An accurate description of the state of the ionosphere is crucial for understanding the physics of Earth’s coupling to space, including many potentially hazardous space weather phenomena. To support this effort, ground networks of magnetometer stations, optical instruments, and radars have been deployed. However, the spatial coverage of such networks is naturally restricted by the distribution of land mass and access to necessary infrastructure. We present a new technique for local mapping of polar ionospheric electrodynamics, for use in regions with high data density, such as Fennoscandia and North America. The technique is based on spherical elementary current systems (SECS), which were originally developed to map ionospheric currents. We expand their use by linking magnetic field perturbations in space and on ground, convection measurements from space and ground, and conductance measurements, via the ionospheric Ohm’s law. The result is a technique that is similar to the Assimilative Mapping of Ionospheric Electrodynamics (AMIE) technique, but tailored for regional analyses of arbitrary spatial extent and resolution. We demonstrate our technique on synthetic data, and with real data from three different regions. We also discuss limitations of the technique, and potential areas for improvement.

Seasonal Ice Zone Reconnaissance Surveys (SIZRS) is a multi-investigator program of repeated ocean, ice, and atmospheric measurements. These measurements make use of U.S. Coast Guard flights across the Beaufort-Chukchi Sea seasonal sea ice zone (SIZ), the region between maximum winter ice extent and minimum summer ice extent. The long-term goal of SIZRS is to track and understand the interplay among the ice, atmosphere, and ocean, contributing to the rapid decline in summer ice extent. The fundamental SIZRS approach is to make monthly flights, June to October, with US Coast Guard Air Station Kodiak C-130s across the Beaufort Sea SIZ along 150°W from 72°N to 76°N or ~ 1 degree of latitude north of the ice edge, whichever is farther north. We make oceanography stations every degree of latitude by dropping Aircraft eXpendable CTDs (AXCTDs) and Aircraft eXpendable Current Profilers (AXCPs) typically while traveling northbound (PI: J. Morison). On the return leg, we drop atmospheric dropsondes from 3000 meters altitude to measure atmospheric temperature, humidity, and winds (PI: A. Schweiger). We also drop UpTempO drifting buoys that report time series of ocean temperature profiles (PI: M. Steele) and various meteorology and ice-tracking buoys of the International Arctic Buoy Program (IABP, PI: I. Rigor).

Approximately eight months of co-located spaceborne lidar and lightning observations were analyzed in a pathfinder study to understand the advantages and challenges of using these combined observations to understand thunderstorms. Data from the Lightning Imaging Sensor (LIS) and the Cloud-Aerosol Transport System (CATS) lidar were used when they overlapped on the International Space Station during March-October 2017. Using simple matching criteria, 8246 LIS flashes occurred within 25 km of the CATS ground track. CATS cloud-top heights near these flashes showed similar behavior with latitude when compared to a spaceborne radar-based climatology, but the lidar cloud tops were approximately 2-km higher than 20-dBZ radar echo tops. CATS cloud phase near LIS flashes was consistent with ice or mixed-phase more than 90% of the time, showing the value of using lightning observations to validate lidar-based feature masks. In addition, correlations between a proxy for LIS flash rate and CATS ice water path, cloud optical depth, and cloud-top height were low (0.38-0.42) but positive and highly statistically significant (> 99%), suggesting lidar retrievals of cloud properties can be meaningfully compared with lightning observations despite lidar’s known inability to penetrate deeply into optically thick clouds like thunderstorms. Finally, CATS was used to help diagnose LIS false alarms due to surface-based glint. The false alarm rate was approximately 0.1%, which demonstrated the excellent performance of the surface glint filter in the LIS processing code. The results suggest that fruitful scientific insights can be expected from larger combined lidar/lightning datasets.

The plant hydrodynamic approach represents a recent advancement to land surface modeling, in which stomatal conductance responds to water availability in the xylem rather than in the soil. To provide a realistic representation of tree hydrodynamics, hydrodynamic models must resolve processes at the level of a single modelled tree, and then scale the resulting fluxes to the canopy and land surface. While this tree-to-canopy scaling is trivial in a homogeneous canopy, mixed-species canopies require careful representation of the species properties and a scaling approach that results in a realistic description of both the canopy and individual-tree hydrodynamics, as well as leaf-level fluxes from the canopy and their forcing. Here, we outline advantages and pitfalls of three commonly used approaches for representing mixed-species forests in land surface models, and present a new framework for scaling vegetation characteristics and fluxes in mixed-species forests. The new formulation scales fluxes from the tree- to canopy-level in an energy- and mass-conservative way and allows for a consistent multi-species canopy description for hydrodynamic models.

