Author(s): Lukas Mayrhofer, Myfanwy E. Evans, and Gero Friesecke
We present fast simulation methods for the self-assembly of complex shapes in two dimensions. The shapes are modeled via a general boundary curve and interact via a standard volume term promoting overlap and an interpenetration penalty. To efficiently realize the Gibbs measure on the space of possib…
[Phys. Rev. E 110, 015309] Published Wed Jul 24, 2024
Abstract
As the Arctic rapidly warms, sea ice extent is decreasing and oil and gas extraction activities are expanding. Local combustion emissions affect the Arctic atmospheric aerosol chemical mixing state (the distribution of chemical species across the aerosol population), which impacts climate-relevant properties. Bulk and single-particle measurements of submicron aerosols were conducted at Oliktok Point, Alaska within the North Slope of Alaska oil fields. In this work, we quantify aerosol diversity using online single-particle mass spectrometry data (32,880 individual particles), offline single-particle microscopy data (20,912 individual particles), and online bulk aerosol mass spectrometry and aethalometer data. This method was used to derive individual particle mass fractions for both refractory and non-refractory material within distinct particle types. Single-particle, average single-particle, and bulk population diversities (D
i
, D
α
, D
γ
, respectively) and mixing state indices (χ) were calculated for the data set. Calculated D
i
values were generally low (2.2 ± 0.6), as individual particle masses were dominated by a few chemical species of interest. Aged aerosol particles (those internally mixed with nitrate and/or sulfate) exhibited higher D
i
values (>3) compared to recently emitted (fresh) aerosol particles. During oil field plume periods, D
α
values approached three due to the abundance of diesel combustion particles, which were rich in sulfate, black carbon, and organic aerosol. Overall, the submicron aerosol population within the Arctic oil fields was found to be relatively externally mixed (χ < 50%), due to the constant local emissions within oil fields combining with background aerosol and locally emitted sea spray aerosol at the coastal site.
Abstract
Atmospheric trace gas measurements can be used to independently assess national greenhouse gas inventories through inverse modeling. Atmospheric nitrous oxide (N2O) measurements made in the United Kingdom (UK) and Republic of Ireland are used to derive monthly N2O emissions for 2013–2022 using two different inverse methods. We find mean UK emissions of 90.5 ± 23.0 (1σ) and 111.7 ± 32.1 (1σ) Gg N2O yr−1 for 2013–2022, and corresponding trends of −0.68 ± 0.48 (1σ) Gg N2O yr−2 and −2.10 ± 0.72 (1σ) Gg N2O yr−2, respectively, for the two inverse methods. The UK National Atmospheric Emissions Inventory (NAEI) reported mean N2O emissions of 73.9 ± 1.7 (1σ) Gg N2O yr−1 across this period, which is 22%–51% smaller than the emissions derived from atmospheric data. We infer a pronounced seasonal cycle in N2O emissions, with a peak occurring in the spring and a second smaller peak in the late summer for certain years. The springtime peak has a long seasonal decline that contrasts with the sharp rise and fall of N2O emissions estimated from the bottom-up UK Emissions Model (UKEM). Bayesian inference is used to minimize the seasonal cycle mismatch between the average top-down (atmospheric data-based) and bottom-up (process model and inventory-based) seasonal emissions at a sub-sector level. Increasing agricultural manure management and decreasing synthetic fertilizer N2O emissions reduces some of the discrepancy between the average top-down and bottom-up seasonal cycles. Other possibilities could also explain these discrepancies, such as missing emissions from NH3 deposition, but these require further investigation.
