The dynamic topography of the core-mantle boundary (CMB) provides important constraints on dynamic processes in the mantle and core. However, inferences on CMB topography are complicated by the uneven coverage of data with sensitivity to different length scales and strong heterogeneity in the lower mantle. Particularly, a trade-off exists with density variations, which ultimately drive mantle flow and are vital for determining the origin of mantle structures. Here, I review existing models of CMB topography and lower mantle density, focusing on seismological constraints. I develop average models and vote maps with the aim to find model consistencies and discuss what these may teach us about lower mantle structure and dynamics. While most density models image two areas of dense anomalies beneath Africa and the Pacific, their exact location and relationship to seismic velocity structure differs between studies. CMB topography strongly influences the retrieved density structure, which helps to resolve differences between recent studies based on Stoneley modes and tidal measurements. Current CMB topography models vary both in pattern and amplitude and a discrepancy exists between models based on body-wave and normal-mode data. As existing models feature elevated topography below the Large-Low-Velocity Provinces (LLVPs), very dense compositional anomalies may currently be ruled out as possibility. To achieve a similar consistency as observed in lower mantle models of S-wave and P-wave velocity, future studies should combine multiple data sets to break existing trade-offs between CMB topography and density. Important considerations in these studies should be the choice of theoretical approximation and parameterisation. Efforts to develop models of CMB topography consistent with body-wave, normal-mode and geodetic data should be intensified, which will aid in narrowing down possible explanations for the LLVPs and provide additional insights into mantle dynamics.

Dry deposition is one of the driving factors behind ozone-meteorology relationships. We examine the response of ozone deposition to heat and dry anomalies using three long-term co-located ecosystem-scale carbon dioxide, water vapor and ozone flux measurement records. We find that, as expected, canopy stomatal conductance generally decreases during days with dry air or soil. However, during hot days, concurrent increases in non-stomatal conductance are inferred at all three sites, which may be related to several temperature-sensitive processes not represented in the current generation of big-leaf models. This may offset the reduction in stomatal conductance, leading to smaller net reduction, or even net increase, in total deposition velocity. We find the response of deposition velocity to soil dryness may be related to its impact on photosynthetic activity, though considerable variability exists. Our findings emphasize the need for better understanding and representation of non-stomatal ozone deposition processes.

A new technique has been developed in which the high-latitude electric potential is determined from field-aligned current observations from the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE) and conductances modeled by Sami3 is Also a Model of the Ionosphere (SAMI3). This is a development of the Magnetosphere-Ionosphere Coupling (MIX) approach first demonstrated by Merkin and Lyon (2010). An advantage of using SAMI3 is that the model can be used to predict Total Electron Content (TEC) in the polar caps, based on the AMPERE-derived potential solutions. 23 May 2014 is chosen as a case study to assess the new technique for a moderately disturbed case (min Dst: -36 nT, max AE: 909 nT) with good GPS data coverage. The new AMPERE/SAMI3 solutions are compared against independent GPS-based TEC observations from the Multi-Instrument Data Analysis Software (MIDAS) by Mitchell and Spencer, 2003, and against Defense Meteorological Satellite Program (DMSP) ion drift data. The comparison shows excellent agreement between the location of the tongue of ionization in the MIDAS GPS data and the AMPERE/SAMI3 potential pattern, and good overall agreement with DMSP drifts. SAMI3 predictions of high-latitude TEC are much improved when using the AMPERE-derived potential as compared to that of the Weimer (2005) model. The two potential models have substantial differences, with Weimer producing an average 77 kV cross-cap potential versus 60 kV for the AMPERE-derived potential. The results indicate that the 66-satellite Iridium constellation provides sufficient resolution of field-aligned currents to estimate large-scale ionospheric convection as it impacts TEC.

Studies over the last two decades have shown that submicron particulates (SMPs) can be transferred from the seawater into sea spray aerosol (SSA), potentially impacting SSA cloud seeding ability. This work reports the first concurrent bulk and sea surface microlayer (SSML) SMP (0.4‑1.0 µm) measurements, made during two mesocosm phytoplankton blooms in a region devoid of active wave breaking and bubble formation, providing insight into how biological and physicochemical processes influence seawater SMP distributions. Modal analyses of the SMP size distributions revealed contributions from multiple, biologically‑related particulate populations that were controlled by the microbial loop. With negligible bubble scavenging occurring, SSML enrichment of SMPs remained low throughout both experiments, suggesting this process is vital for SMP enrichment in the SSML. Because many biological SMPs can induce ice formation in SSA, our findings are discussed in the context of SMP transfer into SSA and its potential importance for SSA ice nucleation.

