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Natural gas is considered a bridging technology in the energy transition because it produces fewer carbon emissions than coal, for example. However, when leaks exist, methane is released into the atmosphere, leading to a dramatic increase in the carbon footprint of natural gas, as methane is a much stronger greenhouse gas than carbon dioxide. Therefore, we conducted a detailed study of methane emissions from gas-powered end-use appliances and then compared their climate impacts with those of electricity-powered appliances. We used the Munich Oktoberfest as a case study and then extended the study to 25 major natural gas consuming countries. This showed that electricity has been the more climate-friendly energy source at Oktoberfest since 2005, due to the extensive use of renewable electricity at the festival and the presence of methane emissions, particularly caused by the incomplete combustion of natural gas in cooking and heating appliances. By contrast, at the global level, our study shows that natural gas still produces lower end-use carbon emissions than electricity in 18 of the 25 countries studied. However, as the share of renewable energy in the electricity mix steadily increases in most countries, the carbon footprint of electricity will be lower than that of natural gas in these countries in the near future. These findings from our comparison of the total carbon emissions of electric and gas-powered end-use appliances can help inform the debate on how to effectively address climate change., Earth's Future, 11 (2), ISSN:2328-4277

Communities in the Pamir Mountains of Central Asia are among the most vulnerable to climate change due to their geographic location and subsistence-based livelihoods. Historically, ecological calendars supported their agropastoral lifestyles which provided anticipatory capacity to seasonal changes. Due to decades of Soviet colonization and socioecological transformations, knowledge of these ecological calendars fell into disuse. In 2016, Savnob and Roshorv, two villages in the Bartang Valley of Tajikistan, began the revitalization of these calendars using a participatory action research process through knowledge co-generation. We undertook a comparative analysis to investigate the importance of context-specificity to ensure food security and reduce their vulnerability to climate change. A preliminary analysis of the temperature regime and local language terms, relating to the positioning and quality of land, framed our methods-of-analysis. We compared the villagers' ecological calendars by focusing on indicator species, potentially threatening weather events, land-use, livelihood activities, and the role of the vernal equinox. Despite their close geographic proximity, context-specificity determined by distinct microecologies influences the timing and practice of these communities' livelihood activities. These villages have different dependencies on biotic and abiotic events, crops, and land-use; all of which affect food security and survival. These differences contributed to mutual support between the two villages, increased the availability of food, and thereby, lowered their vulnerability to climate change. As Savnob's and Roshorv's ecological calendars are updated with changing climate, they can once again enhance their anticipatory capacity while reducing their vulnerability.

Detailed imaging of accretionary wedges reveals splay fault networks that could pose a significant tsunami hazard. However, the dynamics of multiple splay fault activation during megathrust earthquakes and the consequent effects on tsunami generation are not well understood. We use a 2‐D dynamic rupture model with complex topo‐bathymetry and six curved splay fault geometries constrained from realistic tectonic loading modeled by a geodynamic seismic cycle model with consistent initial stress and strength conditions. We find that all splay faults rupture coseismically. While the largest splay fault slips due to a complex rupture branching process from the megathrust, all other splay faults are activated either top down or bottom up by dynamic stress transfer induced by trapped seismic waves. We ascribe these differences to local non‐optimal fault orientations and variable along‐dip strength excess. Generally, rupture on splay faults is facilitated by their favorable stress orientations and low strength excess as a result of high pore‐fluid pressures. The ensuing tsunami modeled with non‐linear 1‐D shallow water equations consists of one high‐amplitude crest related to rupture on the longest splay fault and a second broader wave packet resulting from slip on the other faults. This results in two episodes of flooding and a larger run‐up distance than the single long‐wavelength (300 km) tsunami sourced by the megathrust‐only rupture. Since splay fault activation is determined by both variable stress and strength conditions and dynamic activation, considering both tectonic and earthquake processes is relevant for understanding tsunamigenesis., Plain Language Summary: In subduction zones, where one tectonic plate moves beneath another, earthquakes can occur on many different faults. Splay faults are relatively steep faults that branch off the largest fault (the megathrust) in a subduction zone. As they are steeper than the megathrust, the same amount of movement on them could result in more vertical displacement of the seafloor. Therefore, splay faults are thought to play an important role in the generation of tsunamis. Here, we use computer simulations to study if an earthquake can break multiple splay faults at once and how this affects the resulting tsunami. We find that multiple splay faults can indeed fail during a single earthquake due to the stress changes from trapped seismic waves, which promote rupture on splay faults. Rupture on splay faults results in larger seafloor displacements with smaller wavelengths, so the ensuing tsunami is bigger and results in two main flooding episodes at the coast. Our results show that it is important to consider rupture on splay faults when assessing tsunami hazard., Key Points: Multiple splay faults can be activated during a single earthquake by megathrust slip and dynamic stress transfer due to trapped waves. Splay fault activation is facilitated by their favorable orientation with respect to the local stress field and their closeness to failure. Long‐term geodynamic stresses and fault geometries affect dynamic splay fault rupture and the subsequent tsunami., Volkswagen Foundation (VolkswagenStiftung) http://dx.doi.org/10.13039/501100001663, Royal Society (The Royal Society) http://dx.doi.org/10.13039/501100000288, EC | H2020 | H2020 Priority Excellent Science | H2020 European Research Council (ERC) http://dx.doi.org/10.13039/100010663, Deutsche Forschungsgemeinschaft (DFG) http://dx.doi.org/10.13039/501100001659, National Science Foundation (NSF) http://dx.doi.org/10.13039/100000001, https://github.com/TUM-I5/SWE, https://doi.org/10.5281/zenodo.6969455

Collapse of permafrost coasts delivers large quantities of particulate organic carbon (POC) to Arctic coastal areas. With rapidly changing environmental conditions, sediment and organic carbon (OC) mobilization and transport pathways are also changing. Here, we assess the sources and sinks of POC in the highly dynamic nearshore zone of Herschel Island‐Qikiqtaruk (Yukon, Canada). Our results show that POC concentrations sharply decrease, from 15.9 to 0.3 mg L−1, within the first 100–300 m offshore. Simultaneously, radiocarbon ages of POC drop from 16,400 to 3,600 14C years, indicating rapid settling of old permafrost POC to underlying sediments. This suggests that permafrost OC is, apart from a very narrow resuspension zone (, Key Points Suspended particulate organic carbon (POC) sharply decreases in age and concentration within the nearshore zoneBeyond a narrow resuspension zone, permafrost‐derived OC dominates surface sediments, whereas much younger OC is found in the water columnPermafrost‐derived POC is rapidly removed from the water column, showing the critical role of nearshore dynamics for OC transport

Acute exacerbation of chronic obstruction pulmonary disease (AECOPD) as a respiratory disease, is considered to be related to air pollution by more and more studies. However, the evidence on how air pollution affect the incidence of AECOPD and whether there are population differences is still insufficient. Therefore, we select PM10, PM2.5, SO2, NO2, CO, and O3 as representatives combined with daily AECOPD admission data from 1 January 2015 to 26 June 2016 in the rural areas of Qingyang, northwestern China to explore the associations of air pollution with AECOPD. Based on a time‐stratified case‐crossover design, we constructed a distributed lag nonlinear model to qualify the single and cumulative lagged effects of air pollution on AECOPD. Stratified related risks by sex and age were also reported. The cumulative exposure‐response curves were approximately linear for PM2.5, “V”‐shaped for PM10, “U”‐shaped for NO2 and inverted‐“V” for SO2, CO and O3. Exposure to high‐PM2.5 (42 μg/m3), high‐PM10 (91 μg/m3), high‐SO2 (58 μg/m3), low‐NO2 (12 μg/m3), and high‐CO (1.55 mg/m3) increased the risk of AECOPD. Females aged 15–64 were more susceptible under extreme concentrations of PM2.5, SO2, CO, and low‐PM10 than other subgroups. In addition, adults aged 15–64 were more sensitive to extreme concentrations of NO2 compared with the elderly ≥65 years old, while the latter were more sensitive to high‐PM10. High‐SO2, high‐NO2, and extreme concentrations of PM2.5 had the greatest effects on the day of exposure, while low‐SO2 and low‐CO had lagged effects on AECOPD. Precautionary measures should be taken with a focus on vulnerable subgroups, to control hospitalization for AECOPD associated with air pollutants., Key Points Exposure to high‐PM2.5, high‐PM10, high‐SO2, low‐NO2, and high‐CO increased the risk of acute exacerbation of chronic obstruction pulmonary disease (COPD)The cumulative curves were approximately linear for PM2.5, “V”‐shaped for PM10, “U”‐shaped for NO2 and inverted‐”V” for SO2, CO and O3 The nonlinear effects on acute exacerbation of COPD at different lags varied based on the air pollutants, involved gender and age

