The 4 July 2019 Mw 6.4 Earthquake and 5 July Mw 7.1 Earthquake struck near Ridgecrest, California. Caltech-Jet Propulsion Laboratory Advanced Rapid Imaging and Analysis (ARIA) project automatically processed synthetic aperture radar (SAR) images from Copernicus Sentinel-1A and -1B satellites operated by the European Space Agency, and products were delivered to the US and California Geological Surveys to aid field response. We integrate geodetic measurements for the three-dimensional vector field of coseismic surface deformation for thee two events and measure the early postseismic deformation, using SAR data from Sentinel-1 satellites and the Advanced Land Observation Satellite-2 (ALOS-2) satellite operated by Japanese Aerospace Exploration Agency. We combine less precise large-scale displacements from SAR images by pixel offset tracking or matching, including the along-track component, with the more precise SAR interferometry (InSAR) measurements in the radar line-of-sight direction and intermediate-precision along-track InSAR to estimate all three components of the surface displacement for the two events together. InSAR coherence and coherence change maps the surface disruptions due to fault ruptures reaching the surface. Large slip in the Mw 6.4 earthquake was on a NE-striking fault that intersects with the NW-striking fault that was the main rupture in the Mw 7.1 earthquake. The main fault bifurcates towards the southeast ending 3 km from the Garlock Fault. The Garlock fault had triggered slip of about 15 mm along a short section directly south of the main rupture. About 3 km NW of the Mw 7.1 epicenter, the surface fault separates into two strands that form a pull-apart with about 1 meter of down-drop. Further NW is a wide zone of complex deformation. We image postseismic deformation with InSAR data and point measurements from new GPS stations installed by the USGS. Initial analysis of the first InSAR measurements indicates the pull-apart started rebounding in the first weeks and the main fault had substantial afterslip close to the epicenter where the largest coseismic slip occurred. Slip on a NE-striking fault near the northern end of the main rupture in the first weeks, in the same zone as large and numerous aftershocks along NE-striking and NW-striking trends shows complex deformation.

Microplastics have become ubiquitous in all reaches of the world. Due to their small size, low density, and environmental persistence, they are transported throughout the Earth's system. Despite its importance, little is known about microplastic transport and deposition, especially by snow particles, and most people are not aware of the extent of the problem. The PlastiX-Snow Citizen Science Project aims to fill these research and informational gaps using crowd-sourcing to achieve scientific research outputs, educational programming, and active outreach and engagement. We will initially measure the spatial distribution of snow deposited microplastics throughout a region in New York State and expand nationally using community partners. As trained partners, the Snow Ambassadors will inform the local community about microplastics, recruit participants, and assist in leading trainings. As nodes of the project, they will expand the reach to a large demographic of people across the country, including both life-long learners and school groups. Citizen scientists will collect, examine, and report the snow-deposited microplastics in their own backyard. The PlastiX-Snow team also will collect snowmelt samples from participants to robustly analyze the microplastics at Lamont-Doherty Earth Observatory. According to a National Academy of Sciences, a primary concern regarding citizen science projects is the lack of engagement and feedback to the participants of the program's findings. We specifically address these challenges by actively and continually engaging our participants and partners through direct and virtual public programming, classroom visits, media, newsletters, and an interactive website. PlastiX-Snow goals are to 1. Collect data for a deeper understanding of microplastics disseminated by snow, 2. Teach the public about the dangers of microplastics and potential solutions, 3. Engage communities, students, educators, and the public to participate in groundbreaking, relevant scientific research. We aim to shed light on the severity of microplastic pollution, build a bridge between the public and the scientific community, connect citizen scientists to their natural environment through field work, and encourage them to serve as environmental stewards and leaders in their own communities.

