It is now widely understood that seasonal snow cover in the Western United States is melting earlier than in past decades. This could have significant consequences for human populations and ecosystems dependent on regularity in timing and magnitude of downstream flows that originate as snow. However, while earlier melt is well established, less is known about intra-annual changes in the spatial and temporal distribution of accumulation and ablation (melt) cycles in the core winter months and spring months, i.e. the ‘persistence’ of seasonal snow cover. This is significant because changes to the persistence of seasonal snow in the winter and spring could have important implications for other snow cover characteristics such as albedo, as well as ancillary hydrologic factors such as soil moisture and runoff. To understand these changes in persistence, this project focuses on study basins in different climatic zones of the Columbia river basin, capturing the shift from maritime snowpack in the west to alpine snowpack in the east. The research relies on a combination of time series analysis of NRCS SNOTEL stations and snow courses and use of an optical remote sensing product which is based on the MODIS MOD10A1 dataset. To compensate for significant winter and spring cloud cover, particularly in the Pacific Northwest, a temporal and spatial gap filling approach utilizing higher spatial resolution products (e.g. Landsat and Sentinel 2) is implemented primarily in Google Earth Engine. The seasonal snow persistence from the MODIS-based product is evaluated using additional Landsat, Sentinel 2 and Planet Labs data, as well as data from the in situ monitoring stations. Finally, changes in intra-annual seasonal snow cover persistence are characterized for core winter, spring and early summer months along an elevational gradient and across study sub-basins.

The Space Physics Data Facility (SPDF https://spdf.gsfc.nasa.gov) and Solar Data Analysis Center (SDAC https://umbra.nascom.nasa.gov/), as the NASA Heliophysics active final archives, will be preserving and distributing the data from Parker Solar Probe. Working in cooperation with current operating missions and the heliophysics community, SPDF ingests, preserves and serves a wide range of past and current public science-quality data from the ionosphere into the furthest reach of deep-space exploration. SPDF has been working with the Parker Solar Probe mission in preparation for archiving and serving its in-situ data starting 2019 Nov 12, and also has arrangements to serve in-situ data from Solar Orbiter when those data become public. SPDF will facilitate scientific analysis of multi-instrument and multi-mission datasets to enhance the science return of Parker Solar Probe mission. SPDF develops and maintains the Common Data Format (CDF) and the associated ISTP/SPDF metadata guidelines. SPDF services include CDAWeb, which supports both survey and burst mode data with graphics, listings and data superset/subset functions. All public data held by SPDF are also available for direct file download by HTTPS or FTPS links from the SPDF home page (https://spdf.gsfc.nasa.gov). SPDF is currently receiving and serving from missions including Helios, MMS, Van Allen Probes, THEMIS/ARTEMIS, GOLD, ACE, Cluster, Geotail, Polar, Wind and many others, and >120 Ground-Based investigations. SPDF recently added support for ARASE/ERG and MAVEN as supplementary access at the requests of those missions. SPDF also operates the multi-mission orbit displays and query services of SSCWeb and the Java-based 4D Orbit Viewer, as well as the Heliophysics Data Portal (HDP) discipline-wide data inventory and access service, and the OMNIweb near-Earth solar wind plasma and magnetic field database.

Over the past third of a century the Incorporated Research Institutions for Seismology (IRIS) has facilitated observational seismology in many ways. At the beginning of IRIS in 1984, and with the support of the National Science Foundation and in partnership with the US Geological Survey, IRIS embarked on deploying the Global Seismographic Network (GSN). Key characteristics of the GSN are its use of high-performance digitizers, very broad band seismometers, strong motion accelerometers, and high frequency sensors to provide multi-decadal observations across a wide frequency band and dynamic range. The IRIS Portable Array Seismic Studies of the Continental Lithosphere (PASSCAL) program has also operated since 1984. PASSCAL’s extensive inventory of seismic equipment has been used by scientists to make observations on every part of the globe. The number and breadth of observations made with this equipment has fueled thousands of research papers and contributed to the education of hundreds, if not thousands, of students. More recently, the IRIS-operated EarthScope Transportable Array (TA) provided a breakthrough in the systematic collection of data using an array of unprecedented size. The success of the TA has ushered in a new era of “Large N” seismology, focused on dense spatial coverage of sensors to reduce aliasing and provide more complete recording of the full wavefield. The TA highlighted the power of survey mode data collection, where systematic, spatially-dense, and high-quality data fuel data-driven discovery, as opposed to deployments made to test a specific hypothesis. Key future directions in observational seismology include an increasing emphasis on wavefield measurements. Deploying instruments in large numbers requires reductions in the size, weight, and power of units, as well as a focus on dirt-to-desktop data management strategies that merge data and metadata while minimizing human intervention with the data flow from the sensor in the dirt to the scientist’s desktop. Other critical frontiers include pervasive seafloor observations to enable studies of key structures like subduction zones, more accessible satellite telemetry to enable ubiquitous sensing of the environment, and new sensing technologies such as MEMS and Distributed Acoustic Sensing.