Since the 1980–1990s, international research efforts have augmented our knowledge of the physical and chemical properties of the Arctic Ocean water masses, and recent studies have documented changes. Understanding the processes responsible for these changes is necessary to be able to forecast the local and global consequences of these property evolutions on climate. The present work investigates the distributions of geochemical tracers of particle fluxes and circulation in the Amerasian Basin and their temporal evolution over the last three decades (from stations visited between 1983 and 2015). Profiles of 230‐thorium (230Th) and 231‐protactinium (231Pa) concentrations and neodymium isotopes (expressed as εNd) measured in the Amerasian Basin prior to 2000 are compared to a new, post‐2000s data set. The comparison shows a large scale decrease in dissolved 230Th and 231Pa concentrations, suggesting intensification of scavenging by particle flux, especially in coastal areas. Higher productivity and sediment resuspension from the shelves appear responsible for the concentration decrease along the margins. In the basin interior, increased lateral exchanges with the boundary circulation also contribute to the decrease in concentration. This study illustrates how dissolved 230Th and 231Pa, with εNd support, can provide unique insights not only into changes in particle flux but also into the evolution of ocean circulation and mixing., Key Points Concentration decreases of 230Thd and 231Pad suggest an enhancement of particulate scavenging since the 2000s in the Amerasian BasinParticulate scavenging results from higher productivity and sediment resuspension from the shelvesPost‐2000s changes in intermediate and deep layers suggest enhanced lateral mixing between the margins and central areas of the basins

Accelerated urbanization increases both the frequency and intensity of heatwaves (HW) and urban heat islands (UHIs). An extreme HW event occurred in 2012 summer that caused temperatures of more than 40°C in Chicago, Illinois, USA, which is a highly urbanized city impacted by UHIs. In this study, multiple numerical models, including the High Resolution Land Data Assimilation System (HRLDAS) and Weather Research and Forecasting (WRF) model, were used to simulate the HW and UHI, and their performance was evaluated. In addition, sensitivity testing of three different WRF configurations was done to determine the impact of increasing model complexity in simulating urban meteorology. Model performances were evaluated based on the statistical performance metrics, the application of a multi‐layer urban canopy model (MLUCM) helps WRF to provide the best performance in this study. HW caused rural temperatures to increase by ∼4°C, whereas urban Chicago had lower magnitude increases from the HW (∼2–3°C increases). Nighttime UHI intensity (UHII) ranged from 1.44 to 2.83°C during the study period. Spatiotemporal temperature fields were used to estimate the potential heat‐related exposure and to quantify the Excessive Heat Factor (EHF). The EHF during the HW episode provides a risk map indicating that while urban Chicago had higher heat‐related stress during this event, the rural area also had high risk, especially during nighttime in central Illinois. This study provides a reliable method to estimate spatiotemporal exposures for future studies of heat‐related health impacts., Key Points Extreme heatwave induced ∼4°C increase in temperature in rural Chicago while increased urban temperature by 2–3°CUrban heat island intensity is estimated to be around 1.44–2.38°CExcessive Heat Factor is higher than 50°C2 in urban Chicago due to heatwave

The impact of the stratospheric quasi‐biennial oscillation (QBO) on the prediction of the tropospheric Madden‐Julian oscillation (MJO) is evaluated in reforecasts from nine models participating in subseasonal prediction projects, including the Subseasonal Experiment (SubX) and Subseasonal to Seasonal (S2S) projects. When MJO prediction skill is analyzed for December to February, MJO prediction skill is higher in the easterly phase of the QBO than the westerly phase, consistent with previous studies. However, the relationship between QBO phase and MJO prediction skill is not statistically significant for most models. This insignificant QBO‐MJO skill relationship is further confirmed by comparing two subseasonal reforecast experiments with the Community Earth System Model v1 using both a high‐top (46‐level) and low‐top (30‐level) version of the Community Atmosphere Model v5. While there are clear differences in the forecasted QBO between the two model top configurations, a negligible change is shown in the MJO prediction, indicating that the QBO in this model may not directly control the MJO prediction and supporting the insignificant QBO‐MJO skill relationship found in SubX and S2S models., Key Points The QBO‐MJO prediction skill relationship is mostly insignificant in nine subseasonal reforecastsThe QBO‐MJO prediction skill relationship is sensitive to the choice of QBO eventsThe insignificant QBO‐MJO skill relationship is supported by comparing reforecasts by a high‐top and low‐top version of CESM1

This study investigates the atmospheric drivers of severe precipitation deficits in the Greater Alpine Region during the last 210 years utilizing a daily atmospheric circulation type reconstruction. Precipitation deficit tends to be higher during periods with more frequent anticyclonic (dry) and less frequent cyclonic (wet) circulation types, as would be expected. However, circulation characteristics are not the main drivers of summer precipitation deficit. Dry soils in the warm season tend to limit precipitation, which is particularly the case for circulation types that are sensitive to a soil moisture‐precipitation feedback. This mechanism is of specific relevance in explaining the major drought decades of the 1860s and 1940s. Both episodes show large negative precipitation anomalies in spring followed by increasing frequencies of circulation types sensitive to soil moisture precipitation feedbacks. The dry springs of the 1860s were likely caused by circulation characteristics that were quite different from those of recent decades as a consequence of the large spatial extent of Arctic sea ice at the end of the Little Ice Age. On the other hand, the dry springs of the 1940s developed under a persistent positive pressure anomaly across Western and Central Europe, triggered by positive sea surface temperatures in the western subtropical Atlantic., Key Points Winter and spring droughts are strongly related to the Eastern Atlantic‐Western Russia atmospheric variability patternSummer droughts are enhanced by prevailing dry spring conditions through a positive soil moisture‐precipitation feedbackThe major dry episodes in the 1860s and 1940s are particularly driven by this feedback mechanismThe dry springs of the 1860s were likely caused by unusual circulation regimes, synchronized with large spatial extents of Arctic sea ice at the end of the Little Ice AgeThe dry springs of the 1940s are due to positive sea surface temperature anomalies in the western subtropical Atlantic, which are related to a positive Eastern Atlantic‐Western Russia pattern

We analyzed the interaction between the North American monsoon anticyclone (NAMA) and summertime cross‐tropopause convective outflow by applying a trajectory analysis to a climatology of convective overshooting tops (OTs) identified in GOES satellite images, which covers the domain from 29°S to 68°N and from 205 to 1.25°W for the time period of May through September 2013. With this analysis we identified seasonally, geographically, and altitude‐dependent variability in NAMA strength and in cross‐tropopause convection that control their interaction. We find that the NAMA has the strongest impact on the circulation of convectively influenced air masses in August. Over the entire time period examined the intertropical convergence zone contributes the majority of OTs with a larger fraction of total OTs at 370 K (on average 70%) than at 400 K (on average 52%). During August at 370 K, the convectively influenced air masses within the NAMA circulation, as determined by the trajectory analysis, are primarily sourced from the intertropical convergence zone (monthly average of 66.1%), while at 400 K the Sierra Madres and the Central United States combined constitute the dominant source region (monthly average of 44.1%, compared to 36.6% of the combined Intertropical Convergence Zone regions). When evaluating the impact of cross‐tropopause convection on the composition and chemistry of the upper troposphere and lower stratosphere, the effects of the NAMA on both the distribution of convective outflow and the residence time of convectively influenced air masses within the NAMA region must be considered., Key Points A trajectory analysis of overshooting tops identifies effect of North American Monsoon Anticyclone on cross‐tropopause convective outflowThe North American monsoons anticyclone's influence is greatest in August concurrent with the overshooting convection maximum frequencyThe intertropical convergence zone contributes most of the overshooting tops, but at upper levels other regions' contributions increase