Historical volcanic eruptions in Canary Islands have traditionally been considered as quiet eruptions, mostly effusive and with scant explosions (VEI 1-2), attending to the general characteristics of this volcanism. Nevertheless, the geological record of these eruptions shows abundant signs of moderately explosive activity with PDC desposits and bombs and blocks of considerable size outcropping in the islands. Scientific studies of Canarian historical volcanism have been focused on the analysis of the volcanic processes and the assessment of related hazards, paying special attention to the monitoring of volcanic activity in the last few years. However, there is hardly any data of their effects on the population, like number of victims, affected people, displaced, emigrated, damage to the crops, lands, animals, etc. In general, there is also no accurate information on the actions of authorities or on the crises management. The knowledge of the impacts caused by volcanic eruptions provides very significant information to be prepared for future eruptions, and can be found in documentary sources, both handwritten and printed and graphic, in official or private documentation. The analysis of these historical chronicles has shed light on these aspects not previously considered neither by the authorities nor the scientist or general public. The results of this research examining the historical chronicles shows that, contrary to what is generally believed, historical eruptions in Canary Islands did cause several impacts both on the population (dead, injured or missing, displaced and evacuated), and on the economy (damage to crops, livestock, buildings, roads and various infrastructures). The historical documents also highlight that there was indeed crisis management, both from the political and scientific authorities in most of the eruptions. From all these we can conclude that no social interpretation has been made about the legacy of historical eruptions in Canary Islands nor about the impact they had. We should implement this knowledge in the prevention and emergency plans made by the authorities for a better management of future eruptions, considering the population increase in the Canary Islands from ca. 360000 inhabitants in 1900 up to ca. 2.13 millions in 2018, plus some millions of tourists (15.5 in 2018).

The Seismometer to Investigate Ice and Ocean Structure (SIIOS) project is exploring the science capabilities of seismometers in ocean world analog environments. Ocean worlds, such as Europa, Enceladus and Titan, have thick global icy shells overlying liquid oceans. The icy shells may be seismically active due to tidal stresses. SIIOS tests several seismometers in a small-aperture array in a mock-lander configuration to quantify the ability to detect, locate, and identify seismic sources, as well as constrain local ice structure. The SIIOS experiment was deployed on two terrestrial analogs for ocean worlds. We first deployed on Gulkana Glacier in Alaska in September 2017, and then deployed in Northwestern Greenland, over a subglacial lake from May 2018-August 2018. Both areas serve as analog locations for Europa due to the layering of ice, water and rock. Gulkana was a relatively noisy site due to surface runoff and drainage, higher topographic variation (inducing rockfalls), and proximity to active plate boundaries. Greenland was a quieter site, in part due to its geologic setting high on the ice sheet, as well as from the installation process. During the Greenland deployment, we covered instruments with a large aluminum box that was buried, thus reducing noise from atmospheric and thermal effects. At both analog sites, the instruments passively recorded seismicity and seismic background noise. The passive data was used to create power spectral density (PSDs) and then probability density functions (PDFs), of the background noise. The PDFs of Gulkana showed higher noise levels compared to those of Greenland. Using the passive data, we detected and identified events originating from ice quakes, and in the case of Gulkana; rockfalls and drainage from a nearby moulin. A frequency-dependent polarization analysis was also conducted to indicate the dominant directionality of the background signals through time. The results indicate how background or ambient signals could be used on ocean worlds to characterize the local seismicity.

Polygonal Fault Systems (PFS) are an enigmatic class of small non-tectonic extensional faults. PFS are predominantly hosted in fine-grained sedimentary tiers and are prevalent along many continental margin basins. The genesis of PFS is widely debated and little is known about the timeframe for polygonal fault growth. We present the first measurements of throw rates for polygonal faults by measuring the vertical offset of seven age-calibrated horizons mapped using three-dimensional seismic reflection data from the Norwegian Sea. Individual polygonal faults exhibit a range of throw rate profiles through time, ranging from near linear to singly or multiply stepped. The stepped profiles have a range of short-term throw rates ranging from 0 to 18 m/Ma. Time-averaged throw rates of 180 polygonal faults over the entire 2.61-0 Ma interval are normally distributed and range between 1.4-10.9 m/Ma. We convert our PFS throw rates to displacement rates and compare these to published displacement rates for gravity driven and tectonic normal faults. We find that the displacement rates of polygonal faults mark the lower limit of a continuous spectrum of extensional fault displacement rates; they are up to two orders-of-magnitude slower than gravity-driven faults, and up to three orders-of-magnitude slower than the fastest growing tectonic faults. We attribute the ultra-slow kinematic behaviour to the non-tectonic nature of polygonal faults where throw accumulates primarily through dewatering of the largely fine-grained sediments comprising the host layers for the PFS, and differential volumetric strain between the fault footwalls and hangingwalls.