SummaryMany intracontinental basins form as broad depressions through prolonged, slow subsidence of the continental lithosphere. Such long-lived basins can record lithospheric processes over hundreds of millions of years, serving as important archives of lithospheric evolution. Since continental amalgamation in the Mesoproterozoic, the lithosphere beneath the intracontinental Canning Basin has been subject to several tectonic events, with extensive crustal reworking evidenced through different upper crust datasets. However, knowledge of the structure of the subcontinental lithospheric mantle is lacking. As a consequence, understanding the coupled evolution between surface and deep lithospheric processes, crucial to resolving basin formation, development, and survival, remains problematic. Here, we combine geochemical, geophysical, and petrophysical data within a thermodynamic modelling framework to determine the thermochemical properties, rheology, density, and seismic structure of the lithospheric and sublithospheric mantle beneath the Canning Basin. The results indicate a thick, rigid lithosphere with a maximum thickness of 185 km and strength of ca. 1$\times$1013 Pa m, and an anomalously Fe-enriched subcontinental lithospheric mantle with a Mg# of 88.6. This mantle structure is not consistent with pre-collisional fragments or a Precambrian collisional setting and may reflect magmatic refertilisation during high-volume mafic magmatic events. Potential candidate events are the ∼1070 Ma Warakurna, ∼825 Ma Gairdner, and ∼510 Ma Kalkarindji Large Igneous Provinces. The youngest of these is temporally and spatially correlated with and therefore interpreted to have influenced the Canning Basin formation. We propose that refertilisation caused a negatively buoyant subcontinental lithospheric mantle and prolonged subsidence and preservation of the basin, while the strong lithosphere ensured lithospheric stability and longevity.
SummaryCross-correlations of seismic ambient noise are frequently used to image Earth structure. Usually, tomographic studies assume that noise sources are uniformly distributed and interpret noise correlations as empirical Green’s functions. However, previous research suggests that this assumption can introduce errors in the estimated models, especially when noise correlation waveforms are inverted. In this paper, we investigate changes in subsurface models inferred from noise correlation waveforms depending on whether the noise source distribution is considered to be uniform. To this end, we set up numerical experiments that mimic a tomographic study in Southern California exploiting ambient noise generated in the Pacific Ocean. Our results show that if the distribution of noise sources is deemed uniform instead of being numerically represented in the wave simulations, the misfit of the estimated models increases. In our experiments, the model misfit increase ranges between 5 % and 21 %, depending on the heterogeneity of the noise source distribution. This indicates that assuming uniform noise sources introduces source-dependent model errors. Since the location of noise sources may change over time, these errors are also time-dependent. In order to mitigate these errors, it is necessary to account for the noise source distribution. The spatial extent to which noise sources must be considered depends on the propagation distance of the ambient noise wavefield. If only sources near the study area are considered, model errors may arise.
SummaryMoment tensor (MT) inversion is a classical geophysical inverse problem that infers a force-equivalent model of a seismic source from seismological observations. Like other inverse problems, the accuracy of the inversion depends on the reliability of the forward problem simulating waveforms from the source location through an Earth structural model. Apart from errors in data, the error in forward waveform simulation, also known as theory error, is a significant source of error contributing to the misfit function between the predicted and observed waveforms. Here, we set up numerical experiments to comprehensively probe the sensitivity of the linearized MT inversion to 3D regional Earth model errors, a known predominant factor of the theory error. Using the Monte Carlo method, we estimate the empirical structural covariance matrices to characterize the waveform mismatch due to the imperfect knowledge of Earth's structure. Firstly, although the inversion accuracy deteriorates with increasing model errors, incorporating the structural covariance matrices into the misfit function improves the accuracy of inversion results for all theorized error distributions. Secondly, we propose a slightly modified form of the structural covariance matrix, which further enhances the inversion outcome. Lastly, as the true structural errors are likely spatially correlated, we highlight the importance of adequately treating the correlation into the MT inversion because of its significant impact on inversion. Overall, as a preliminary effort in quantifying 3D structural errors on MT inversion, this study proves the computational feasibility by means of numerical experiments and will hopefully provide a way forward for future work on this topic.