Seismic site characterization of rock and soil properties has a large impact on earthquake ground motions and engineering seismology, especially for evaluation of local site amplification, calibration of strong-motion records and realistic shaking estimates at seismic stations, site-specific hazard assessment, estimation of ground motion models and soil classification for building code applications. However, there is not yet a common way to exchange site characterization information, whereas setting-up standard practices and quality assessment are becoming very important to reach high-level metadata. Within the framework of the SERA “Seismology and Earthquake Engineering Research Infrastructure Alliance for Europe” Horizon 2020 Project, a networking activity is leading to the definition of a European strategy and standards for site characterization of seismic stations in Europe. Based on the results of an online questionnaire, we first defined a list of indicators considered as mandatory for a reliable site characterization: fundamental resonance frequency, shear-wave velocity profile (Vs), time-averaged Vs over the first 30 m, depth of seismological and engineering bedrock, surface geology, soil class. We then proposed a summary report for each indicator, containing the most significant background information of data acquisition and processing details, and a quality metrics scheme. This requires the evaluation of both (i) reliability of the site characterization indicators provided by different methods, and (ii) consistency among the indicators according to the current knowledge and experience of the scientific community. The quality metrics application to some Italian accelerometric sites, characterized within the Italian Civil Protection Department-INGV agreement (2016 to 2021), highlights the capabilities of capturing the characterization quality. These guidelines have been shared within European and worldwide scientific community and validated through focus groups during a dedicated workshop (https://sites.google.com/view/site-characterization-workshop/). They represent a first attempt to reach high-level metadata for site characterization, being aware that they can be improved and modified after a few years of experience and feedback from users.

Observed surface temperature trends over recent decades are characterized by (i) intensified warming in the Indo-Pacific Warm Pool and slight cooling in the eastern equatorial Pacific, consistent with strengthening of the Walker circulation, and (ii) cooling in the Southern Ocean. In contrast, state-of-the-art coupled climate models generally project Walker circulation weakening, enhanced warming in the eastern equatorial Pacific, and warming in the Southern Ocean. Here we investigate the ability of 16 climate model large ensembles to reproduce observed sea-surface temperature and sea-level pressure trends over 1979-2020 through a combination of externally forced climate change and internal variability. We find large-scale differences between observed and modeled trends that are very unlikely (<5% probability) to occur due to internal variability as represented in models. Disparate trends are found even in regions with weak multi-decadal variability, suggesting that model biases in the transient response to anthropogenic forcing constitute part of the discrepancy.

Soil organic carbon (SOC) stocks represent a large component of the global carbon cycle that is sensitive to warming. Modeling and empirical studies often assume that temperature responses of microbial physiological functions and extracellular enzymatic reactions are predictive of ecosystem-scale SOC decomposition responses to warming. However, temperature-dependent soil trophic interactions such as predation of microbial decomposers by other organisms have not yet been incorporated into quantitative SOC models. Here, we incorporated a microbial predator into a tri-trophic population ecology model and a global-scale predictive SOC model to determine how predation would affect soil community population dynamics and temperature sensitivity of SOC stocks. Predators increased SOC stocks and their dependence on substrate input rates. Top-down controls of predators on microbial biomass caused SOC warming responses to diverge from microbial temperature responses, with warming-induced SOC losses reduced or reversed when predators were more temperature-sensitive. Our results suggest that higher trophic levels can reduce the sensitivity of SOC to warming, and that differences in temperature sensitivity across trophic levels may be a key determinant of SOC warming responses.