Fossil fuel CO2 emissions (ffCO2) constitute the majority of greenhouse gas emissions and are the main determinant of global climate change. The COVID-19 pandemic caused wide-scale disruption to human activity and provided an opportunity to evaluate our capability to detect ffCO2 emission reductions. Quantifying changes in ffCO2 levels is especially challenging in cities, where climate mitigation policies are being implemented but local emissions lead to spatially and temporally complex atmospheric mixing ratios. Here, we used direct observations of on-road CO2 mole fractions with analyses of the radiocarbon (14C) content of annual grasses collected by community scientists in Los Angeles and California, USA to assess reductions in ffCO2 emissions during the first two years of the COVID-19 pandemic. With COVID-19 mobility restrictions in place in 2020, we observed a significant reduction in ffCO2 levels across California, especially in urban centers. In Los Angeles, CO2 enhancements on freeways were 60 ± 16% lower and ffCO2 levels were 43-55% lower than in pre-pandemic years. By 2021, California’s ffCO2 levels rebounded to pre-pandemic levels, albeit with substantial spatial heterogeneity related to local and regional pandemic measures. Taken together, our results indicate that a reduction in traffic emissions by ~60% (or 10-24% of Los Angeles’ total ffCO2 emissions) can be robustly detected by plant 14C analysis and pave the way for mobile- and plant-based monitoring of ffCO2 in cities without CO2 monitoring infrastructure such as those in the Global South.

This study focuses on methods to estimate dry marine aerosol surface area (SA) from bulk optical measurements. Aerosol SA is used in many models’ ice nucleating particle (INP) parameterizations, as well as influencing particle light scattering, hygroscopic growth, and reactivity, but direct observations are scarce in the Southern Ocean (SO). Two campaigns jointly conducted in austral summer 2018 provided co-located measurements of aerosol surface area from particle size distributions and lidar to evaluate SA estimation methods in this region. Mie theory calculations based on measured size distributions were used to test a proposed approximation for dry aerosol SA, which relies on estimating effective scattering efficiency (Q) as a function of Ångström exponent (å). For distributions with dry å<1, Q=2 was found to be a good approximation within ±50%, but for distributions with dry å>1, an assumption of Q=3 as in some prior studies underestimates dry aerosol surface area by a factor of 2 or more. We propose a new relationship between dry å and Q, which can be used for -0.2

This paper reports our simulations of the volume emission rate (VER) of the O($^1D$) redline nightglow perturbed by waves traveling across the thermosphere at around 250 km altitude. Waves perturb the electronic and neutral background densities and temperatures in the region and modify the O($^1D$) layer intensity as it is captured by ground-based nightglow instruments. The changes in the integrated volume emission rate are calculated for various vertical wavelengths of the perturbations. We demonstrate that, as the solar activity intensifies, the vertical scales of most likely observable TID waves become larger. For high solar activity, we demonstrate that only waves presenting vertical wavelengths larger than 360 km are likely to be observed. The variation of the range of likely observable vertical wavelengths with the solar cycle offers a plausible explanation for the low occurrence rate of TID in measurements of the redline nightglow during high solar activity periods. We have compared our results with those of \citeA{Negale:2018} and \citeA{Paulino:2018} to verify that observed vertical wavelengths distribute around 140-210 km, in good correspondence with our predicted threshold wavelength $\lambda_{z}^t\sim$160 km for very low solar cycle period.