Hydrographic data collected from research cruises, bottom‐anchored moorings, drifting Ice‐Tethered Profilers, and satellite altimetry in the Beaufort Gyre region of the Arctic Ocean document an increase of more than 6,400 km3 of liquid freshwater content from 2003 to 2018: a 40% growth relative to the climatology of the 1970s. This fresh water accumulation is shown to result from persistent anticyclonic atmospheric wind forcing (1997–2018) accompanied by sea ice melt, a wind‐forced redirection of Mackenzie River discharge from predominantly eastward to westward flow, and a contribution of low salinity waters of Pacific Ocean origin via Bering Strait. Despite significant uncertainties in the different observations, this study has demonstrated the synergistic value of having multiple diverse datasets to obtain a more comprehensive understanding of Beaufort Gyre freshwater content variability. For example, Beaufort Gyre Observational System (BGOS) surveys clearly show the interannual increase in freshwater content, but without satellite or Ice‐Tethered Profiler measurements, it is not possible to resolve the seasonal cycle of freshwater content, which in fact is larger than the year‐to‐year variability, or the more subtle interannual variations., Key Points Beaufort Gyre freshwater content time series (2003–2018) from different data sets are updated, compared, and analyzedQualitative and quantitative estimates of factors and mechanisms driving freshwater content changes are providedIn 2003–2018, the major sources of accumulated fresh water were sea ice melt, Mackenzie River runoff, and Bering Strait transport

In this work, we use existing particulate matter (PM) data from Dakar, Senegal, satellite aerosol optical depth (AOD) and the Weather Research and Forecasting (WRF) model to evaluate the role of dust transport from the Sahara and PM concentrations and exposure into other administrative districts of Senegal during 2015 and 2016. We also use data from the Ministry of Health to examine spatial and temporal patterns of acute respiratory infections, asthma, bronchitis, and tuberculosis across Senegal with an emphasis on Northern Hemisphere winter December–February, when air quality is poor, and June–August when there is an improvement in air quality. Measurements in Dakar, Senegal, suggest hazardous PM10 concentrations associated with Saharan dust storms but lower PM10 concentrations during the summer. The WRF dust simulations show a similar temporal pattern to the observations in Dakar, Senegal, with notable biases. However, the WRF model suggests that the highest dust concentrations are found across the northern half of Senegal during the winter season where there are no currently PM measurements. Health data during 2015–2016 show the highest prevalence of asthma and bronchitis in Dakar, Senegal, suggesting that other sources of air pollution are important. Acute respiratory infection is prevalent throughout the country with the high prevalence found in rural zones, for children between 12 and 59 months. All measures including real‐time monitoring, air quality forecast, and communication should be used to protect the public from potentially hazardous environmental conditions during the winter season., Key Points Saharan dust transport is responsible for poor air quality over Senegal from attribution dust simulations of the WRF modelDuring 2015 and 2016, asthma and bronchitis prevalence are highest in the administrative district of Dakar during the summer season (JJA)ARI prevalence exceeds 9,000 per 100,000 for children between 12 and 59 months in the administrative districts of Kafferine during DJF of 2015–2016

Well‐switching programs in Bangladesh have successfully lowered arsenic exposure. In these programs, households switch from wells that are labeled “unsafe” to nearby wells labeled “safe,” but these designations are usually based on inherently inaccurate field kit measurements. Here, we (a) compare the efficacy of field‐kit measurements to accurate laboratory measurements for well switching, (b) investigate the potential impact on well switching of the chosen “safe” threshold, and (c) consider the possible benefits of providing more detailed concentration information than just “safe” and “unsafe.” We explore different hypothetical mitigation scenarios by combining two extensive data sets from Araihazar Bangladesh: a blanket survey of 6595 wells over 25 km2 based on laboratory measurements and 943 paired kit and laboratory measurements from the same area. The results indicate that the decline in average arsenic exposure from relying on kit rather than laboratory data is modest in relation to the logistical and financial challenge of delivering exclusively laboratory data. The analysis further indicates that the 50 μg/L threshold used in Bangladesh to distinguish safe and unsafe wells, rather than the WHO guideline of 10 μg/L, is close to optimal in terms of average exposure reduction. We also show that providing kit data at the maximum possible resolution rather than merely classifying wells as unsafe or safe would be even better. These findings are relevant as the government of Bangladesh is about to launch a new blanket testing campaign of millions of wells using field kits., Key Points Well‐switching based on kit or laboratory measurements produces similar reductions in arsenic exposureThe optimal well switching threshold for reducing arsenic exposure is near 50 μg/L, higher than the WHO guideline of 10 μg/LProviding actual kit readings instead of classifying wells as just "unsafe" or "safe" could lead to a higher reduction in arsenic exposure

Dust storms, such as those associated with haboobs and strong regional winds, are frequently assumed to cause increases in cases of Valley fever (coccidioidomycosis). The disease is caused by inhaling arthroconidia of Coccidioides fungi that, after being disturbed from semi‐desert subsoil, have become airborne. Fungal arthroconidia can be transported in low‐wind conditions as well as in individual dust events, but there is no reliable evidence that all or most dust storms consistently lead to subsequent increases in coccidioidomycosis cases. Following a review of the relevant literature, this study examines the relationship between dust storms and coccidioidomycosis cases to determine if there is a consistent and general association between them. All recorded dust storms from 2006 to 2020 in and near the Phoenix area of Maricopa County, Arizona and the Bakersfield area of Kern County, California were used in a compositing analysis (superposed epoch analysis) of subsequent coccidioidomycosis cases in each area. Analyses of monthly and weekly disease case data showed no statistical differences in the patterns of coccidioidomycosis cases following dust storms versus non‐dust storm conditions, for the entire data set as well as for seasonal subsets of the data. This study thoroughly analyzes post‐dust storm coccidioidomycosis cases for a large set of dust storms, and it confirms and expands upon previous literature, including a recent study that measured airborne arthroconidia and found no consistent links connecting wind and dust conditions to increases in coccidioidomycosis., Key Points It is often asserted that large dust storms like haboobs generally lead to increases in cases of Valley fever (coccidioidomycosis)This study thoroughly tests that assertion using dust storm and coccidioidomycosis case data over many yearsConsistent with literature, results show no statistical difference in cases of the disease after dust storm and non‐dust‐storm conditions