Current approaches to estimate NOx emissions fail to account for new and small sources, biomass burning, and sources which change rapidly in time, generally don’t account for measurement error, and are either based on models, or do not consider wind, chemistry, and dynamical effects. This work introduces a new, model-free analytical environment that assimilates daily TROPOMI NO2 measurements in a mass-conserving manner, to invert daily NOx emissions. This is applied over a rapidly developing and energy-consuming region of Northwest China, specifically chosen due to substantial economic and population changes, new environmental policies, large use of coal, and access to independent emissions measurements for validation, making this region representative of many rapidly developing regions found across the Global South. This technique computes a net NOx emissions gain of 70% distributed in a seesaw manner: a more than doubling of emissions in cleaner regions, chemical plants, and regions thought to be emissions-free, combined with a more than halving of emissions in city centers and at well-regulated steel and powerplants. The results allow attribution of sources, with major contributing factors computed to be increased combustion temperature, atmospheric transport, and in-situ chemical processing. It is hoped that these findings will drive a new look at emissions estimation and how it is related to remotely sensed measurements and associated uncertainties, especially applied to rapidly developing regions. This is especially important for understanding the loadings and impacts of short-lived climate forcers, and provides a bridge between remotely sensed data, measurement error, and models, while allowing for further improvement of identification of new, small, and rapidly changing sources.

Sediment trapping behind dams is currently a major source of bias in large-scale hydro-geomorphic models, hindering robust analyses of anthropogenic influences on sediment fluxes in freshwater and coastal systems. This study focuses on developing a new reservoir trapping efficiency (Te) parameter to account for the impacts of dams in hydrological models. This goal was achieved by harnessing a novel remote sensing data product which offers high-resolution and spatially continuous maps of suspended sediment concentration across the Contiguous United States (CONUS). Validation of remote sensing-derived surface sediment fluxes against USGS depth-averaged sediment fluxes showed that this remote sensing dataset can be used to calculate Te with high accuracy (R2 = 0.98). Te calculated for 116 dams across the CONUS, using upstream and downstream sediment fluxes from their reservoirs, range from 0.3% to 98% with a mean of 43%. Contrary to the previous understanding that large reservoirs have larger Te and vice versa, these data reveal that large reservoirs can have a wide range of Te values. A suite of 21 explanatory variables were used to develop an empirical Te model using multiple regression. The strongest model predicts Te using five variables: dam height, incoming sediment flux, outgoing water discharge, reservoir length, and Aridity Index. A global model was also developed using explanatory variables obtained from a global dam database to conduct a global-scale analysis of Te. These CONUS- and global-scale Te models can be integrated into hydro-geomorphic models to more accurately predict river sediment transport by representing sediment trapping in reservoirs.

We propose an integrated dynamics-physics coupling framework for weather and climate-scale models. Each physical parameterization would be advanced on its natural time scale, revised to include a moist thermodynamic relationship, and finally integrated into the relevant components of the dynamical core. We show results using a cloud microphysics scheme integrated within the dynamical core of the GFDL SHiELD weather model to demonstrate the promise of this concept. We call it the in-line microphysics as it is in-lined within the dynamical core. Statistics gathered from one year of weather forecasts show significantly better prediction skills when the model is upgraded to use the in-line microphysics. However, we do find that some biases are degraded with the in-line microphysics. The in-line microphysics also shows larger-amplitude and higher-frequency variations in cloud structures within a tropical cyclone than the traditionally-coupled microphysics. Finally, we discuss the prospects for further development of this integrated dynamics-physics coupling.

This paper presents a new coupled urban change and hazard consequence model that considers population growth, a changing built environment, natural hazard mitigation planning, and future acute hazards. Urban change is simulated as an agent-based land market with six agent types and six land use types. Agents compete for parcels with successful bids leading to changes in both urban land use – affecting where agents are located – and structural properties of buildings – affecting the building’s ability to resist damage to natural hazards. IN-CORE, an open-source community resilience model, is used to compute damages to the built environment. The coupled model operates under constraints imposed by planning policies defined at the start of a simulation. The model is applied to Seaside, Oregon, a coastal community in the North American Pacific Northwest subject to seismic-tsunami hazards emanating from the Cascadia Subduction Zone. Ten planning scenarios are considered including caps on the number of vacation homes, relocating community assets, limiting new development, and mandatory seismic retrofits. By applying this coupled model to the testbed community, we show: (1) placing a cap on the number of vacation homes results in more visitors in damaged buildings, (2) that mandatory seismic retrofits do not reduce the number of people in damaged buildings when considering population growth, (3) polices diverge beyond year 10 in the model, indicating that many policies take time to realize their implications, and (4) the most effective policies were those that incorporated elements of both urban planning and enforced building codes.