We present simulations of the outer radiation belt electron flux during the March 2015 and March 2013 storms using a radial diffusion model. Despite differences in Dst intensity between the two storms the response of the ultra-relativistic electrons in the outer radiation belt was remarkably similar, both showing a sudden drop in the electron flux followed by a rapid enhancement in the outer belt flux to levels over an order of magnitude higher than those observed during the pre-storm interval. Simulations of the ultra-relativistic electron flux during the March 2015 storm show that outward radial diffusion can explain the flux dropout down to L*=4. However, in order to reproduce the observed flux dropout at L*<4 requires the addition of a loss process characterised by an electron lifetime of around one hour operating below L*~3.5 during the flux dropout interval. Nonetheless, during the pre-storm and recovery phase of both storms the radial diffusion simulation reproduces the observed flux dynamics. For the March 2013 storm the flux dropout across all L-shells is reproduced by outward radial diffusion activity alone. However, during the flux enhancement interval at relativistic energies there is evidence of a growing local peak in the electron phase space density at L*~3.8, consistent with local acceleration such as by VLF chorus waves. Overall the simulation results for both storms can accurately reproduce the observed electron flux only when event specific radial diffusion coefficients are used, instead of the empirical diffusion coefficients derived from ULF wave statistics.

The western tropical Pacific (WTP) exhibits large interannual sea level anomalies (SLAs), and the sea level falling in El Niño is evidently stronger than the rising in La Niña. The asymmetry is most prominent near 160°E with the response to El Niño larger by three times and becomes less obvious near the western boundary. Sensitivity experiments of a simplified ocean model suggest that the asymmetry in surface wind forcing structure between El Niño and La Niña is critical. The El Niño’s westerly wind anomaly patch locates more east than the La Niña’s easterly wind patch during the mature stage, and its upwelling effects are accumulated over a wider longitude range and cause stronger negative SLAs in the WTP. Near the western boundary, however, upwelling effects are attenuated by easterly wind anomalies during El Niño conditions. The asymmetric ocean responses to ENSO winds may participate in the asymmetry of ENSO cycle.

This study uses cloud and radiative properties collected from in-situ and remote sensing instruments during two coordinated campaigns over the Southern Ocean between Tasmania and Antarctica in January-February 2018 to evaluate the simulations of clouds and precipitation in nudged-meteorology simulations with the CAM6 and AM4 global climate models sampled at the times and locations of the observations. Fifteen SOCRATES research flights sampled cloud water content, cloud droplet number concentration, and particle size distributions in mixed-phase boundary-layer clouds at temperatures down to -25 C. The six-week CAPRICORN2 research cruise encountered all cloud regimes across the region. Data from vertically-pointing 94 GHz radars deployed was compared with radar-simulator output from both models. Satellite data was compared with simulated top-of-atmosphere (TOA) radiative fluxes. Both models simulate observed cloud properties fairly well within the variability of observations. Cloud base and top in both models are generally biased low. CAM6 overestimates cloud occurrence and optical thickness while cloud droplet number concentrations are biased low, leading to excessive TOA reflected shortwave radiation. In general, low clouds in CAM6 precipitate at the same frequency but are more homogeneous compared to observations. Deep clouds are better simulated but produce snow too frequently. AM4 underestimates cloud occurrence but overestimates cloud optical thickness even more than CAM6, causing excessive outgoing longwave radiation fluxes but comparable reflected shortwave radiation. AM4 cloud droplet number concentrations match observations better than CAM6. Precipitating low and deep clouds in AM4 have too little snow. Further investigation of these microphysical biases is needed for both models.

TanSat is the 1st Chinese carbon dioxide (CO) measurement satellite, launched in 2016. In this study, the University of Leicester Full Physics (UoL-FP) algorithm is implemented for TanSat nadir mode XCO retrievals. We develop a spectrum correction method to reduce the retrieval errors by the online fitting of an 8 order Fourier series. The model and a priori is developed by analyzing the solar calibration measurement. This correction provides a significant improvement to the O A band retrieval. Accordingly, we extend the previous TanSat single CO weak band retrieval to a combined O A and CO weak band retrieval. A Genetic Algorithm (GA) has been applied to determine the threshold values of post-screening filters. In total, 18.3% of the retrieved data is identified as high quality compared to the original measurements. The same quality control parameters have been used in a footprint independent multiple linear regression bias correction due to the stronger correlation with the XCO retrieval error. Twenty sites of the Total Column Carbon Observing Network (TCCON) have been selected to validate our new approach for the TanSat XCO retrieval. We show that our new approach produces a significant improvement on the XCO retrieval accuracy and precision when compared to TCCON with an average bias and RMSE of -0.08 ppm and 1.47 ppm, respectively. The methods used in this study can help to improve the XCO retrieval from TanSat and subsequently the Level-2 data production, and hence will be applied in the TanSat operational XCO processing.