Recurrent and large forest fires negatively impact ecosystem, air quality, and human health. Moderate Resolution Imaging Spectroradiometer fire product is used to identify forest fires over central India domain, an extremely fire prone region. The study finds that from 2001 to 2020, ∼70% of yearly forest fires over the region occurred during March (1,857.5 counts/month) and April (922.8 counts/month). Some years such as 2009, 2012, and 2017 show anomalously high forest fires. The role of persistent warmer temperatures and multiple climate extremes in increasing forest fire activity over central India is comprehensively investigated. Warmer period from 2006 to 2020 showed doubling and tripling of forest fire activity during forest fire (February–June; FMAMJ) and non‐fire (July–January; JASONDJ) seasons, respectively. From 2015 JASONDJ to 2018 FMAMJ, central India experienced a severe heatwave, a rare drought and an extremely strong El Niño, the combined effect of which is linked to increased forest fires. Further, the study assesses quinquennial spatiotemporal changes in forest fire characteristics such as fire count density and average fire intensity. Deciduous forests of Jagdalpur‐Gadchiroli Range and Indravati National Park in Chhattisgarh state are particularly fire prone (>61 fire counts/grid) during FMAMJ and many forest fires are of high intensity (>45 MW). Statistical associations link high near surface air temperature and low precipitation during FMAMJ to significantly high soil temperature, low soil moisture content, low evapotranspiration and low normalized difference vegetation index. This creates a significantly drier environment, conducive for high forest fire activity in the region., Key Points Compared to 2001–2005, central India domain forest fire activity during 2006–2020 doubled in forest fire season and tripled in non‐fire seasonRole of persistent warmer temperatures and multiple climate extremes in increasing forest fire activity over central India is highlightedDeciduous forests of Jagdalpur‐Gadchiroli Range and Indravati National Park are extremely fire prone and fires are of high intensity

Along ultraslow spreading ridges melt is distributed unequally, but melt focusing guides melt away from amagmatic segments toward volcanic centers. An interplay of tectonism and magmatism is thought to control melt ascent, but the detailed process of melt extraction is not yet understood. We present a detailed image of the seismic velocity structure of the Logachev volcanic center and adjacent region along the Knipovich Ridge. With travel times of P‐ and S‐waves of 3,959 earthquakes we performed a local earthquake tomography. We simultaneously inverted for source locations, velocity structure and the Vp/Vs‐ratio. An extensive low velocity anomaly coincident with high Vp/Vs‐ratios >1.9 lies underneath the volcanic center at depths of 10 km below sea level in an aseismic area. More shallow, tightly clustered earthquake swarms connect the anomaly to a shallow anomaly with high Vp/Vs‐ratio beneath the basaltic seafloor. We consider the deep low‐velocity anomaly to represent an area of partial melt from which melts ascent vertically to the surface and northwards into the adjacent segment. By comparing tomographic studies of the Logachev and Southwest Indian Ridge Segment‐8 volcano we conclude that volcanic centers of ultraslow spreading ridges host spatially confined, circular partial melt areas below 10 km depth, in contrast to the shallow extended melt lenses along fast spreading ridges. Lateral feeding over distances of 35 km is possible at orthogonal spreading segments, but limited at the obliquely spreading Knipovich Ridge., Plain Language Summary: Mid‐ocean ridges mark the tectonic plate boundaries, where the plates drift apart. Fresh magma rises into the gap and builds new seafloor. The slower the plates drift apart, the less magma is present underneath the ridge. At very slow spreading ridges there is not enough magma to build new seafloor along the entire length of the ridge. Rather, melt is guided toward individual volcanic centers spaced at about 100 km, where melt accumulates and ascents. In our study we try to find melt storage areas and ascent paths of such a volcanic center. With velocities of different seismic wave types from earthquakes we map the velocity structure of the area underneath the major Logachev volcanic center. Lower velocities indicate an area partly including melt at depths of more than 10 km, far deeper than at mid‐ocean ridges with sufficient melt supply. From the deep magma reservoir, many earthquake swarms map the long ascent path of melt to the surface. The interplay of magmatic and tectonic activity is important here. In a comparison with results from another volcanic center, we find that lateral magma feeding is possible in orthogonal spreading, but limited in oblique spreading, as at the Knipovich Ridge., Key Points: Active volcanic centers at ultraslow spreading ridges host deeper and more confined partial melt areas than faster spreading ridges. Earthquake swarms delineate melt ascent paths from the partial melt area to the surface. Lateral feeding at shallow depths into subordinate segments is prevented by ridge obliquity., Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659

We assess the detectability of COVID‐like emissions reductions in global atmospheric CO2 concentrations using a suite of large ensembles conducted with an Earth system model. We find a unique fingerprint of COVID in the simulated growth rate of CO2 sampled at the locations of surface measurement sites. Negative anomalies in growth rates persist from January 2020 through December 2021, reaching a maximum in February 2021. However, this fingerprint is not formally detectable unless we force the model with unrealistically large emissions reductions (2 or 4 times the observed reductions). Internal variability and carbon‐concentration feedbacks obscure the detectability of short‐term emission reductions in atmospheric CO2. COVID‐driven changes in the simulated, column‐averaged dry air mole fractions of CO2 are eclipsed by large internal variability. Carbon‐concentration feedbacks begin to operate almost immediately after the emissions reduction; these feedbacks reduce the emissions‐driven signal in the atmosphere carbon reservoir and further confound signal detection., Key Points Climate model simulations suggest a lagged response in the global growth rate of atmospheric CO2 due to COVID‐19 emissions reductionsDetection of this reduction in observations is hampered by internal variability combined with reduced ocean and land uptake of CO2 Our results foreshadow the challenges of detecting the effects of CO2 mitigation efforts to meet the Paris climate agreement

Black carbon (BC) plays an important role in climate and health sciences. Using the combination of a year real‐time BC observation (photoacoustic extinctiometer) and data for PM2.5 and selected co‐pollutants, we herein show that annual BC Mass concentration has a bi‐modal distribution, in a cold‐climate city of Montreal. In addition to the summer peak, a winter BC peak was observed (up to 0.433 μg/m3), lasting over 3 months. A comparative study between two air pollution hotspots, downtown and Montreal international airport indicated that airborne average BC Mass concentration in downtown was 0.344 μg/m3, whereas in the residential areas around Montreal airport BC Mass values were over 400% higher (1.487 μg/m3). During the numerous snowfall events, airborne BC Mass concentration decreased. High‐resolution scanning/transmission electron microscopy with energy dispersive X‐ray spectroscopy analysis of the snow samples provided evidence that airborne BC particles or carbon nanomaterials were indeed transferred from polluted air to snow. During the COVID‐19 lockdown, the BC concentration and selected co‐pollutants, decreased up to 72%, confirming the predominance of anthropogenic activities in BC emission. This first cold‐climate BC data set can be essential for more accurate air quality and climate modeling. About one‐third of the Earth's land surface receive snow annually, the impact of this study on air quality, health and climate change is discussed., Key Points Montreal black carbon (BC) mass concentration had a bi‐modal distribution (summer and winter). Airport BC Mass was >400% higher than downtownBC Mass decreased during snow falls. High‐resolution scanning/transmission electron microscopy with energy dispersive X‐ray spectroscopy showed BC or carbon nanomaterials, at snow‐air interfaceThe concentration of BC and some co‐pollutants decreased to 72% in the COVID‐19 period, pointing to significant anthropogenic BC emissions

The PM2.5 exposure risk assessment is the foundation to reduce its adverse effects. Population survey‐related data have been deficient in high spatiotemporal detailed descriptions. Social media data can quantify the PM2.5 exposure risk at high spatiotemporal resolutions. However, due to the no‐sample characteristics of social media data, PM2.5 exposure risk for older adults is absent. We proposed combining social media data and population survey‐derived data to map the total PM2.5 exposure risk. Hourly exceedance PM2.5 exposure risk indicators based on population modeling (HEPEpmd) and social media data (HEPEsm) were developed. Daily accumulative HEPEsm and HEPEpsd ranged from 0 to 0.009 and 0 to 0.026, respectively. Three peaks of HEPEsm and HEPEpsd were observed at 13:00, 18:00, and 22:00. The peak value of HEPEsm increased with time, which exhibited a reverse trend to HEPEpsd. The spatial center of HEPEsm moved from the northwest of the study area to the center. The spatial center of HEPEpsd moved from the northwest of the study area to the southwest of the study area. The expansion area of HEPEsm was nearly 1.5 times larger than that of HEPEpsd. The expansion areas of HEPEpsd aggregated in the old downtown, in which the contribution of HEPEpsd was greater than 90%. Thus, this study introduced various source data to build an easier and reliable method to map total exceedance PM2.5 exposure risk. Consequently, exposure risk results provided foundations to develop PM2.5 pollution mitigation strategies as well as scientific supports for sustainability and eco‐health achievement., Key Points Total exceedance PM2.5 exposure risk, including youths and older adults, was mappedThe hourly exceedance PM2.5 exposure risk (HEPE)psd was more aggregated than the HEPEsm Contribution of HEPEpsd varied geographically with percentage more than 50%