One of the central challenges for global food security is the growing pressure from increasingly frequent extreme weather events that results in sharp drops in crop yield and disruptions in the food supply. Such pressure can potentially be alleviated by international crop trade, which plays a crucial role in reallocating food commodities from surplus to deficit regions. However, few studies have examined the influence of extreme weather events and the synchrony of crop yield anomalies on trade linkages among nations. To investigate such influence, we used the international trade network of wheat as an example, developed relevant covariates, and tested specialized statistical and machine learning methods. The results show that countries with higher differences in extreme weather stress tend to have higher import volumes and more trade partners. Trade partnerships are more likely to be established between countries with synchronous yield variations. These findings indicate that increase in heat stress and co-occurring yield loss could lead to future higher dependence on imports, especially for vulnerable import dependent nations, and affect the stability of wheat supply. Hence, the current international trade network needs to be improved by contemplating the patterns of extreme weather and yield synchrony among countries.

Satellite observations of coastal Louisiana indicate an overall land loss over recent decades, which could be attributed to climate- and human-induced factors, including sea level rise (SLR). Climate-induced hydrological change (CHC) has impacted the way flood control structures are used, altering the spatiotemporal water distribution. Based on “what-if” scenarios, we determine relative impacts of SLR and CHC on increased flood risk over southern Louisiana and examine the role of water management via flood control structures in mitigating flood risk over the region. Our findings show that CHC has increased flood risk over the past 28 years. The number of affected people increases as extreme hydrological events become more exceptional. Water management reduces flood risk to urban areas and croplands, especially during exceptional hydrological events. For example, currently (i.e., 2016-2020 period), CHC-induced flooding puts an additional 73km2 of cropland under flood risk at least half of the time (median flood event) and 65km2 once a year (annual flood event), when compared to a past period (1993-1997). A ten- to twenty-fold increase relative to SLR-induced flooding. CHC also increases population vulnerability in southern Louisiana to flooding; additional 9900 residents currently live under flood risk at least half of the time, and that number increases to 27,400 for annual flood events. Residents vulnerable to SLR-induced flooding is lower (6000 and 3300 residents, respectively). Conclusions are that CHC is a major factor that should be accounted for flood resilience and that water management interventions can mitigate risks to human life and activities.

Understanding terrestrial ecosystems and their response to anthropogenic climate change requires quantification of land-atmosphere carbon exchange. However, top-down and bottom-up estimates of large-scale land-atmosphere fluxes, including the northern extratropical growing season net flux (GSNF), show significant discrepancies. We develop a data-driven metric for the GSNF using atmospheric carbon dioxide concentration observations collected during the High-Performance Instrumented Airborne Platform for Environmental Research (HIAPER) Pole-to-Pole Observations (HIPPO) and Atmospheric Tomography Mission (ATom) flight campaigns. This aircraft-derived metric is bias corrected using three independent atmospheric inversion systems. We estimate the northern extratropical GSNF to be 5.7 ± 0.2 Pg C and use it to evaluate net biosphere productivity from the Coupled Model Intercomparison Project phase 5 and 6 (CMIP5 and CMIP6) models. While the model-to-model spread in the GSNF has decreased in CMIP6 models relative to that of the CMIP5 models, there is still disagreement on the magnitude and timing of seasonal carbon uptake with most models underestimating the GSNF and overestimating the length of the growing season relative to the observations. We also use an emergent constraint approach to estimate annual northern extratropical gross primary productivity to be 56 ± 15 Pg C, heterotrophic respiration to be 25 ± 11 Pg C, and net primary productivity to be 28 ± 10 Pg C. The flux inferred from these aircraft observations provides an additional constraint on large-scale, gross fluxes in prognostic Earth system models that may ultimately improve our ability to accurately predict carbon-climate feedbacks.