Soils of mountain regions are estimated to contain large amounts of organic matter (OM), equivalent to stocks found in high-latitude boreal and tundra soils. Mountain environments are also experiencing profound changes in land management under the influence of socio-economic pressures as well as the need to adapt to climate change, which is occurring at a faster rate than in lowland areas. These anthropogenic impacts are expected to strongly affect soil OM storage. Most studies of land-use change have however focused on topsoil OM; whether similar trends will hold true for subsoil OM remains unknown. Using Rock-Eval pyrolysis as a proxy for soil OM dynamics, we showed that the hierarchy of controls on OM properties and transformations varied greatly with increasing soil depth. In the topsoil, OM properties were related to the nature of plant inputs, their degree of in-mixing with the mineral matrix and the occurrence of seasonal water saturation. In the subsoil however, the foremost predictors of OM properties were geochemical parameters. This shift in the nature of determinants of OM dynamics indicates that shallow and deep soil OM pools should respond differently to external forcings. Podzolic profiles showed the strongest decoupling of topsoil and subsoil OM properties. We focused on this soil type to specifically investigate the effects of land use on subsoil OM. We selected field sites from the Coastal Range of British Columbia, Canada and the Pennine Alps, Switzerland representing undisturbed and managed forest, shrubland and pasture. Samples were analyzed for organic C content, OM quality and reactive mineralogy. Results showed that herbaceous cover was associated with an increase in topsoil but not subsoil OM. In the subsoil, variations in OM content and properties were associated with changes in reactive Al and Fe mineral phases. Overall, our data indicate that organo-mineral and organo-metal interactions are of prime importance to OM accumulation in the subsoil, and that understanding the response of deep soil C stocks to land use change will require consideration of the geochemical and mineralogical environment. Our results further suggest that so-called reactive mineral phases may themselves be impacted by land use, in turn affecting deep soil C stabilization and destabilization processes.

Seismological models of the mantle are routinely developed using a range of techniques applied to different data types. For quite some time, it has been recognised that on long wavelengths models of shear-wave velocity variations show a large degree of consistency. More recently, the same has been suggested for models that describe compressional-wave velocity variations. However, controversy remains regarding models of lower mantle density variations, which provide important constraints on the nature of mantle structures, e.g. whether they are caused by thermal variations or whether additional chemical heterogeneity is required. The imaging of density structure is difficult due to a small effect on seismic observables and a strong trade-off with core-mantle boundary (CMB) topography. In addition, no consistent model of CMB topography variations exists with current models differing both in amplitude and pattern. Here, I review models of lower mantle density structure and core-mantle boundary topography from the literature, with the aim to identify which structures are consistent and what we can already learn from these models. In addition, I discuss ways in which differences between existing models may be resolved in future.

Useful to Usable (U2U) was a 6-year USDA-funded research and extension project focused on improving the uptake of climate information by Midwestern U.S. farmers and agricultural advisors. Led by Purdue University, this interdisciplinary team from nine Midwestern universities developed five web-based decision support tools to examine production, financial, and environmental outcomes of different climate scenarios and management options. A dedicated team of Extension educators, marketing specialists, and program evaluators worked together to promote U2U products and collect, analyze, and communicate data to 1) help increase project impact and 2) to measure outcomes for accountability purposes. Since outreach began in July 2013, farmers and advisors have been reached at 165 outreach events and training sessions in ten Midwestern states and at least 6 regional webinars have been conducted. A four-round mailed and electronic marketing campaign reached an estimated 35,600 people from March 2015 – November 2016. Additionally, the team developed a variety of educational materials (user guides, fact sheets, presentations, etc.) to support U2U outreach and dissemination efforts. Throughout 2016-2017, the team evaluated the outcomes, impacts, and overall reach of the U2U project over the 6-year project duration. They conducted two large-scale surveys with farmers and agricultural advisors, conducted personal interviews with advisors in Iowa and Nebraska, gathered informal success stories from U2U team members and collaborators, and tracked website traffic using Google Analytics. This presentation will highlight the project’s evaluation design, results, and lessons learned, including evaluation of outreach and dissemination approaches and longer-term outcome/impact evaluation. Some key metrics include the likelihood of using U2U online tools, actual use of tools, use of tools to aid financial and environmental decisions, and willingness to consider climate information in the future (not limited to U2U tools).