The concept of resilience is multi‐faceted. This commentary builds upon the analytical distinctions of resilience provided by Urquiza et al. (2021, https://doi.org/10.1029/2020EF001508). In response to this article, we emphasize several distinctions between resilience and other systems concepts. These include distinctions between resilience, risk, and vulnerability, the tradeoff between resilience and efficiency, resilience contrasted with robustness, the relationship between resilience and sustainability, and finally methods for building resilience‐by‐design or resilience‐by‐intervention. Improving understanding of these concepts will enable planners to select resilience strategies that best support their system goals. We use examples from the 2020–2021 coronavirus pandemic to illustrate the concepts and the juxtapositions between them., Key Points Resilience is one of many properties of systems affected by threat and resilience differ from robustness, sustainability, and riskBuilding resilience will require tradeoff on efficiency in complex systemsResilience can be build by design and by intervention

The high computational cost of Global Climate Models (GCMs) is a problem that limits their use in many areas. Recently an inverse climate modeling (InvCM) method, which fixes the global mean sea surface temperature (SST) and evolves the CO2 mixing ratio to equilibrate climate, has been implemented in a cloud‐resolving model. In this article, we apply InvCM to ExoCAM GCM aquaplanet simulations, allowing the SST pattern to evolve while maintaining a fixed global‐mean SST. We find that InvCM produces the same climate as normal slab‐ocean simulations but converges an order of magnitude faster. We then use InvCM to calculate the equilibrium CO2 for SSTs ranging from 290 to 340 K at 1 K intervals and reproduce the large increase in climate sensitivity at an SST of about 315 K at much higher temperature resolution. The speedup provided by InvCM could be used to equilibrate GCMs at higher spatial resolution or to perform broader parameter space exploration in order to gain new insight into the climate system. Additionally, InvCM could be used to find unstable and hidden climate states, and to find climate states close to bifurcations such as the runaway greenhouse transition., Key Points We converge a GCM by varying the CO2 while keeping the global‐mean surface temperature fixed (the inverse climate modeling method)Inverse climate modeling converges about 10 times faster than normal slab‐ocean simulationsThe SST gradient response timescale is the bottleneck on the convergence rate of inverse climate modeling

Air pollution levels are uneven within cities, contributing to persistent health disparities between neighborhoods and population sub‐groups. Highly spatially resolved information on pollution levels and disease rates is necessary to characterize inequities in air pollution exposure and related health risks. We leverage recent advances in deriving surface pollution levels from satellite remote sensing and granular data in disease rates for one city, Washington, DC, to assess intra‐urban heterogeneity in fine particulate matter (PM2.5)‐ attributable mortality and morbidity. We estimate PM2.5‐attributable cases of all‐cause mortality, chronic obstructive pulmonary disease, ischemic heart disease, lung cancer, stroke, and asthma emergency department (ED) visits using epidemiologically derived health impact functions. Data inputs include satellite‐derived annual mean surface PM2.5 concentrations; age‐resolved population estimates; and statistical neighborhood‐, zip code‐ and ward‐scale disease counts. We find that PM2.5 concentrations and associated health burdens have decreased in DC between 2000 and 2018, from approximately 240 to 120 cause‐specific deaths and from 40 to 30 asthma ED visits per year (between 2014 and 2018). However, remaining PM2.5‐attributable health risks are unevenly and inequitably distributed across the District. Higher PM2.5‐attributable disease burdens were found in neighborhoods with larger proportions of people of color, lower household income, and lower educational attainment. Our study adds to the growing body of literature documenting the inequity in air pollution exposure levels and pollution health risks between population sub‐groups, and highlights the need for both high‐resolution disease rates and concentration estimates for understanding intra‐urban disparities in air pollution‐related health risks., Key Points Fine particulate matter‐attributable health risks are unevenly and inequitably distributed across Washington, DCHigher PM2.5‐attributable disease burdens are found in neighborhoods with larger proportions of people of color in Washington, DCHigh‐resolution disease and concentration estimates are needed to understand intra‐urban disparities in air pollution‐related health risks

Following extreme drought during the 2019–2020 bushfire summer, the eastern part of Australia suffered from a week‐long intense rainfall and extensive flooding in March 2021. Understanding how much water storage changes in response to these climate extremes is critical for developing timely water management strategies. To quantify prompt water storage changes associated with the 2021 March flooding, we processed the low‐latency (1–3 days), high‐precision intersatellite laser ranging measurements from GRACE Follow‐On spacecraft and determined instantaneous gravity changes along spacecraft orbital passes. Such new data processing detected an abrupt surge of water storage approaching 60–70 trillion liters (km3 of water) over a week in the region, which concurrently caused land subsidence of ∼5 mm measured by a network of ground GPS stations. This was the highest speed of ground water recharge ever recorded in the region over the last two decades. Compared to the condition in February 2020, the amount of recharged water was similar but the recharge speed was much faster in March 2021. While these two events together replenished the region up to ∼80% of the maximum storage over the last two decades, the wet antecedent condition of soils in 2021 was distinctly different from the dry conditions in 2020 and led to generating extensive runoff and flooding in 2021., Key Points New GRACE Follow‐On gravity data processing quantified prompt changes in water storage by the heavy rainfall and flooding in March 2021The eastern Australia experienced the highest speed of ground water recharge and wet antecedent soils were responsible for extensive flooding in 2021The study demonstrated the feasibility of rapid processing (1–3 days) for immediate assessment of mass changes from extreme events

The popular beaches of the San Diego‐Tijuana (US/MX) border region are often impacted by untreated wastewater sourced from Mexico—via the Tijuana River Estuary (TJRE) and San Antonio de los Buenos outfall at the Pt. Bandera (SAB/PTB) shoreline, leading to impacted beaches and human illness. The US‐Mexico‐Canada trade agreement will fund border infrastructure projects reducing untreated wastewater discharges. However, estimating project benefits such as reduced human illness and beach impacts is challenging. We develop a coupled hydrodynamic, norovirus (NoV) pathogen, and swimmer illness risk model with the wastewater sources for the year 2017. The model is used to evaluate the reduction in human illness and beach impacts under baseline conditions and three infrastructure diversion scenarios which (Scenario A) reduce SAB/PTB discharges and moderately reduce TJRE inflows or (Scenarios B, C) strongly reduce TJRE in inflows only. The model estimates shoreline untreated wastewater and NoV concentrations, and the number of NoV ill swimmers at Imperial Beach CA. In the Baseline, the percentage of swimmers becoming ill is 3.8% over 2017, increasing to 4.5% during the tourist season (Memorial to Labor Day) due to south‐swell driven SAB/PTB plumes. Overall, Scenario A provides the largest reduction in ill swimmers and beach impacts for the tourist season and full year. The 2017 tourist season TJRE inflows were not representative of those in 2020, yet, Scenario A likely still provides the greatest benefit in other years. This methodology can be applied to other coastal regions with wastewater inputs., Key Points We develop a coupled hydrodynamic, a pathogen, and swimmer illness model to evaluate US/MX border region shoreline wastewater impactsYear 2017 model simulations quantitatively assess the benefits of four infrastructure scenarios that reduce regional wastewater inputsTourist season has the most ill swimmers from plumes advecting north from MX, and mitigating this source yields the largest benefit