Downslope windstorms are responsible for wildfires, wind gusts, and turbulence in the lee side of the Taebaek Mountains, called Yeongdong region (YD) in Korea. We classified the synoptic conditions of the windstorms in the YD using a Self-Organizing Map (SOM). For the windstorm events from 1979 to 2019, sea level pressure anomalies were used to train the SOM. It was found that the synoptic patterns could be classified into three representative types: 1) the south high and north low pattern in spring, 2) the west high and east low pattern in winter, and 3) the strong low-pressure system passing the northern part of Korea. At the 850 hPa level, prevailing southwesterly (nortwesterly) flow with warm (cold) advection was dominant in Type 1 (2), and Type 3 presented a well-developed baroclinic system of cyclone. Adiabatic warming by downslope windstorm is the strongest in Type 1, which is likely to have a huge impact on the spread of wildfires. Three mesoscale generation mechanisms were examined under different synoptic patterns. Hydraulic jump theory was dominant for the windstorms in Type 2 due to upstream flows with moderate Froude numbers and inversion layers. The partial reflection of mountain waves was found in all types but more frequent in Type 1 than others. Downslope windstorms with wave breaking at critical levels mostly occurred in Type 1. This objective classification of weather patterns responsible for downslope windstorm in the YD is useful for better prediction and future projection of this event with climate change.

Heatwaves cause excess mortality and physiological impacts on humans throughout the world, and climate change will intensify and increase the frequency of heat events. Many adaptation and mitigation studies use spatial distribution of highly vulnerable local populations to inform heat reduction and response plans. However, most available heat vulnerability studies focus on urban areas with high heat intensification by Urban Heat Islands (UHIs). Rural areas encompass different environmental and socioeconomic issues that require alternative analyses of vulnerability. We categorized Nebraska census tracts into four urbanization levels, then conducted factor analyses on each group and captured different patterns of socioeconomic vulnerabilities among resulted Heat Vulnerability Indices (HVIs). While disability is the major component of HVI in two urbanized classes, lower education and races other than white have higher contributions in HVI for the two rural classes. To account for environmental vulnerability of HVI, we considered different land type combinations for each urban class based on their percentage areas and their differences in heat intensifications. Our results demonstrate different combination of initial variables in heat vulnerability among urban classes of Nebraska and clustering of high and low heat vulnerable areas within the highest urbanized section. Less urbanized areas show no spatial clustering of HVI. More studies with separation on urbanization level of residence can give insights into different socioeconomic vulnerability patterns in rural and urban areas, while also identifying changes in environmental variables that better capture heat intensification in rural settings.

Palaeoclimate variability must be constrained to predict the nature and impacts of future climate change in the Eastern Mediterranean. Here, we present a high-resolution multiproxy dataset from Kocain Cave, the first of its kind from SW Turkey. Regional fluctuations in effective-moisture are recorded by variations in magnesium, strontium, phosphorous and carbon isotopes, with oxygen isotopes reacting to changes in precipitation amount and temperature. Important are: a double-peak of arid conditions at 1150 and 800 BCE, a wet period 330-460 CE followed by a rapid shift to dry conditions 460-830 CE, and a distinct dry/wet Medieval Climate Anomaly/Little Ice Age pattern. There are large discrepancies between Turkish records and the Kocain record, which shares more similarities with other Eastern Mediterranean coastal records. Heterogeneity of regional climate and palaeoclimate proxy records are emphasised.