Abstract
Sea surface salinity (SSS) is crucial to the marine ecosystem. Soil Moisture and Ocean Salinity (SMOS) establishes a geophysical modeling function (GMF) between sea surface brightness temperature (BT) and SSS, which incorporates sea surface wind speed and significant wave height (SWH) to retrieve the SSS. However, the relationship between sea surface BT and SSS is complex and influenced by a variety of factors, making it challenging to accurately characterize this relationship using GMF. Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) observations directly respond to sea surface roughness and offer low cost and high spatiotemporal resolution advantages. Therefore, in this study, for the first time, spaceborne GNSS-R observations from the Cyclone GNSS (CYGNSS) have been incorporated into the SMOS SSS retrieval. Additionally, an empirical model between SMOS BT and Argo SSS was developed using an artificial neural network (ANN). Compared to the conventional SMOS SSS retrieval method, the proposed method in this study reduces the root mean square error (RMSE) of the retrieved SSS from 1.17 to 0.76 psu and increases the correlation coefficient (R) from 0.55 to 0.66. Furthermore, comparisons were made with ground truth measurements from the National Data Buoy Center (NDBC) buoys, which indicated that the proposed method decreases the RMSE of the retrieved SSS from 0.87 to 0.62 psu and reduces the absolute mean deviation from 0.66 to 0.48 psu. These provide references for the future application of spaceborne GNSS-R in SSS retrieval.
An international group of experts in mountain hydrology argue that the traditional understanding of the mountain water cycle has largely ignored the role that cryosphere-groundwater interactions play. This oversight could lead to incomplete or inaccurate predictions of water availability in mountain regions, especially in the context of climate change, suggest the authors in a Perspective Paper appearing in Nature Water.
Abstract
Narrow bipolar events (NBEs) are impulsive and powerful intracloud discharges. Recent observations indicate that some NBEs exhibit a slanted orientation rather than strictly vertical. This paper investigates the effect of the slanted NBEs using a newly developed rebounding-wave model. The modeling results are validated against the full-wave Finite- Difference Time-Domain method and compared with measurements for both vertical and slanted NBE cases. It is found that the inclination of the NBEs affects both the waveforms and amplitudes of the electrostatic, induction and radiation components of the electric fields at close distances (≤10 km). However, it primarily influences the amplitudes of the fields for distances beyond 50 km, where the radiation component dominates, resulting in changes of ≥30% when the slant angle exceeds 30°. The slanted rebounding-wave model improves the agreement with respect to a purely vertical channel and can be extended to any discharge geometry at arbitrary observation distances.
Abstract
In the context of China's “dual carbon” goal, emissions of air pollutants are expected to significantly decrease in the future. Thus, the direct climate effects of black carbon (BC) aerosols in East Asia are investigated under this goal using an updated regional climate and chemistry model. The simulated annual average BC concentration over East Asia is approximately 1.29 μg/m3 in the last decade. Compared to those in 2010–2020, both the BC column burden and instantaneous direct radiative forcing in East Asia decrease by more than 55% and 80%, respectively, in the carbon peak year (2030s) and the carbon neutrality year (2060s). Conversely, the BC effective radiative forcing (ERF) and regional climate responses to BC exhibit substantial nonlinearity to emission reduction, possibly resulting from different adjustments of thermal-dynamic fields and clouds from BC-radiation interactions. The regional mean BC ERF at the tropopause over East Asia is approximately +1.11 W/m2 in 2010–2020 while negative in the 2060s. BC-radiation interactions in the present-day impose a significant annual mean cooling of −0.2 to −0.5 K in central China but warming +0.3 K in the Tibetan Plateau. As China's BC emissions decline, surface temperature responses show a mixed picture compared to 2010–2020, with more cooling in eastern China and Tibet of −0.2 to −0.3 K in the 2030s, but more warming in central China of approximately +0.3 K by the 2060s. The Indian BC might play a more important role in East Asian climate with reduction of BC emissions in China.