Global monitoring of seasonal snow water equivalent (SWE) has advanced significantly over the past decades. However, challenges remain when estimating SWE from passive and active microwave signatures, because a priori characterization of snow properties is required for SWE retrievals. Numerical experiments have shown that utilizing physical snow models to acquire snowpack characterization can potentially improve microwave‐based SWE retrievals. This study aims to identify the challenges of assimilating active and passive microwave signatures with physical snow models, and to examine solutions to those challenges. Guided by observations from a point‐based study, we designed a sensitivity experiment to quantify the effects of changes in the physically modeled SWE—and of corresponding changes to other snowpack properties—to the microwave‐based SWE retrievals. The results indicate that assimilating microwave signatures with physical snow models face some critical challenges associated with the physical relationship between SWE and snow microstructure. We demonstrate these challenges can be overcome if the microwave algorithms account for these relationships., Key Points Simulated snow properties from SnowModel are used in microwave‐based snow water equivalent (SWE) retrievals by MEMLS3&aBiases in physically modeled SWE can induce larger biases in microwave‐based SWE retrievalsThe challenges can be mitigated when microwave algorithms account for the physical relationship of snow properties

We present a data‐driven approach to clustering or grouping Global Navigation Satellite System (GNSS) stations according to observed velocities, displacements or other selected characteristics. Clustering GNSS stations provides useful scientific information, and is a necessary initial step in other analysis, such as detecting aseismic transient signals (Granat et al., 2013, https://doi.org/10.1785/0220130039). Desired features of the data can be selected for clustering, including some subset of displacement or velocity components, uncertainty estimates, station location, and other relevant information. Based on those selections, the clustering procedure autonomously groups the GNSS stations according to a selected clustering method. We have implemented this approach as a Python application, allowing us to draw upon the full range of open source clustering methods available in Python's scikit‐learn package (Pedregosa et al., 2011, https://doi.org/10.5555/1953048.2078195). The application returns the stations labeled by group as a table and color coded KML file and is designed to work with the GNSS information available from GeoGateway (Donnellan et al., 2021, https://doi.org/10.1007/s12145-020-00561-7; Heflin et al., 2020, https://doi.org/10.1029/2019ea000644) but is easily extensible. We demonstrate the methodology on California and western Nevada. The results show partitions that follow faults or geologic boundaries, including for recent large earthquakes and post‐seismic motion. The San Andreas fault system is most prominent, reflecting Pacific‐North American plate boundary motion. Deformation reflected as class boundaries is distributed north and south of the central California creeping section. For most models a cluster boundary connects the southernmost San Andreas fault with the Eastern California Shear Zone (ECSZ) rather than continuing through the San Gorgonio Pass., Key Points Unsupervised clustering methods provide a data‐driven way of analyzing and partitioning Global Navigation Satellite System observations of crustal deformationDeformation is distributed across the San Andreas fault system but is localized at the creeping section in central CaliforniaThe Southern San Andreas fault connects with the Eastern California Shear Zone rather than the rest of the San Andreas fault system

25 pags., 14 figs., 2 tabs., Tropospheric ozone ((Formula presented.)) is an important greenhouse gas and a surface pollutant. The future evolution of (Formula presented.) abundances and chemical processing are uncertain due to a changing climate, socioeconomic developments, and missing chemistry in global models. Here, we use an Earth System Model with natural halogen chemistry to investigate the changes in the (Formula presented.) budget over the 21st century following Representative Concentration Pathway (RCP)6.0 and RCP8.5 climate scenarios. Our results indicate that the global tropospheric (Formula presented.) net chemical change (NCC, chemical gross production minus destruction) will decrease (Formula presented.), notwithstanding increasing or decreasing trends in ozone production and loss. However, a wide range of surface NCC variations (from −60 (Formula presented.) to 150 (Formula presented.)) are projected over polluted regions with stringent abatements in (Formula presented.) precursor emissions. Water vapor and iodine are found to be key drivers of future tropospheric (Formula presented.) destruction, while the largest changes in (Formula presented.) production are determined by the future evolution of peroxy radicals. We show that natural halogens, currently not considered in climate models, significantly impact on the present-day and future global (Formula presented.) burden reducing (Formula presented.) 30–35 Tg (11–15 (Formula presented.)) of tropospheric ozone throughout the 21st century regardless of the RCP scenario considered. This highlights the importance of including natural halogen chemistry in climate model projections of future tropospheric ozone., EC, H2020, H2020 Priority Excellent Science, H2020 European Research Council (ERC). Grant Number: ERC-2016-COG726349; NSF, Office of Science of the US Department of Energy, PICT-2016-0714 (ANPCyT) i-COOP-B20331 (CSIC + CONICET)

PM2.5 is a major component of air pollution in China and has a serious threat to public health. It is very important to quantify spatial characteristics of the health effects caused by outdoor PM2.5 exposure. This study analyzed the spatial distribution of PM2.5 concentration (45.9 μg/m3 national average in 2016) and premature mortality attributed to PM2.5 in cities at the prefectural level and above in China in 2016. Using the Global Exposure Mortality Model (GEMM), the total premature mortality in China was estimated to be 1.55 million persons, and the per capita mortality was 11.2 per 10,000 persons in the year 2016, resulting in higher estimates compared to the integrated exposure‐response model. We assessed the premature mortality attributed to PM2.5 through common diseases, including ischemic heart disease (IHD), cerebrovascular disease (CEV), chronic obstructive pulmonary disease (COPD), lung cancer (LC), and lower respiratory infections (LRI). The premature mortality due to IHD and CEV accounted for 68.5% of the total mortality, and the per capita mortality (per 10,000 persons) for all ages due to IHD was 3.86, the highest among diseases. For the spatial distribution of disease‐specific premature mortality, the top two highest absolute numbers of premature mortality associated with IHD, CEV, LC, and LRI, respectively, were found in Chongqing and Beijing. In 338 cities of China, we have found a significant positive spatial autocorrelation of per capita premature mortality, indicating the necessity of coordinated regional governance for an efficient control of PM2.5., Key Points Based on an extended observational network in China, 1.55 million premature deaths attributed to PM2.5 is estimated in 2016Higher premature mortality using the Global Exposure Mortality Model compared to the Integrated Exposure‐Response modelA positive spatial autocorrelation of the per capita premature mortality is found at the city levels

As droughts have widespread social and ecological impacts, it is critical to develop long‐term adaptation and mitigation strategies to reduce drought vulnerability. Climate models are important in quantifying drought changes. Here, we assess the ability of 285 CMIP6 historical simulations, from 17 models, to reproduce drought duration and severity in three observational data sets using the Standardized Precipitation Index (SPI). We used summary statistics beyond the mean and standard deviation, and devised a novel probabilistic framework, based on the Hellinger distance, to quantify the difference between observed and simulated drought characteristics. Results show that many simulations have less than ±10% error in reproducing the observed drought summary statistics. The hypothesis that simulations and observations are described by the same distribution cannot be rejected for more than 80% of the grids based on our H distance framework. No single model stood out as demonstrating consistently better performance over large regions of the globe. The variance in drought statistics among the simulations is higher in the tropics compared to other latitudinal zones. Though the models capture the characteristics of dry spells well, there is considerable bias in low precipitation values. Good model performance in terms of SPI does not imply good performance in simulating low precipitation. Our study emphasizes the need to probabilistically evaluate climate model simulations in order to both pinpoint model weaknesses and identify a subset of best‐performing models that are useful for impact assessments., Key Points Simulations reproduce observed drought duration and severity in terms of the Standardized Precipitation Index (SPI) but simulated low precipitation is considerably biasedVariance in drought statistics among the simulations is higher in the tropics compared to other latitudinal zonesGood model performance in terms of SPI does not imply that low precipitation values are well simulated by the climate models