In this paper, the climatological state and the seasonal variability of the Arabian Heat Low (AHL) and the Intertropical Discontinuity (ITD) are investigated over the Arabian Peninsula using the 1979-2019 ERA-5 reanalysis data. The AHL is a summertime feature, mostly at 15º-35ºN and 40º-60ºE, exhibiting a clear strengthening over the last four decades in line with the observed increase in surface temperature. However, no clear shift in its position is detected. The AHL, driven by both thermodynamic and dynamic forcing, is broader and stronger during daytime, and exhibits considerable variability on day-to-day time-scales, likely due to the convection associated with the Asian summer monsoon. The ITD is the boundary between the hot and dry desert air and the cooler and more moist air from the Arabian Sea. It lies along the Arabian Peninsula’s southern coastline in the cold season but reaches up to 28º N between 50º - 60º E in the summer months. While in the former it has a rather small diurnal variability, in the latter it shows daily fluctuations of up to 10º in latitude. The presence of the Sarawat Mountains over southwestern Saudi Arabia preclude a northward migration of the ITD in this area. The ITD exhibited a weak northward migration in the 41-year period, likely due to the increased sea surface temperatures in the Arabian Sea. On inter-annual timescales, the El Niño-Southern Oscillation, the Indian Ocean Dipole, and solar-geomagnetic effects play an important role in the AHL’s and ITD’s variability.

The Gulf of Mexico circulation is modulated by a mesoscale current, the Loop Current (LC), and large anticyclonic eddies that detach from it. The LC dynamics are recurrent, and its evolution is in and from a few preferential states. This observation points to the existence of a low-dimensional dynamical attractor. Building upon advancements in dynamical system theory, this work characterizes the average and instantaneous dimensions of such an attractor. The instantaneous dimension and its evolution in time are compared among an altimeter-based dataset, an ocean reanalysis and an operational hindcast. The LC complexity, measured by its dimension, differs among them, especially when the dimension is high. During shedding events, on the other hand, differences between datasets emerge in the second principal component. The information provided by this analysis is relevant to operational ocean forecasts and points to where improvement should occur.

A centroid location catalog of P-wave microseisms is crucial for understanding the origins of microseisms. Although a back-projection method is feasible for locating the centroids, the computational cost is still expensive for making a global catalog over ten years. Contrary, although the computational cost of beamforming is low, it cannot distinguish P from PP waves. To combine the advantages of both methods, we develop the auto-focusing method as a natural extension of beamforming. In the first step of this method, we estimated the slowness vector based on conventional beamforming and the epicentral distance inferred from the wavefront curvature by maximizing the beam power. In the second step, we iteratively update the values based on the perturbation theory. In the third step, based on the classified phase according to the estimated epicentral distance and the slowness, we infer the source location from the slowness vector with corrections for a global 3-D P-wave velocity structure. We also infer the centroid-single-force (CSF) from the beam power. We applied this method to the vertical components of seismic records at approximately 780 Hi-net stations in Japan from 2004 to 2020. We also compare the CSF catalog with a synthetic CSF catalog based on a numerical ocean wave model: WAVEWATCH III. Both catalogs generally show similar temporal-spatial patterns of centroids. The amplitudes of CSF are consistent with each other, although the seismic signal-to-noise ratio limits the detected events. Exceptionally, significant activities in the Gulf of Carpentaria cannot be explained by the ocean wave model.

Since 2007, the global mole fraction of atmospheric methane (CH4) has steadily increased meanwhile the 13C/12C isotopic ratio of CH4 (expressed as δ13C-CH4) has shifted to more negative values. This suggests that CH4 emissions are primarily driven by biogenic sources. However, more in situ isotopic measurements of CH4 are needed at the local scales to identify which biogenic sources dominate CH4 emissions regionally. In California, dairies contribute a substantial amount of CH4 emissions from enteric fermentation and manure management. In this study, we present seasonal atmospheric measurements of δ13C-CH4 from dairy farms in the San Joaquin Valley of California. We used δ13C-CH4 to characterize emissions from enteric fermentation by measuring downwind of cattle housing (e.g., freestall barns, corrals) and from manure management areas (e.g., anaerobic manure lagoons) with a mobile platform equipped with cavity ring-down spectrometers. Across seasons, the δ13C-CH4 from enteric fermentation source areas ranged from -69.7 ± 0.6 per mil (‰) to -51.6 ± 0.1‰ while the δ13C-CH4 from manure lagoons ranged from -49.5 ± 0.1‰ to -40.5 ± 0.2‰. Measurements of δ13C-CH4 of enteric CH4 suggest a greater than 10‰ difference between cattle production groups in accordance with diet. Isotopic signatures of CH4 were used to characterize enteric and manure CH4 from downwind plume sampling of dairies. Our findings show that δ13C-CH4 measurements could improve the attribution of CH4 emissions from dairy sources at scales ranging from individual facilities to regions and help constrain the relative contributions from these different sources of emissions to the CH4 budget.

