a robust multisite methodology proposed in Chandler (2020), obtaining better predictive capability while keeping similar spatial performance. Since the assessed models still underestimate autocorrelation, we also propose a straightforward extension of BNICRFs that incorporates the temporal structure at each location of interest able to produce both temporarily and spatially realistic precipitation occurrence fields

Dust is a meteorological phenomenon that has a strong impact on the environment, air quality, and human health. In the USA one of the most widely used databases of information on dust events is the Storm Events Database (SED). This project aims to examine the reliability and usefulness of the SED as a source for documenting the climatology of dust storms (DS) across the USA. While SED provides information potentially useful for understanding the frequency, distribution, and importance of DS across the USA, our analysis of DS from 2000 to 2020 shows that many DS were missing while some recorded events of less severe blowing dust (BLDU) in the SED were incorrectly reported as DS. Although the dust records from SED have been widely utilized to study dust related physical and societal issues, the limitations found in this study need to be taken into consideration in future studies.

Understanding the simple mechanism of variable solar irradiance ocean warming/cooling allows for effective attribution, detection, and prediction of ocean-related extreme weather events based on solar activity level and duration. Modern and historical solar and ocean data were used to find the ocean warms from rising and high solar activity over any duration, and vice-versa, such as cooling from low activity during the Dalton Minimum. Equatorial ocean heat content and sea surface temperature are sensitive to daily total solar irradiance (TSI) variation and level, and to the upwelled heat accumulation from prior days sub-surface absorbed solar energy penetration. Equatorial evaporation from the absorbed solar energy performs as the pump of Earth’s hydrological system, sourcing atmospheric rivers and associated extreme weather events, follows a solar cycle pattern. Decadal scale ocean warming and post-solar cycle maximum El Nino events were empirically found to occur after solar activity rises above a decadal average of 120 sfu F10.7cm, equivalent to 94 v2 SIDC sunspot number and 1361.25 W/m2 LASP SORCE TSI. HadSST3 was found to linearly vary with the annual change in TSI, nominally at 0.5°C/W/year, varying with rate of TSI change. An empirical F10.7-TSI-SST model was derived combining a F10.7cm-SORCE TSI correlation model and the HadSST3-TSI sensitivity factor, predicated on the SWPC Solar Cycle 24 panel 2016 F10.7cm flux forecast. The author used this model in December of 2015 to uniquely and successfully predict the 2016 HadSST3 temperature fall to within 0.03°C. Cross correlation analysis indicate solar minimum La Nina events result from insufficient TSI over time, producing less equatorial evaporation, less cloud cover and precipitation, causing drought in the US. The Solar Modern Maximum that peaked in late 2003 warmed the ocean creating 20th century climate change via higher than average solar activity, higher than the 1361.25 W/m2 decadal TSI warming threshold. The 2003 European heat wave was forced by cumulative high solar activity warming of the AMO, and from high TSI during the heat wave. The 2013/14 Cold Wave is partly attributable to periods of lower than solar minimum level irradiance from October 2013 through February 2014. Future extremes depend on the duration of future solar cycle activity extremes.

This study investigates the influence of oceanic and atmospheric processes in extratropical thermodynamic air-sea interactions resolved by satellite observations (OBS) and by two climate model simulations run with eddy-resolving high-resolution (HR) and eddy-parameterized low-resolution (LR) ocean components. Here, spectral methods are used to characterize the sea surface temperature (SST) and turbulent heat flux (THF) variability and co-variability over scales between 50-10000 km and 60 days-80 years in the Pacific Ocean. The relative roles of the ocean and atmosphere are interpreted using a stochastic upper-ocean temperature evolution model forced by noise terms representing intrinsic variability in each medium, defined using climate model data to produce realistic rather than white spectral power density distributions. The analysis of all datasets shows that the atmosphere dominates the SST and THF variability over zonal wavelengths larger than ~2000-2500 km. In HR and OBS, ocean processes dominate the variability of both quantities at scales smaller than the atmospheric first internal Rossby radius of deformation (R1, ~600-2000 km) due to a substantial ocean forcing coinciding with a weaker atmospheric modulation of THF (and consequently of SST) than at larger scales. The ocean-driven variability also shows a surprising temporal persistence, from intraseasonal to multidecadal, reflecting a red spectrum response to ocean forcing similar to that induced by atmospheric forcing. Such features are virtually absent in LR due to a weaker ocean forcing relative to HR.