We present a method of characterizing the horizontal and vertical electron density roughness of the D-region ionosphere using Nationwide Differential GPS (NDGPS) transmitters as Low Frequency (LF; 30-300 kHz) and Medium Frequency (MF; 300-3000 kHz) signals of opportunity. The horizontal roughness is characterized using an amplitude cross-correlation method, which yields the correlation length scale metric. The vertical roughness is characterized using a differential phase height, which is needed to mitigate the effects of transmitter phase instability. The ranges and typical values of roughness metrics are investigated using data from several field campaign measurements. Finally, the roughness metrics for an NDGPS transmitter and VLF transmitter are compared. It is found that the roughness detected by the VLF transmitter is significantly smoother and demonstrates the utility of this method to complement traditional VLF measurements.

Because of their limited spatial resolution, numerical weather prediction and climate models have to rely on parameterizations to represent atmospheric turbulence and convection. Historically, largely independent approaches have been used to represent boundary layer turbulence and convection, neglecting important interactions at the subgrid scale. Here we build on an eddy-diffusivity mass-flux (EDMF) scheme that represents all subgrid-scale mixing in a unified manner, partitioning subgrid-scale fluctuations into contributions from local diffusive mixing and coherent advective structures and allowing them to interact within a single framework. The EDMF scheme requires closures for the interaction between the turbulent environment and the plumes and for local mixing. A second-order equation for turbulence kinetic energy (TKE) provides one ingredient for the diffusive local mixing closure, leaving a mixing length to be parameterized. A new mixing length formulation is proposed, based on constraints derived from the TKE balance. It expresses local mixing in terms of the same physical processes in all regimes of boundary layer flow. The formulation is tested at a range of resolutions and across a wide range of boundary layer regimes, including a stably stratified boundary layer, a stratocumulus-topped marine boundary layer, and dry convection. Comparison with large eddy simulations (LES) shows that the EDMF scheme with this diffusive mixing parameterization accurately captures the structure of the boundary layer and clouds in all cases considered.

We demonstrate that an extended eddy-diffusivity mass-flux (EDMF) scheme can be used as a unified parameterization of subgrid-scale turbulence and convection across a range of dynamical regimes, from dry convective boundary layers, over shallow convection, to deep convection. Central to achieving this unified representation of subgrid-scale motions are entrainment and detrainment closures. We model entrainment and detrainment rates as a combination of turbulent and dynamical processes. Turbulent entrainment/detrainment is represented as downgradient diffusion between plumes and their environment. Dynamical entrainment/detrainment are proportional to a ratio of buoyancy difference and vertical velocity scale, partitioned based on buoyancy sorting approaches and modulated by a function of relative humidity difference in cloud layer to represent buoyancy loss owing to evaporation in mixing. We first evaluate the closures offline against entrainment and detrainment rates diagnosed from large-eddy simulations (LES) in which tracers are used to identify plumes, their turbulent environment, and mass and tracer exchanges between them. The LES are of canonical test cases of a dry convective boundary layer, shallow convection, and deep convection, thus spanning a broad range of regimes. We then compare the LES with the full EDMF scheme, including the new closures, in a single column model (SCM). The results show good agreement between the SCM and LES in quantities that are key for climate models, including thermodynamic profiles, cloud liquid water profiles, and profiles of higher moments of turbulent statistics. The SCM also captures well the diurnal cycle of convection and the onset of precipitation.

The intraseasonal variation of the Tibetan Plateau summer monsoon (TPSM) during 1979–2011 is investigated. The TPSM shows a dominant quasi-biweekly oscillation (QBWO) in most summer seasons, and its active/break phases are closely related to more/less precipitation over the Tibetan Plateau. We suggest that the TPSM QBWO is associated with a southeastward propagating nonstationary wave train in the middle and upper troposphere. It shows equivalent barotropic vertical structures over the midlatitudes and a baroclinic structure over the eastern Tibetan Plateau. Wave activity flux analysis indicates that it originates from northern Europe, which is an active center of the summertime Arctic Oscillation (AO). The AO also shows significant QBWO signals and leads TPSM QBWO by about 13 days. Phase composite and wave activity flux analyses of AO QBWO confirmed that the wave train influences TPSM QBWO, suggesting that AO plays an important role in the TPSM on a 10- to 20-day timescale.