Accurate modeling of Jupiter’s magnetospheric magnetic field is important for not only scientific research but also mission planning. We develop a new empirical global model of Jupiter’s magnetic field in the Juno era, including components from the planetary dynamo, Chapman-Ferraro currents, and cross-tail current sheet. The internal field is based on the JRM09 model. The shielding field is obtained by minimizing the component normal to Jupiter’s magnetopause and varies due to the change of solar wind dynamic pressure. Combined with the curved magnetodisk, the high-resolution TS07 method is used for modeling Jupiter’s magnetodisc and tail currents. The best-fitting results show an asymmetric magnetodisc current system in azimuthal direction with a tilted angle to Jupiter’s magnetic equator. The sweep-back effects of Jupiter’s magnetic fields are also reproduced by the radial current system. This new model is validated by comparing with Juno’s magnetometer data in the range from 5 Rj to 60 Rj, where Rj=71492 km is the radius of Jupiter.

Geomagnetically induced currents (GIC) are a phenomenon well known for its negative effects on the operations of power systems. To efficiently mitigate them requires different types of the power system modelling, from GIC to alternating current (AC) harmonic generation, to 3-dimentional finite-element models of transformers. Initiated by variations of the geomagnetic field in the presence of the conductive earth, i.e. the geophysical variables characterized by continuous frequency spectra, GIC and harmonics also exhibit continuous spectra. In order to adequately estimate their variations for mitigation purposes, the analysis on the impact of spectral frequency content on GIC and harmonics characteristics is required. The presented study is based on the geomagnetic measurements and the power network data (i.e. GIC and harmonics) with high sampling rates recorded during two geomagnetic storms, 31 March 2001 and 26-27 July 2004. Availability of data covering both the source and the result of geomagnetic storm impacts on power grid allows to (1) analyse the influence of spectral content on sufficient representation of both geomagnetic and geoelectric variations during the intervals with significant raise in GIC and harmonics (such as rapid variations during Storm Sudden Commencement and storm itself), and to (2) justify the choice of the sufficient resolution of data by the network response presented as GIC and harmonics variations. In summary, the optimal resolution is suggested and the deficiencies associated with the under-representation of the geomagnetic spectrum are identified and discussed.

The ocean mixed layer plays an important role in subseasonal climate dynamics because it can exchange large amounts of heat with the atmosphere, and it evolves significantly on subseasonal timescales. Estimation of the subseasonal variability of the ocean mixed layer is therefore important for subseasonal to seasonal prediction and analysis. The increasing coverage of in-situ Argo ocean profile data allows for greater analysis of the aseasonal ocean mixed layer depth (MLD) variability on subseasonal and interannual timescales; however, current sampling rates are not yet sufficient to fully resolve subseasonal MLD variability. Other products, including gridded MLD estimates, require optimal interpolation, a process that often ignores information from other oceanic variables. We demonstrate how satellite observations of sea surface temperature, salinity, and height facilitate MLD estimation in a pilot study of two regions: the mid-latitude southern Indian and the eastern equatorial Pacific Oceans. We construct multiple machine learning architectures to produce weekly 1/2 degree gridded MLD anomaly fields (relative to a monthly climatology) with uncertainty estimates. We test multiple traditional and probabilistic machine learning techniques to compare both accuracy and probabilistic calibration. We find that incorporating sea surface data through a machine learning model improves the performance of MLD estimation over traditional optimal interpolation in terms of both mean prediction error and uncertainty calibration. These preliminary results provide a promising first step to greater understanding of aseasonal MLD phenomena and the relationship between the MLD and sea surface variables. Extensions to this work include global and temporal analyses of MLD.

The 2019/2020 Australian wildfires emitted large quantities of atmospheric pollutant gases and aerosols. Using state-of-the-art near-real-time satellite measurements of tropospheric composition, we present an analysis of several emitted trace gases and their long-range transport, and compare to the previous (2018/2019) fire season. Observations of carbon monoxide (CO) show that fire emissions were so intense that the distinct Australian fire plume managed to circumnavigate the Southern Hemisphere (SH) within a few weeks, with eastward propagation over the South Pacific, South America, the South Atlantic, Africa and the Indian Ocean. Elevated atmospheric methane levels were also detected in January 2020 fire plumes over the Pacific, defined using CO as a plume tracer, even though sampling was restricted spatially by aerosols and clouds. Observations also show significant enhancements of methanol from the fires, where CH3OH:CO enhancement ratios increased within the aged plume downwind over the South Pacific indicating secondary in-plume CH3OH formation.