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.

Reducing the model spread in free-tropospheric relative humidity (RH) and its response to warming is a crucial step towards reducing the uncertainty in clear-sky climate sensitivity, a step that is hoped to be taken with recently developed global storm-resolving models (GSRMs). In this study we quantify the inter-model differences in tropical present-day RH across GSRMs, making use of DYAMOND, a first 40-day intercomparison. We find that the inter-model spread in tropical mean free-tropospheric RH is reduced compared to conventional atmospheric models, except from the the tropopause region and the transition to the boundary layer. We estimate the reduction to approximately 50-70% in the upper troposphere and 25-50% in the mid troposphere. However, the remaining RH differences still result in a spread of 1.2 Wm-2 in tropical mean clear-sky outgoing longwave radiation (OLR). This spread is mainly caused by RH differences in the lower and mid free troposphere, whereas RH differences in the upper troposphere have a minor impact. By examining model differences in moisture space we identify two regimes with a particularly large contribution to the spread in tropical mean clear-sky OLR: rather moist regimes at the transition from deep convective to subsidence regimes and very dry subsidence regimes. Particularly for these regimes a better understanding of the processes controlling the RH biases is needed.

The concentration of chlorophyll-a (CHL) is an important proxy for autotrophic biomass and primary production in the ocean. Quantifying trends and variability in CHL are essential to understanding how marine ecosystems are affected by climate change. Previous analyses have focused on assessing trends in CHL mean, but little is known about observed changes in CHL extremes and variance. Here we apply a quantile regression model to detect trends in CHL distribution over the period of 1997-2022 for several quantiles. We find that the magnitude of trends in upper quantiles of global CHL (>90th) are larger than those in lower quantiles (≤50th) and in the mean, suggesting a growing asymmetry in CHL distribution. On a regional scale, trends in different quantiles are statistically significant at high latitude, equatorial, and oligotrophic regions. Assessing changes in CHL distribution has potential to yield a more comprehensive understanding of climate change impacts on CHL.

The Space Physics Data Facility (SPDF) has developed and/or leveraged standardized self-describing data formats, metadata for datasets and parameters, time conventions, and dataset and filenaming conventions that enable effective data analysis and browsing using generic easy-to-use software and web services. Software and services include SPDF’s CDAWeb , and external tools such as Autoplot and SPEDAS IDL library. Standards and conventions include: datasets and filenaming and , the CDF scientific data format (including its new Python library ), the ISTP/IACG/SPDF Guidelines for global and variable attributes , time variable types , and the SKTeditor metadata creation tool . The SPASE standards for describing datasets for easy searching are crucial to the Heliophysics Data Portal .

The Calhoun Critical Zone Observatory (CCZO) has been collecting above and below canopy water, energy and carbon fluxes and other hydro-meteorological processes since 2016. The observations provide a unique opportunity to investigate the coupling between aboveground environmental conditions and belowground fluxes of mass and energy in general and the fate of fixed carbon in forested ecosystems in particular. The simultaneous measurements of soil-canopy-atmosphere states and fluxes reveal the role of pine forest in diurnal and seasonal dynamics of the CCZ ecosystem.

Low Earth Orbit (LEO) satellites offer extensive data of the radiation belt region, but utilizing these observations is challenging due to potential contamination and difficulty of intercalibration with spacecraft measurements at Highly Elliptic Orbit (HEO) that can observe all equatorial pitch-angles. This study introduces a new intercalibration method for satellite measurements of energetic electrons in the radiation belts using a data assimilation approach. We demonstrate our technique by intercalibrating the electron flux measurements of the National Oceanic and Atmospheric Administration (NOAA) Polar-orbiting Operational Environmental Satellites (POES) NOAA-15,-16,-17,-18,-19 and MetOp-02 against Van Allen Probes observations from October 2012 to September 2013. We use a reanalysis of the radiation belts obtained by assimilating Van Allen Probes and Geostationary Operational Environmental Satellites (GOES) observations into 3-D Versatile Electron Radiation Belt (VERB-3D) code simulations via a standard Kalman filter. We compare the reanalysis to the POES dataset and estimate the flux ratios at each time, location and energy. From these ratios we derive energy and $L^*$ dependent recalibration coefficients. To validate our results, we analyse on-orbit conjunctions between POES and Van Allen Probes. The conjunction recalibration coefficients and the data-assimilative estimated coefficients show strong agreement, indicating that the differences between POES and Van Allen Probes observations remain within a factor of two. Additionally, the use of data assimilation allows for improved statistics, as the possible comparisons are considerably increased. Data-assimilative intercalibration of satellite observations is an efficient approach that enables intercalibration of large datasets using short periods of data.