Abstract
Stratiform and convective precipitation are known to be associated with distinct isotopic fingerprints in the tropics. Such rain type specific isotope signals are of key importance for climate reconstructions derived from climate proxies (e.g., stable isotopes in tree rings). Recently, the relation between rain type and isotope signal in present-day climate has been intensively discussed. While some studies point out the importance of deep convection, other studies emphasize the role of stratiform precipitation for strongly depleted isotope signals in precipitation. Uncertainties arise from observational studies due to data scarcity while modeling approaches with global climate models cannot explicitly resolve convective processes and rely on parameterizations. High-resolution climate models are particularly important for studies over complex topography and for the simulation of convective cloud formation and organization. Therefore, we applied the isotope-enabled version of the high-resolution climate model from the Consortium for Small-Scale Modeling (COSMOiso) over the Andes of tropical south Ecuador, South America, to investigate the influence of stratiform and convective rain on the stable oxygen isotope signal of precipitation (δ18OP). Our results highlight the importance of deep convection for depleting the isotopic signal of precipitation and increasing its deuterium excess. Due to the opposing effect of shallow and deep convection on the δ18OP signal, the use of a stratiform fraction might be misleading. We therefore propose to use a shallow and deep convective fraction to analyze the effect of rain types on δ18OP.
Abstract
The eddy-covariance (EC) method assumes a homogeneous underlying surface. However, recent studies increasingly examining on EC measurements across diverse surfaces, raising concerns about measurement precision and accuracy. This study evaluates the impacts of altering the emission height and rate on the EC measurements through utilizing an artificial source emission system. The results demonstrated a significant impact of changes in the emission height and rate on the EC measurements. Higher emission height may lead to the underestimation of the measured EC fluxes, attributed to the variations in the footprint area and turbulent transport. Traditional data quality control methods may discard effective EC data during sudden changes in the emission rate. Therefore, to secure effective data and accurately observe emissions, it was practical to analyze the auxiliary factors, such as environmental factors, such as vapor pressure deficit (VPD). An unresolved issue would persist with the correction of the EC method for accurately capturing the actual emission signals when there was a sudden increase in the data range or deviation. Furthermore, comparing the footprint model estimations with the actual emissions demonstrated the necessity of footprint analyses, offering a valuable reference for the data calibration when the uncertainties arose owing to inhomogeneous underlying surfaces. Although EC fluxes across the three averaging periods indicated no significant differences, the footprint model suggested that 15-min interval was the optimal. Further validation experiments are required for the EC measurements in locations with complex source conditions to enhance our understanding of land-atmosphere flux exchange.
Abstract
The Chinese Loess Plateau (CLP) in northern China is home to one of the most prominent loess records in the world, reflecting past eolian dust activity in East Asia. However, their interpretation is hampered by ambiguity in the origin of loess-forming dust and an incomplete understanding of the circulation forcing dust accumulation. In this study, we used a novel modeling approach combining a dust emission model FLEXDUST with simulated back trajectories from FLEXPART to trace the dust back to where it was emitted. Over 21 years (1999–2019), we modeled back trajectories for fine (∼2 μm) and super-coarse (∼20 μm) dust particles at six CLP sites during the peak dust storm season from March to May. FLEXPART source-receptor relationships are combined with the dust emission inventory from FLEXDUST to create site-dependent high-resolution maps of the source contribution of deposited dust. The nearby dust emission areas were found to be the main source of dust to the CLP. Dust deposition across the CLP was found to predominantly occur via wet removal, with also some super-coarse dust from distant emission regions being wet deposited following high-level tropospheric transport. The high topography located on the downwind side of the emission area plays an essential role in forcing the emitted super-coarse dust upward. On an interannual scale, the phase of the Arctic Oscillation in the preceding winter was found to have a strong association with the spring deposition rate on the CLP, while the strength of the East Asian Winter Monsoon was less influential.
A new automated system of monitoring and classifying persistent vibrations at active volcanoes can eliminate the hours of manual effort needed to document them.