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 alternate 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 resultant 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 combinations of initial variables in heat vulnerability among urban classes of Nebraska and clustering of high and low heat vulnerable areas within the highest urbanized sections. 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., Key Points Despite similar incidence rates, Heat Vulnerability Index (HVI) in rural areas is under studied in comparison to urban areasThe environmental vulnerability variables in rural areas are dissimilar to urban areas, so we applied different variables to calculate themWe found different organization of socioeconomic variables in calculated HVIs, suggesting separate heat strategies for urbanization levels

Mine wastes pollute the environment with metals and metalloids in toxic concentrations, causing problems for humans and wildlife. Microorganisms colonize and inhabit mine wastes, and can influence the environmental mobility of metals through metabolic activity, biogeochemical cycling and detoxification mechanisms. In this article we review the microbiology of the metals and metalloids most commonly associated with mine wastes: arsenic, cadmium, chromium, copper, lead, mercury, nickel and zinc. We discuss the molecular mechanisms by which bacteria, archaea, and fungi interact with contaminant metals and the consequences for metal fate in the environment, focusing on long‐term field studies of metal‐impacted mine wastes where possible. Metal contamination can decrease the efficiency of soil functioning and essential element cycling due to the need for microbes to expend energy to maintain and repair cells. However, microbial communities are able to tolerate and adapt to metal contamination, particularly when the contaminant metals are essential elements that are subject to homeostasis or have a close biochemical analog. Stimulating the development of microbially reducing conditions, for example in constructed wetlands, is beneficial for remediating many metals associated with mine wastes. It has been shown to be effective at low pH, circumneutral and high pH conditions in the laboratory and at pilot field‐scale. Further demonstration of this technology at full field‐scale is required, as is more research to optimize bioremediation and to investigate combined remediation strategies. Microbial activity has the potential to mitigate the impacts of metal mine wastes, and therefore lessen the impact of this pollution on planetary health., Key Points Microbes colonize and inhabit mine wastes, they tolerate high concentrations of metals and contribute to soil functioning and plant growthMicrobes transform metal speciation and environmental mobility, through metabolism, biogeochemical cycling and metal resistance mechanismsBeneficial microbial activity can be stimulated to remediate metal‐containing mine wastes, but more long‐term field studies are required

The lightning data products generated by the low‐frequency (LF) radio lightning locating system (LLS) deployed during the Remote sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observation (RELAMPAGO) field campaign in Argentina provide a valuable data set to research the lightning evolution and characteristics of convective storms that produce high‐impact weather. LF LLS data sets offer a practical range for mesoscale studies, allowing for the observation of lightning characteristics of storms such as mesoscale convective systems or large convective lines that travel longer distances which are not necessarily staying in range of regional VHF‐based lightning detection systems throughout their lifetime. LF LLSs also provide different information than optical space‐borne lightning detectors. Lightning measurements exclusive to LF systems include discharge peak current, lightning polarity, and lightning type classification based on the lightning‐emitted radio waveform. Furthermore, these measurements can provide additional information on flash rates (e.g., positive cloud‐to‐ground flash rate) or narrow bipolar events which may often be associated with dynamically intense convection. In this article, the geolocation and data processing of the LF data set collected during RELAMPAGO is fully described and its performance characterized, with location accuracy better than 10 km. The detection efficiency (DE) of the data set is compared to that of the Geostationary Lightning Mapper, and spatiotemporal DE losses in the LF data set are discussed. Storm case studies on November 10, 2018, highlight the strengths of the data set, which include robust flash clustering and insightful flash rate and peak current measures, while illustrating how its limitations, including DE losses, can be managed., Key Points Geolocation and processing of the low‐frequency (LF) data set collected during the Remote sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observation campaign is fully described and characterizedUnique LF observations include discharge peak current, polarity, and possibly type classification based on the emitted radio waveformLF spatiotemporal detection efficiency and storm case studies are discussed and compared to Geostationary Lightning Mapper data

Forest and vegetation fires, used as tools for agriculture and deforestation, are a major source of air pollutants and can cause serious air quality issues in many parts of Asia. Actions to reduce fire may offer considerable, yet largely unrecognized, options for rapid improvements in air quality. In this study, we used a combination of regional and global air quality models and observations to examine the impact of forest and vegetation fires on air quality degradation and public health in Southeast Asia (including Mainland Southeast Asia and south‐eastern China). We found that eliminating fire could substantially improve regional air quality across Southeast Asia by reducing the population exposure to fine particulate matter (PM2.5) concentrations by 7% and surface ozone concentrations by 5%. These reductions in PM2.5 exposures would yield a considerable public health benefit across the region; averting 59,000 (95% uncertainty interval (95UI): 55,200–62,900) premature deaths annually. Analysis of subnational infant mortality rate data and PM2.5 exposure suggested that PM2.5 from fires disproportionately impacts poorer populations across Southeast Asia. We identified two key regions in northern Laos and western Myanmar where particularly high levels of poverty coincide with exposure to relatively high levels of PM2.5 from fires. Our results show that reducing forest and vegetation fires should be a public health priority for the Southeast Asia region., Key Points Eliminating forest and vegetation fires could substantially improve regional air quality in Mainland Southeast AsiaReducing exposure to particulate and ozone pollution from fires would yield a considerable public health benefit across Southeast AsiaParticulate air pollution from fires disproportionately impacts poorer populations across Southeast Asia

As anthropogenic emissions continue to decline and emissions from landscape (wild, prescribed, and agricultural) fires increase across the coming century, the relative importance of landscape‐fire smoke on air quality and health in the United States (US) will increase. Landscape fires are a large source of fine particulate matter (PM2.5), which has known negative impacts on human health. The seasonal and spatial distribution, particle composition, and co‐emitted species in landscape‐fire emissions are different from anthropogenic sources of PM2.5. The implications of landscape‐fire emissions on the sub‐national temporal and spatial distribution of health events and the relative health importance of specific pollutants within smoke are not well understood. We use a health impact assessment with observation‐based smoke PM2.5 to determine the sub‐national distribution of mortality and the sub‐national and sub‐annual distribution of asthma morbidity attributable to US smoke PM2.5 from 2006 to 2018. We estimate disability‐adjusted life years (DALYs) for PM2.5 and 18 gas‐phase hazardous air pollutants (HAPs) in smoke. Although the majority of large landscape fires occur in the western US, we find the majority of mortality (74%) and asthma morbidity (on average 75% across 2006–2018) attributable to smoke PM2.5 occurs outside the West, due to higher population density in the East. Across the US, smoke‐attributable asthma morbidity predominantly occurs in spring and summer. The number of DALYs associated with smoke PM2.5 is approximately three orders of magnitude higher than DALYs associated with gas‐phase smoke HAPs. Our results indicate awareness and mitigation of landscape‐fire smoke exposure is important across the US., Key Points While the majority of large fires occur in the United States (US) West, a majority of smoke‐attributable US mortality and morbidity occur east of ∼100 degWA higher percent of mortality and morbidity is attributable to smoke in high fire‐impacted northwestern states, relative to other US statesDisability‐adjusted life years attributable to fine particles in smoke are much higher than that from gas‐phase hazardous air pollutants