Terrestrial-aquatic interfaces such as salt marshes, mangroves, and similar wetlands provide an optimum natural environment for the sequestration and long-term storage of carbon (C) from the atmosphere, commonly known as coastal blue carbon. There are over 4 million acres of salt marsh in the US and over half of these are along the east coast of the US. Due to anthropogenic activities, this area presents the greatest nitrogen (N) pollution problem in coastal ecosystems in the U.S. as part of atmospheric N deposition, runoff, and riverine export. Ammonia (NH3) is the most abundant alkaline gas in the atmosphere. Agricultural intensification is the primary anthropogenic source of NH3 leading to a doubling of reactive nitrogen (Nr) entering the biosphere. Despite this, there are limited atmospheric measurements of NH3 concentrations in coastal areas along the east coast. The objective of this study is to advance our process-level understanding of NH3 air-surface exchange over a tidal salt marsh at the Saint Jones Reserve (DE), which is part of the National Estuarine Research Reserve System (NERRs). Continuous and high temporal resolution measurements of atmospheric NH3 concentrations were measured using a cavity ring-down spectrometer, reporting 30 min concentration averages. These high temporal resolution measurements allowed the estimation of the average diurnal cycle of NH3 fluxes using a new analytical methodology. Micrometeorological measurements were also measured using the eddy covariance system operated concurrently above the tidal marsh at the research site, which is part of the AmeriFlux network (US-StJ). This pilot study represents one of the few atmospheric measurements of NH3 over a tidal salt marsh in the eastern U.S. Such measurements are important to characterize the processes that influence the exchanges of NH3 between the atmosphere and the aquatic surface and provide baseline data to form more reliable parameterizations to simulate NH3 deposition and emissions in tidal salt marshes using surface-atmosphere transfer models.

While flagship missions such as CHAMP and GOCE have shown us with accelerometer measurements that the thermospheric density in Low Earth Orbit (LEO) can increase by more than 200% during enhanced geomagnetic activity, current empirical models, such as those of the MSISE and Jacchia families, as well as the Drag Temperature Model, fail to reproduce this behavior, limiting the ability to perform orbit prediction and space situational awareness. Several methods have been employed to address this dilemma. One is the High-Accuracy Satellite Drag Model (HASDM), which uses its Dynamic Calibration Atmosphere to employ differential correction across 75 spherical calibration satellites to generate correction parameters to the density that are related to 10.7 cm solar radio flux and ap (Storz et al. 2005). Doornbos et al. 2008 has implemented a method that estimates height-dependent scale factors to the densities from empirical models with respect to densities directly derived from two- line element sets (TLEs). HASDM’s reliance on Space Surveillance Network observations limit its accessibility and detail, and Doornbos’ methods are limited by the fact that TLEs are mean elements; densities derived from them are subject to errors due to smoothing over an entire orbit. In addition, the method of deriving densities from TLEs was initially done only to provide inputs to the SGP4 orbital propagator, which was initially developed without consideration of solar radiation pressure on the trajectory of modeled spacecraft. We present a method to generate new model densities during geomagnetic storms by using an in-house orbital propagator, the Spacecraft Orbital Characterization Kit (SpOCK). This method estimates and applies scale factors to F10.7 and a p to minimize orbit propagation errors with TLEs. The method is tested on a variety of satellites, including CHAMP, GOCE, and the CubeSats of the QB50 and FLOCK constellations. This method proposes to grant insight into storm-time thermospheric density enhancement by modeling the effects of storms on the drag of numerous LEO spacecraft, increasing our understanding of thermospheric dynamics and granting us improved tools for space traffic management and thermospheric research.

This study discusses the role of the aridity index in controlling the long-term fluxes of direct (Qd) and baseflow (Qd). Here we present an analytical derivation starting from similar assumptions as the one presented in the original work of Budyko that leads to two simple expressions for the control of the aridity index on Qb and Qd. These two analytical equations can be combined into a single expression with the same properties as the original Budyko equation. Our study provides a method for predicting long term baseflow and direct runoff for poorly gaged basins as well as permit the derivation of sensitivity equations for the assessment of the impact of these fluxes with respect to changes in aridity index.