An unanswered question in planetary science is how could the early Martian atmosphere have maintained a greenhouse effect sufficient to allow for liquid water on the surface? A recent study by Wordsworth et al. (DOI:10.1002/2016GL071766) suggested that previously unaccounted-for collision-induced absorption (CIA) by carbon dioxide (CO2) and hydrogen gas (H2), and by CO2 and methane (CH4) could provide the additional atmospheric absorption needed to trap enough radiation to raise the Martian surface temperature above freezing. However, as CIA cross-sections for CO2-H2 and CO2-CH4 complexes do not exist in the literature, the authors could only use computational methods to simulate the CIA absorption cross-sections that they themselves identify in the study as needing experimental validation. Preliminary results will be presented from experimental measurements of the CIA cross-sections for CO2-H2 and CO2-CH4 complexes performed using Fourier Transform Spectroscopy. We have obtained Beam-time at the Canadian Light Source Far-IR beamline in late October and early November which will allow us to derive Cross-sections over a spectral range of 0-3000 cm-1 and a temperature range of 200-350 K. In addition to allowing us to experimentally validate the hypothesis of Wordsworth, the cross-sections so obtained can also be applied to other planetary systems with CO2-rich atmospheres, such as Venus, and will be useful to terrestrial spectroscopists.

Human activities have been changing deposition rate of the atmospheric reactive N over the last decades. The understanding of the process that rules the accumulation and deposition of reactive nitrogen in the environment still faces major gaps mainly in regions with lack of data as South America. In this work, we evaluated the atmospheric dry deposition of reactive nitrogen (Nr) using concentration measurements of different chemical species, in six sampling sites with different environmental characteristics in the state of São Paulo, Brazil, for a sampling period of approximately 24 months (2015-2017). We used the sampling system DELTA (Sampler for Atmospheric Sampling of Long Term), and ion chromatography method to determine the concentration of gaseous (NH3 and HNO3), and particulate (NH4+ and NO3-) chemical species in the atmosphere. The N-NH3 (gaseous) was the dominant form of Nr in the atmosphere at all sampling sites. The highest N-NH3 mean value was found near the urban area of the municipality of São Paulo (SP, 1.58 ± 0.73 μgN m-3) and the lowest mean value in the most eastern sampling site (0.26 ± 0.26 μgN m-3). The high values in the SP sampling site is related to the intense traffic in the metropolitan area and, also to waste management, industrial NH3, and human emissions as observed in other urban areas around the world. The particulate N-NH4+ is the second most common form of Nr in the São Paulo state atmosphere. The other forms of Nr in the atmosphere, the gaseous HNO3 and particulate NO3, represent only about 10% of the total Nr in the atmosphere each. The total gaseous Nr concentration was a factor of 1.7 larger than particulate Nr. The Nr deposition varied throughout the years. We did not observe a pattern of variation linked to meteorological characteristics of dry and wet season, as observed in other regions of the globe. However, we found good correlation with wind speed higher than 3.5 m s-1 and humidity. Our results provide the first spatial analysis of Nr deposition using in situ data in a Latin American region and will contribute to the understanding of nitrogen balance and to improve Nr deposition modelling approaches. This study was supported by the project Nitrogen cycling in Latin America: drivers, impacts and vulnerabilities (Nnet, IAI/CRN3005 and FAPESP 2012/06416-1), PCI Program of the MCTIC, and collaborators.

As the sea level rises, it is alarming that the threat from flooding induced by tropical cyclones would cause more severe damages to coastal regions worldwide. In order to address this threat, optimizing coastal protective or mitigation strategies is necessary, given limited resources. The optimization methodology must incorporate feedback from stakeholders for practical use. Multiple interviews were conducted by engineering model developers and social scientists with stakeholders who have first-hand knowledge and varied backgrounds in New York. The protective strategies have been tuned to the critical infrastructure's particular and contextual risks due to flood hazards by engaging and integrating stakeholders' knowledge on the interdependency of the infrastructures and other aspects after the first interview. The second interview was conducted for further model improvement.