Abstract
The structure of fault zones and the ruptures they host are inextricably linked. Fault zones are narrow, which has made imaging their structure at seismogenic depths a persistent problem. Fiber-optic seismology allows for low-maintenance, long-term deployments of dense seismic arrays, which present new opportunities to address this problem. We use a fiber array that crosses the Garlock Fault to explore its structure. With a multifaceted imaging approach, we peel back the shallow structure around the fault to see how the fault changes with depth in the crust. We first generate a shallow velocity model across the fault with a joint inversion of active source and ambient noise data. Subsequently, we investigate the fault at deeper depths using travel-time observations from local earthquakes. By comparing the shallow velocity model and the earthquake travel-time observations, we find that the fault's low-velocity zone below the top few hundred meters is at most unexpectedly narrow, potentially indicating fault zone healing. Using differential travel-time measurements from earthquake pairs, we resolve a sharp bimaterial contrast at depth that suggests preferred westward rupture directivity.
Global variable-resolution simulations of extreme precipitation over Henan, China, in 2021 with MPAS-Atmosphere v7.3
Zijun Liu, Li Dong, Zongxu Qiu, Xingrong Li, Huiling Yuan, Dongmei Meng, Xiaobin Qiu, Dingyuan Liang, and Yafei Wang
Geosci. Model Dev., 17, 5477–5496, https://doi.org/10.5194/gmd-17-5477-2024, 2024
In this study, we completed a series of simulations with MPAS-Atmosphere (version 7.3) to study the extreme precipitation event of Henan, China, during 20–22 July 2021. We found the different performance of two built-in parameterization scheme suites (mesoscale and convection-permitting suites) with global quasi-uniform and variable-resolution meshes. This study holds significant implications for advancing the understanding of the scale-aware capability of MPAS-Atmosphere.
Development of a novel storm surge inundation model framework for efficient prediction
Xuanxuan Gao, Shuiqing Li, Dongxue Mo, Yahao Liu, and Po Hu
Geosci. Model Dev., 17, 5497–5509, https://doi.org/10.5194/gmd-17-5497-2024, 2024
Storm surges generate coastal inundation and expose populations and properties to danger. We developed a novel storm surge inundation model for efficient prediction. Estimates compare well with in situ measurements and results from a numerical model. The new model is a significant improvement on existing numerical models, with much higher computational efficiency and stability, which allows timely disaster prevention and mitigation.
Evaluation of Dust Emission and Land Surface Schemes in Predicting a Mega Asian Dust Storm over South Korea Using WRF-Chem (v4.3.3)
Ji Won Yoon, Seungyeon Lee, Ebony Lee, and Seon Ki Park
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-114,2024
Preprint under review for GMD (discussion: open, 0 comments)
This study evaluates the WRF-Chem model's prediction of a mega Asian Dust Storms (ADSs) over South Korea on March 28–29, 2021. We assessed five dust emission and four land surface schemes for predicting ADSs. Using surface observations and remote sensing data, we examined variables, such as temperature, humidity, wind speed, PM10, and aerosol optical depth. The UoC04 dust emission and CLM4 land surface scheme combination reduced RMSE for PM10 by up to 29.6 %, showing the best performance.
Insights into ground strike point properties in Europe through the EUCLID lightning location system
Dieter Roel Poelman, Hannes Kohlmann, and Wolfgang Schulz
Nat. Hazards Earth Syst. Sci., 24, 2511–2522, https://doi.org/10.5194/nhess-24-2511-2024, 2024
EUCLID's lightning data unveil distinctive ground strike point (GSP) patterns in Europe. Over seas, GSPs per flash surpass inland, reaching a minimum in the Alps. Mountainous areas like the Alps and Pyrenees have the closest GSP separation, highlighting terrain elevation's impact. The daily peak current correlates with average GSPs per flash. These findings could significantly influence lightning protection measures, urging a focus on GSP density rather than flash density for risk assessment.
The wildfire season of 2023 was the most destructive ever recorded in Canada and a new study suggests the impact was unprecedented. It found that four of the year's wildfires in mine-impacted areas around Yellowknife, Northwest Territories potentially contributed up to half of the arsenic that wildfires emit globally each year.