Forest biodiversity has been declining across the globe due to anthropogenic activities. Losses of biodiversity have led to reduced forest health and ecosystem services. Therefore, it has become necessary to monitor changes in biodiversity over wide geographic areas. Remote sensing has the potential to monitor biodiversity changes, but the accuracy in which it can be estimated is under debate. In this study, we tested 1) the relationship among distinct metrics of biodiversity, 2) the role topographic measures have on determining biodiversity, and 3) the ability of hyperspectral remote sensing to estimate biodiversity in temperate forests of the Northeastern United States. We characterized biodiversity according to four different metrics: species, functional, structural, and phylogenetic diversity. All four metrics were quantified using species inventory data as well as Light Detecting and Ranging (LiDAR) to calculate additional indices of structural diversity. A digital elevation model was used to obtain measures of slope, aspect, and other topographic indices such as topographic wetness. Hyperspectral imagery was used to obtain reflectance, entropy, and several vegetation indices. In our analyses, species, functional, and phylogenetic diversity were shown to be moderately correlated suggesting similarities between the metrics while correlations between structural diversity and the other metrics were weak. The calculated topographic indices also showed weak correlations with the biodiversity metrics suggesting that topography does not influence measures of biodiversity at the plot level. Depending on the biodiversity metric, relationships between the hyperspectral analyses and biodiversity were weak to moderately strong. These findings suggest that hyperspectral imagery holds some potential for estimating multiple metrics of biodiversity.

Stochastic physics (SP) schemes provide a more realistic representation of the unresolved scales in global circulation models by improving both mean climate and climate variability. We study the impact of including an SP scheme in the atmospheric component of EC-Earth on the simulated climate. In particular, we analyze the evolution of the sea-ice extent in the Arctic during long-term simulations covering the historical and future periods. The experiments consist of coupled climate simulations in which three ensemble members constitute the control runs (base) and three ensemble members include stochastic physics (stoc). For the latter, the Stochastically Perturbed Parametrization Tendencies (SPPT) scheme is incorporated in the atmospheric component of EC-Earth. The original experiments, that are part of the SPHINX project, span from 1850 to 2100. We have additionally extended each simulation for 60 years; the future scenario corresponds to the CMIP5 RCP8.5 set up. We compare both sets of experiments to investigate the climate response to a perturbed atmosphere. The simulated Arctic sea-ice extent in September and March display an overall decrease. The sea-ice loss results faster in the base experiments than in the stoc ones. The model simulates an abrupt sea-ice loss in March that takes place about 10 years earlier in the base experiments than in the stoc ones. The evolution of the global annual mean surface air temperature differs if the SP is on or off. Curves start separating by the second half of the 20th century; reach the maximum difference in 2100 and become almost indistinguishable around 2110. Our results suggest that the transient climate sensitivity is lower when the SP is on than when it is off during the 21st century. However, the opposite occurs when the Arctic is free of sea ice along the whole year. This behavior might be the consequence of the asymmetric effect of stochastic perturbations on the process of condensation. We are now investigating the differences in the albedo and cloud feedbacks between both sets of experiments and the possible influence of the mean state on the model climate sensitivity.

Microbial processing of fresh carbon inputs is recognized as a key step in the formation of mineral-associated organic matter. Low molecular weight (LMW) compounds comprise a notable fraction of these inputs and are rapidly assimilated and metabolized by the microbial community. In this work, we employ ecophysiological studies of microbial isolates to better understand the role of substrate identity as a control on preferences, uptake kinetics, and carbon use efficiencies (CUE) across a gradient of phylogenetic differences (gram negative, gram positive, and fungal). Soil-extracted, solubilized organic matter (SESOM) derived from the Oa horizon of a hemlock-hardwood forest stand and synthetic media based off of this extract were chosen as liquid media for batch growth studies. A combination of exometabolomic techniques (1H NMR, UHPLC-MS) were used to quantify 35 LMW substrates in the original extract (0.4 – 195 μM), comprising 19.5% of total C and 39.9% of total N. Consumption of these substrates by microbial isolates accounted for a substantial amount of total C and N assimilated during growth, representing 43-75% and 58-74%, respectively. Time resolved sampling allowed modeling of sigmoidal uptake curves and the comparison of the midpoint of consumption (Th, hr) and 90% usage windows (ranging from 0.18 – 2.29 hr). Complementary experiments were conducted using synthetic media with all substrates at equimolar concentrations (25 μM) to better constrain the impact of initial concentration. We use stable isotope probing to determine CUE for five different LMW substrates of interest (glucose, acetate, formate, glycine, and valine). Ultimately, we are interested in whether unifying trends can be observed across the physiological gradient and how the metabolic transformations of these inputs may impact the organo-mineral formation process.