Some of the Earth system data products such as those from NASA airborne and field investigations (a.k.a. campaigns), are highly heterogeneous and cross-disciplinary, making the data extremely challenging to manage. For example, airborne and field campaign measurements tend to be sporadic over a period of time, with large gaps. Data products generated are of various processing levels and utilized for a wide range of inter- and cross-disciplinary research and applications. Data and derived products have been historically stored in a variety of domain-specific standard (and some non-standard) formats and in various locations such as NASA Distributed Active Archive Centers (DAACs), NASA airborne science facilities, field archives, or even individual scientists’ computer hard drives. As a result, airborne and field campaign data products have often been managed and represented differently, making it onerous for data users to find, access, and utilize campaign data. Some difficulties in discovering and accessing the campaign data originate from the incomplete data product and contextual metadata that may contain details relevant to the campaign (e.g. campaign acronym and instrument deployment locations), but tend to lack other significant information needed to understand conditions surrounding the data. Such details can be burdensome to locate after the conclusion of a campaign. Utilizing consistent terminology, essential for improved discovery and reuse, is also challenging due to the variety of involved disciplines. To help address the aforementioned challenges faced by many repositories and data managers handling airborne and field data, this presentation will describe stewardship practices developed by the Airborne Data Management Group (ADMG) within the Interagency Implementation and Advanced Concepts Team (IMPACT) under the NASA’s Earth Science Data systems (ESDS) Program.

Our objective is to test and improve cloud subcolumn generators used for greater realism of scales in the radiation schemes and satellite simulators GCMs. For this purpose, we use as guidance water content fields from active observations by the CloudSat radar (CPR) and the CALIPSO lidar (CALIOP). Cloud products from active sensors while suffering significant sampling and coverage drawbacks have the advantage of resolving both horizontal and vertical variability which is what the generators are designed to produce. Our first order goal is to test the ability of the generators to deliver realistic 2D cloud extinction (cloud optical thickness) fields using, as in GCMs, limited domain-averaged information. Our reference 2D cloud extinction fields fully resolving horizontal (along the track of the satellites) and vertical variability come from combining CloudSat’s 2B-CWC-RVOD (liquid clouds) and CALIPSO-enhanced 2C-ICE (ice clouds) products. The combined fields were improved by introducing a simple scheme to fill liquid cloud extinction values identified as missing by comparing with coincident 2D (phase-specific) cloud masks provided by the CALIPSO-enhanced 2B-CLDCLASS-LIDAR CloudSat product. Our presentation will demonstrate the substantial improvements for low clouds brought by the filling scheme through comparisons with MODIS-Aqua cloud fraction distributions expressed in terms of joint cloud top pressure – cloud optical thickness histograms. Beyond global comparisons, the nature of the improvements become clearer when comparing mean joint histograms segregated by MODIS Cloud Regime (CR): improvement is by design superior for MODIS CRs dominated by low clouds. With the improved 2D extinction fields at hand, we test the skill of two subcolumn generators, one used in the COSP satellite simulator package, and one with more sophisticated cloud overlap implemented in the GEOS global model, to reproduce joint histograms that are statistically similar to the observed counterparts described above (as interpreted by COSP’s MODIS simulator). Our main comparison metrics are the Euclidean distance between observed and generator-produced global or near-global mean joint histograms, and the statistics of Euclidean distances calculated for individual scenes. One full year of data is used to assess whether the more sophisticated cloud generator produces clouds with greater realism in 2D cloud variability.

Wildfire smoke influences on air quality and atmospheric chemistry have been underscored by the increasing fire prevalence in recent years, and yet, the connection between fire, smoke emissions, and the subsequent transformation of this smoke in the atmosphere remains poorly constrained. Toward improving these linkages, we present a new method for coupling high-time-resolution satellite observations of fire radiative power (FRP) with in situ observations of smoke aerosols and trace gases. We apply this technique to thirteen fire plumes comprehensively characterized during the recent FIREX-AQ mission and show that changes in FRP directly translate into changes in conserved smoke tracers (CO2, CO, and black carbon aerosol) observed in the downwind smoke plume. The correlation is particularly strong for CO2 (mean r>0.9). This method is important for untangling the competing effects of changing fire behavior versus the influence of dilution and atmospheric processing on the down-wind evolution of measured smoke properties.