The 2021 Pacific Northwest heatwave featured record-smashing high temperatures, raising questions about whether extremes are changing faster than the mean, and challenging our ability to estimate the probability of the event. Here, we identify and draw on the strong relationship between the climatological higher-order statistics of temperature (skewness and kurtosis) and the magnitude of extreme events to quantify the likelihood of comparable events using a large climate model ensemble (CESM2-LE). In general, CESM2 can simulate temperature anomalies as extreme as those observed in 2021, but they are rare: temperature anomalies that exceed 4.5σ occur with an approximate frequency of one in a hundred thousand years. The historical data does not indicate that the upper tail of temperature is warming faster than the mean; however, future projections for locations with similar climatological moments to the Pacific Northwest do show significant positive trends in the probability of the most extreme events.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission has collected global surface elevation measurements for over three years. ICESat-2 carries the Advanced Topographic Laser Altimeter (ATLAS) instrument, which emits laser light at 532 nm, and ice and snow absorb weakly at this wavelength. Previous modeling studies found that melting snow could induce significant bias to altimetry signals, but there is no formal assessment on ICESat-2 acquisitions during the Northern Hemisphere melting season. In this work, we performed two case studies over the Greenland Ice Sheet to quantify volumetric scattering in ICESat-2 signals over snow. Elevation data from ICESat-2 was compared to Airborne Topographic Mapper (ATM) data to quantify bias. We used snow optical grain sizes derived from ATM and the Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) to attribute altimetry bias to snowpack properties. For the first case study, the mean optical grain sizes were 340±65 µm (AVIRIS-NG) and 670±420 µm (ATM), which corresponded with a mean altimetry bias of 4.81±1.76 cm in ATM. We observed larger grain sizes for the second case study, with a mean grain size of 910±381 µm and biases of 6.42±1.77 cm (ICESat-2) and 9.82±0.97 cm (ATM). Although these altimetry biases are within the accuracy requirements of the ICESat-2 mission, we cannot rule out more significant errors over coarse-grained snow, particularly during the Northern Hemisphere melting season.

The spatial scales of whistler-mode waves, determined by their generation process, propagation, and damping, are important for assessing the scaling and efficiency of wave-particle interactions affecting the dynamics of the radiation belts. We use multi-point wave measurements by two Van Allen Probes in 2013-2019 covering all MLTs at L=2-6 to investigate the spatial extent of active regions of chorus and hiss waves, their wave amplitude distribution in the source/generation region, and the scales of chorus wave packets, employing a time-domain correlation technique to the spacecraft approaches closer than 1000 km, which happened every 70 days in 2012-2018 and every 35 days in 2018-2019. The correlation of chorus wave power dynamics using is found to remain significant up to inter-spacecraft separations of 400 km to 750 km transverse to the background magnetic field direction, consistent with previous estimates of the chorus wave packet extent. Our results further suggest that the chorus source region can be slightly asymmetrical, more elongated in either the azimuthal or radial direction, which could also explain the aforementioned two different scales. An analysis of average chorus and hiss wave amplitudes at separate locations similarly shows the reveals different radial and azimuthal extents of the corresponding wave active regions, complementing previous results based on THEMIS spacecraft statistics mainly at larger L>6. Both the chorus source region scale and the chorus active region size appear smaller inside the outer radiation belt (at L< 6) than at higher L-shells.

Despite strong impacts that aerosols have on climate and air quality, significant gaps remain in our knowledge concerning their long-range transport, especially extreme transport events. With this consideration in mind and by leveraging the “atmospheric river” concept, this work develops an objective global algorithm for detecting aerosol atmospheric rivers (AARs), shows a climatology of AARs, elucidates their contributions to major global aerosol transport pathways, and illustrates how AARs can drive extreme cases of poor air quality conditions. Our methodology separately accounts for dust, carbonaceous (accounting for organic and black carbon separately where appropriate), sea salt and sulfate aerosols. Findings show there are a number of long-range regional transport pathways where AARs account for a sizable fraction (40-80%) of the total transport in relatively few events (20-40 AAR days/year). This study highlights the role of AARs in establishing source-receptor relationships that can drive regional air-quality and extremes.

The usage of the term Knowledge Graph (KG) has gained significant popularity since 2012, when Google introduced its own knowledge graph, and how they used it to enhance their searches and question answering systems. While various definitions and interpretations for knowledge graphs have been presented, what remains consistent is that knowledge graphs are commonly used with reasonsers to make inferences about data, based on assertions and axioms written by human experts. But knowledge graphs, which store complex, multi-dimensional data contain hidden patterns and trends that cannot be explored simply using reasoners. In such a case it becomes necessary to extract parts of the knowledge graph (focusing on the instances related to one property at a time) and analyze them individually in order to conduct a focused but tractable exploration of the domain. In this presentation, we present one way to gain insights from knowledge graphs, using network science. To achieve this goal, we have formalised the partitioning of knowledge graphs to unipartite knowledge networks, and present various ways to explore and analyse such knowledge networks to form scientific hypotheses, gain scientific insights and make discoveries.