Agriculture sector is a major consumer of available fresh water. Increasing demand for fresh water in the various sectors has necessitated the use of treated waste water for irrigation. This water has marginal amount of salts which are known to affect the soil properties and crop growth. Hence a scientific approach is necessary to model the moisture uptake by plant roots under varying saline conditions. A numerical model is developed for solving the one dimensional Richards equation for moisture movement in variably saturated vadose zone. The root water uptake is incorporated in the model as the sink term as per the Ojha and Rai (1996) model, which is a non-linear root water uptake model. Combined water and salinity stress in the root water uptake is incorporated as per Feddes et al. (1978) and Mass and Hoffman (1977). Effect of varying salinity concentrations on the water uptake by plant roots and soil moisture dynamics in the root zone is studied on a 40 day period of crop growth. This is attained by providing fresh water and saline water irrigation with 15, 30 and 50 dS/m electrical conductivity. Irrigation field experiments of winter wheat crop were carried out to measure the crop parameters used in the analysis like leaf area index and root depth. The analysis to study the effect of salinity on soil water retention (SWR), hydraulic parameters and plant root water uptake was performed for silt loam (fine textured) and sandy loam (coarse textured) soils. The results show that the roots extract water at potential rate with fresh water, with no water uptake in the case of higher salinity as osmotic stress reaches permanent wilting phase. Overall, the root water uptake reduces as the concentration of salinity increases, even if there is no water stress. However, water stress was encountered at an earlier time in the case of sandy loam soil than in the case of silt loam. As the water content falls below the available moisture content, the water uptake is affected by water stress, which affects the growth and yield of crops. The results obtained from the study are useful in the better management of available water resources for irrigation practices in crop production.

Pesticide seed treatments (PST) which contain fungicides and insecticides are commonly used in agriculture; however, little is known about their effect on soil microbial communities and soil health. Neonicotinoids – controversial insecticides which are common in PST – have received criticism due to potential non-target effects. While fungal pathogens need to be moderated, PST have the potential to disturb broader fungal communities which could lead to reduced nutrient cycling and poor soil health. Given the broad use of PST, their effect on soil fungi needs to be studied within the context of other agricultural management practices. For example, tillage regimes can result in distinct fungal communities which may respond differently to PST. An experimental site was established in 2013 with a corn-soy rotation under three tillage treatments: Full-till, Strip-till, and No-till. Since 2016, seeds with or without PST (fungicides and insecticides) were planted under each tillage regime in a fully factorial design. In 2018, bulk soil was collected from within rows while soy was growing. A range of soil physicochemical variables were measured, and soil function was determined with substrate-induced respiration and enzyme assays. DNA was extracted from soil and the ITS region was sequenced to determine fungal community structure and diversity. While tillage significantly affected fungal community structure (p < 0.01), there was no effect of PST on either community structure (p = 0.59) or diversity (p = 0.52). This indicates that PST does not affect bulk soil fungal communities; however, they may have an impact at different temporal or spatial scales than those studied here. Across all treatments, fungal community structure correlated with soil water holding capacity (rs = 0.23, p = 0.04) and electrical conductivity (rs = 0.26, p = 0.01). Despite not finding an effect of PST on fungal communities, we did find that PST increased potentially mineralizable nitrogen under no-till and shifted community level physiological profiles determined by substrate-induced respiration. These results suggest that while PST can affect certain aspects of soil health, there are no clear effects on the soil fungal community.

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.

Wind and solar energy technologies are, by their nature, variable. Variations in resource availability, based on weather patterns, occur on intra-day to inter-annual time scales. Many energy system models optimize over a single year of input weather and electricity demand data. Energy system planners need increased understanding of the variability in generation potential across multiple years and how this could impact model results. A system achieving 100% reliability modeled using Year A data will not necessarily achieve 100% reliability when applied to Year B data unless an overbuild safety margin is added. We demonstrate: 1) model results can vary significantly based on the year of data used, 2) adding wind and solar does not necessarily reduce the predictability of meeting reliability targets year-to-year and can improve predictability in many cases, and 3) we illustrate a method to derive safety margins to predictably meet 100% reliability year after year and find the least-cost option.