Although lightning discharge is not the only source or only physical phenomenon that affects the Schumann resonances, they have the highest contribution to the Schumann resonances oscillating between the ground the ionosphere. Schumann resonances are predicted through several different numerical models such as the transmission-line matrix model or partially uniform knee model. Here we report a different prediction method for Schumann resonances derived from the first principle fundamental physics combining both particle radiation patterns and the mathematical concept of the Golden ratio. This prediction allows the physical understanding of where Schumann resonances originate from radiation emitted by a particle that involves many frequencies that are not related to Schumann resonances. In addition, this method allows to predict the wave propagation direction of each frequency value in the Schumann frequency spectrum. Particles accelerated by lightning leader tip electric fields are capable of contributing most of the Schumann resonances. The radiation pattern of a single particle consists of many frequencies that there are only specific ones within this pattern that contribute to the Schumann radiation. Vast majority of Schumann resonances distribute quite nicely obeying the Golden ratio interval. This property used in conjunction with the full single-particle radiation patterns also revealed that high-frequency forward-backward peaking radiation patterns as well as low-frequency radiation patterns can contribute to Schumann resonances. Moreover, this also allows to locate them on the full radiation pattern. Furthermore, theoretical analysis using Golden ratio spiral predicts that there are more Schumann resonances in high frequency forward-backward peaking radiation pattern of relativistic particle than low frequency dipole radiation pattern.

Here we assess to what extent the Empirical Canadian High Arctic Ionospheric Model (E-CHAIM) can reproduce the climatological variations of vertical Total Electron Content (vTEC) in the Canadian sector. Within the auroral oval and polar cap, E-CHAIM is found to exhibit Root Mean Square (RMS) errors in vTEC as low 0.4 TECU during solar minimum summer but as high as 5.0 TECU during solar maximum equinox conditions. These errors represent an improvement of up to 8.5 TECU over the errors of the International Reference Ionosphere (IRI) in the same region. At sub-auroral latitudes, E-CHAIM RMS errors range between 1.0 TECU and 7.4 TECU, with greatest errors during the equinoxes at high solar activity. This represents an up to 0.5 TECU improvement over the IRI during summer but worse performance by up to 2.4 TECU during the winter. Comparisons of E-CHAIM performance against in situ measurements from the European Space Agency’s Swarm mission are also conducted, ultimately finding behaviour consistent with that of vTEC. In contrast to the vTEC results, however, E-CHAIM and the IRI exhibit comparable performance at Swarm altitudes, except within the polar cap, where the IRI exhibits systematic underestimation of electron density by up to 1.0e11 e/m^3. Conjunctions with mid-latitude ionosondes demonstrate that E-CHAIM’s errors appear to result from compounding same-signed errors in its NmF2, hmF2, and topside thickness at these latitudes. Overall, E-CHAIM exhibits strong performance within the polar cap and auroral oval but performs comparably to the IRI at sub-auroral latitudes.

Ozone (O3) is an important trace and greenhouse gas in the atmosphere yet, and it threatens the ecological environment and human health at the ground level. Large-scale and long-term studies of O3 pollution in China are few due to highly limited direct measurements whose accuracy and density vary considerably. To overcome these limitations, we employed the ensemble learning method of the extremely randomized trees model by utilizing the spatiotemporal information of a large number of input variables from ground-based observations, remote sensing, atmospheric reanalysis, and model simulation products to estimate ground-level O3. This method yields uniform, long-term and continuous spatiotemporal information of daily maximum eight-hour average (MDA8) O3 over China (called ChinaHighO3) from 2013 to 2020 at a 10 km resolution without any missing values (spatial coverage = 100%). Evaluation against observations indicates that our O3 estimations and predictions are reliable with an average out-of-sample (out-of-station) coefficient of determination (CV-R2) of 0.87 (0.80) and root-mean-square error of 17.10 (21.10) μg/m3 [units here are at standard conditions (273K, 1013hPa)], and are also robust at varying spatial and temporal scales in China. This high-quality and full-coverage O3 dataset allows us to investigate the exposure and trends in O3 pollution at both long- and short-term scales. Trends in O3 concentrations varied substantially but showed an average growth rate of 2.49 μg/m3/yr (p < 0.001) from 2013 to 2020 in China. Most areas show an increasing trend since 2015, especially in summer ozone over the North China Plain. Our dataset accurately captured a recent national and regional O3 pollution event from 23 April to 8 May in 2020. Rapid increase and recovery of O3 concentrations associated with variations in anthropogenic emissions were seen during and after the COVID-19 lockdown, respectively. This carefully vetted and smoothed dataset is valuable for studies on air pollution and environmental health in China.