Mixing state of black carbon (BC) with secondary species has been highlighted as a major uncertainty in assessing its radiative forcing. While recent laboratory simulation has demonstrated that BC could serve as a catalyst to enhance the formation of sulfate, its role in the formation and evolution of secondary aerosols in the real atmosphere remains poorly understood. In the present study, the mixing of BC with sulfate/nitrate in the atmosphere of Guangzhou (China) was directly investigated with a single particle aerosol mass spectrometer (SPAMS). The peak area ratios of sulfate to nitrate (SNRs) for the BC-containing particles are constantly higher than those of the BC-free particles (with negligible BC signals). Furthermore, the seasonal SNR peak is observed in summer and autumn, and the diurnal peak is found in the afternoon, consistent with the trends of radiation-related parameters (i.e., solar radiation and temperature), pointing to the BC-induced photochemical production of sulfate. Such hypothesis is further supported by the multilinear regression and random forest analysis, showing that the variation of SNRs associated with the BC-containing particles could be well explained (R2 = ~0.7-0.8) by the radiation-related parameters (> 30% of the variance) and the relative BC content (~20%) in individual particles, but with limited influence of precursors (SO2/NOx: < 5%). Differently, the radiation-related factors only explain < 10% of the SNR variation for the BC-free particles. These results provide ambient observational evidence pointing to a unique role of BC on the photochemical formation and evolution of sulfate, which merits further quantitative evaluations.

Interspecific interactions shape how and when species (and population) ranges change. Natural enemies (like parasites) can slow population spread, or, conversely, a population can ‘outrun’ its enemies and spread uninhibited. Yet, less is know about how mutualistic interactions shape population spread, and what role ‘outrunning’ mutualistic partners plays. Here, I examine host-symbiont interactions specifically (where a symbiont species lives in/on a host species); common across animals and plants, and spanning the spectrum from parasitism to mutualism. I develop a model to determine when a symbiont shapes its host’s population spread versus when the host outruns its symbiont. I find that symbiont transmission mode is key. For density-dependent transmission, symbionts cannot be sustained at the low-density population edge and the host outruns its symbiont, whereas frequency-dependent transmission leads to symbionts affecting host spread. However, this pattern breaks down in the presence of a host Allee effect; spread dynamics switch from ‘pulled’ to ‘pushed’, enabling a symbiont to influence population spread from behind the range edge. Overall, mutualistic symbionts speed up (and parasitic symbionts slow down) host population spread. These findings indicate that contact structures within a population (which shape symbiont transmission) are critical for determining whether host-symbiont interactions influence population spread.

Passive seismic methods are considered as cost-effective and environmental-friendly alternatives to active (reflection) seismic methods. We have acquired co-located active and passive seismic surveys over a metal-endowed Archean granite-greenstone terrane in the Larder Lake area to investigate the reliability of the estimated elastic properties using the passive seismic methods. The passive seismic data was processed using two different data processing approaches, the ambient noise surface wave tomography (ANSWT) and receiver function analysis methods to generate shear-wave velocity and P- to S-wave (P-S) convertibility profiles of the subsurface, respectively. The Cadillac-Larder Lake Fault (CLLF) was imaged as a south-dipping sub-vertical zone of weak reflectivity in the reflection seismic profile. To the north of the CLLF, a package of north-dipping reflections in the upper-crust (at depths of 5-10 km) resides on the boundary of high (on the top) and low (on the bottom) shear-wave velocity zones estimated using the ANSWT method. This package of reflections is most likely caused by overlaying mafic volcanic and underlying felsic intrusive rocks. The P-S convertibility profile imaged the Moho boundary at ~40 km depth as well as a south-dipping slab that penetrates into the mantel which was interpreted to be either caused by the delamination of the lower crust or a possible deeper extension of the Porcupine-Destor Fault. Overall, the reflectivity, shear-wave velocity, and P-S convertibility profiles exhibited a good correlation and provided a detailed image of the subsurface lithological structure to a depth of 10 km.