A document by Sajjad Azimi. Click on the document to view its contents.

The instrument package SEIS (Seismic Experiment for Internal Structure) with the three very broadband and three short-period seismic sensors is installed on the surface on Mars as part of NASA’s InSight Discovery mission. When compared to terrestrial installations, SEIS is deployed in a very harsh wind and temperature environment that leads to inevitable degradation of the quality of the recorded data. One ubiquitous artifact in the raw data is an abundance of transient one-sided pulses often accompanied by high-frequency spikes. These pulses, which we term “glitches”, can be modeled as the response of the instrument to a step in acceleration, while the spikes can be modeled as the response to a simultaneous step in displacement. We attribute the glitches primarily to SEIS-internal stress relaxations caused by the large temperature variations to which the instrument is exposed during a Martian day. Only a small fraction of glitches correspond to a motion of the SEIS package as a whole caused by minuscule tilts of either the instrument or the ground. In this study, we focus on the analysis of the glitch+spike phenomenon and present how these signals can be automatically detected and removed from SEIS’ raw data. As glitches affect many standard seismological analysis methods such as receiver functions, spectral decomposition and source inversions, we anticipate that studies of the Martian seismicity as well as studies of Mars’ internal structure should benefit from deglitched seismic data.

Autonomous large-scale detection of whistler-ode chorus elements in the Van Allen radiation belts has been an open computational challenge. This is primarily due to: (i) Variability of the spectral morphology of chorus elements, and (ii) Structured background interference from hiss-like chorus that can make elements difficult to detect using traditional signal processing and pattern recognition techniques. We will present computational techniques drawing on pixel connectivity, signal-to-noise (SNR) considerations as well as supervised and unsupervised pattern recognition techniques. Specifically, we will explore the efficacy of popular machine learning techniques trained on unfiltered spectral images versus those trained on culled features generated by unsupervised feature extraction techniques. Representative results will be presented based on magnetic field measurements taken by the EMFISIS instrument suite in the Van Allen probes mission.

Ice Floe Tracker is an open-source tool designed to retrieve floe-scale sea ice motion in the Arctic marginal ice zone during spring and summer.  Ice Floe Tracker enables observation of the floe size distribution, ice floe rotation rates, small-scale variation in floe velocity, and individual floe trajectories. Sea ice motion occurs on a wide range of scales, from the interaction of individual pieces of ice at sub-meter scales, the formation of linear kinematic features, and ice transport via basin-wide gyres. Most existing methods for tracking ice motion from remote sensing imagery rely on cross-correlation and are optimized for the winter season in the central Arctic. Cross-correlation-derived motion vectors estimate area-averaged motion and thus are well-suited for close-packed central Arctic ice; however, such estimates have high uncertainties in the dynamic, strongly deforming sea ice cover of the marginal ice zone. Our tool aims to fill this gap by using shape detection and feature tracking to observe floe-scale ice motion.The Ice Floe Tracker algorithm consists of a series of customizable modules. The code is structured as a modular package written in open-source languages. It includes parallel processing, unit testing, a command line interface, and thorough documentation (available on Github). Routines are provided to download imagery from the NASA Moderate Resolution Imaging Spectroradiometer. The satellite imagery is processed to enhance the contrast between liquid water and sea ice, sharpen floe boundaries, and remove atmospheric noise. The image is then segmented, and geometric features of ice floes are extracted. Finally, ice floe geometry and locations are compared to those in subsequent images and linked to form trajectories.  By making this tool open-source, we aim to encourage cross-disciplinary collaboration. Recent results from collaborations between observational oceanography and discrete-element sea ice model development will be highlighted.