Spatial estimates of crop evapotranspiration with high accuracy from the field to watershed scale have become increasingly important for water management, particularly over irrigated agriculture in semiarid regions. Here, we provide a comprehensive assessment on patterns of annual agricultural water use over California's Central Valley, using 30‐m daily evapotranspiration estimates based on Landsat satellite data. A semiempirical Priestley‐Taylor approach was locally optimized and cross‐validated with available field measurements for major crops including alfalfa, almond, citrus, corn, pasture, and rice. The evapotranspiration estimates explained >70% variance in daily measurements from independent sites with an RMSE of 0.88 mm day−1. When aggregated over the Valley, we estimated an average evapotranspiration of 820 ± 290 mm yr−1 in 2014. Agricultural water use varied significantly across and within crop types, with a coefficient of variation ranging from 8% for Rice (1,110 ± 85 mm yr−1) to 59% for Pistachio (592 ± 352 mm yr−1). Total water uses in 2016 increased by 9.6%, as compared to 2014, mostly because of land‐use conversion from fallow/idle land to cropland. Analysis across 134 Groundwater Sustainability Agencies (GSAs) further showed a large variation of agricultural evapotranspiration among and within GSAs, especially for tree crops, e.g., almond evapotranspiration ranging from 339 ± 80 mm yr−1 in Tracy to 1,240 ± 136 mm yr−1 in Tri‐County Water Authority. Continuous monitoring and assessment of the dynamics and spatial heterogeneity of agricultural evapotranspiration provide data‐driven guidance for more effective land use and water planning across scales., Key Points A 30‐m daily evapotranspiration product estimated using regionally optimized approach and Landsat Analysis Ready Data Collection 1Assessment of agricultural consumptive water use over Central Valley provides critical guidance for sustainable groundwater managementLarge variation of water use was found, mostly due to crop diversity, age structure, and physiological factors

The combination of air quality (AQ) data from satellites and low‐cost sensor systems, along with output from AQ models, have the potential to augment high‐quality, regulatory‐grade data in countries with in situ monitoring networks and provide much needed AQ information in countries without them, including Low and Moderate Income Countries (LMICs). We demonstrate the potential of free and publicly available USA National Aeronautics and Space Administration (NASA) resources, which include capacity building activities, satellite data, and global AQ forecasts, to provide cost‐effective, and reliable AQ information to health and AQ professionals around the world. We provide illustrative case studies that highlight how global AQ forecasts along with satellite data may be used to characterize AQ on urban to regional scales, including to quantify pollution concentrations, identify pollution sources, and track the long‐range transport of pollution. We also provide recommendations to data product developers to facilitate and broaden usage of NASA resources by health and AQ stakeholders., Key Points Satellite data and global air quality models can augment data from surface monitors for health and air quality applicationsThese resources provide much needed, cost‐effective air quality information for many Low and Moderate Income CountriesWith case studies, we show that an effective approach to monitoring combines the strengths of these resources with surface monitors

Deep Chlorophyll Maxima (DCM) are ubiquitous features in stratified oceanic systems. Their establishment and maintenance result from hydrographical stability favoring specific environmental conditions with respect to light and nutrient availability required for phytoplankton growth. This stability can potentially be challenged by mesoscale eddies impacting the water column's vertical structure and thus the environmental parameters that condition the subsistence of DCMs. Here, data from the global BGC‐Argo float network are collocated with mesoscale eddies to explore their impact on DCMs. We show that cyclonic eddies, by providing optimal light and nutrient conditions, increase the occurrence of DCMs characterized by Deep Biomass Maxima for phytoplankton. In contrast, DCMs in anticyclonic eddies seem to be driven by photoacclimation as they coincide with Deep Acclimation Maxima without biomass accumulation. These findings suggest that the two types of eddies potentially have different impacts on the role of DCMs in global primary production., Key Points Deep Chlorophyll Maxima are key biological features of stratified environments that escape satellite ocean‐color detectionThe impact of mesoscale eddies on DCM is assessed by matching a global BGC‐Argo database with eddies detected from satellite altimetryWhile cyclonic eddies increase biomass maxima occurrences, anticyclonic eddies favor the presence of maxima resulting from photoacclimation

In 2020, people's health suffered a great crisis under the dual effects of the COVID‐19 pandemic and the extensive, severe wildfires in the western and central United States. Parks, including city, national, and cultural parks, offer a unique opportunity for people to maintain their recreation behaviors following the social distancing protocols during the pandemic. However, massive forest wildfires in western and central US, producing harmful toxic gases and smoke, pose significant threats to human health and affect their recreation behaviors and mobility to parks. In this study, we employed the geographically and temporally weighted regression (GTWR) Models to investigate how COVID‐19 and wildfires jointly shaped human mobility to parks, regarding the number of visits per capita, dwell time, and travel distance to parks, during June ‐ September 2020. We detected strong correlations between visitations and COVID‐19 incidence in southern Montana, western Wyoming, Colorado, and Utah before August. However, the pattern was weakened over time, indicating the decreasing trend of the degree of concern regarding the pandemic. Moreover, more park visits and lower dwell time were found in parks further away from wildfires and less air pollution in Washington, Oregon, California, Colorado, and New Mexico, during the wildfire season, suggesting the potential avoidance of wildfires when visiting parks. This study provides important insights on people's responses in recreation and social behaviors when facing multiple severe crises that impact their health and wellbeing, which could support the preparation and mitigation of the health impacts from future pandemics and natural hazards., Key Points We investigated human mobility patterns to parks under COVID‐19 pandemic and wildfire season in western and central United StatesWe found a general trend of avoidance to the parks with fewer visits and dwell time in the places with high COVID‐19 casesWith special demand of physical activities in pandemic, people travel further and spend longer time at the parks away from the wildfires

The COVID‐19 outbreak in 2020 prompted strict lockdowns, reduced human activity, and reduced emissions of air pollutants. We measured volatile organic compounds (VOCs) using a proton‐transfer‐reaction mass spectrometry instrument in Changzhou, China from 8 January through 27 March, including periods of pre‐lockdown, strict measures (level 1), and more relaxed measures (level 2). We analyze the data using positive matrix factorization and resolve four factors: textile industrial emissions (62 ± 10% average reduction during level 1 relative to pre‐lockdown), pharmaceutical industrial emissions (40 ± 20%), traffic emissions (71 ± 10%), and secondary chemistry (20 ± 20%). The two industrial sources showed different responses to the lockdown, so emissions from the industrial sector should not be scaled uniformly. The quantified changes in VOCs due to the lockdowns constrain emission inventories and inform chemistry‐transport models, particularly for sectors where activity data are sparse, as the effects of lockdowns on air quality are explored., Key Points We measured and quantified volatile organic compounds (VOCs) in Changzhou, China during the COVID‐19 lockdownsTraffic‐related VOC emissions were reduced by 70%, which agrees with reductions in traffic countsTextile and pharmaceutical industry VOC emissions responded differently, so industrial emissions should not be scaled uniformly

Ice shelf fracture is responsible for roughly half of Antarctic ice mass loss in the form of calving and can weaken buttressing of upstream ice flow. Large uncertainties associated with the ice sheet response to climate variations are due to a poor understanding of these fracture processes and how to model them. Here, we address these problems by implementing an anisotropic, nonlocal integral formulation of creep damage within a large‐scale shallow‐shelf ice flow model. This model can be used to study the full evolution of fracture from initiation of crevassing to rifting that eventually causes tabular calving. While previous ice shelf fracture models have largely relied on simple expressions to estimate crevasse depths, our model parameterizes fracture as a progressive damage evolution process in three‐dimensions (3‐D). We also implement an efficient numerical framework based on the material point method, which avoids advection errors. Using an idealized marine ice sheet, we test the creep damage model and a crevasse‐depth based damage model, including a modified version of the latter that accounts for damage evolution due to necking and mass balance. We demonstrate that the creep damage model is best suited for capturing weakening and rifting over shorter (monthly/yearly) timescales, and that anisotropic damage reproduces typically observed fracture patterns better than isotropic damage. Because necking and mass balance can significantly influence damage on longer (decadal) timescales, we discuss the potential for a combined approach between models to best represent mechanical weakening and tabular calving within long‐term simulations., Key Points Our shallow‐shelf creep damage model can represent the full evolution of ice shelf fracture from crevasse initiation to tabular calvingStrongly anisotropic creep damage produces sharp, arcuate rift patterns more consistent with observations than isotropic creep damageThe zero‐stress damage model poorly captures rifting, but a modified form accounts for damage evolution due to mass balance and necking

The frequent occurrence of local COVID‐19 today gives a strong necessity to better understand the effects of "source‐case" distance and gathering places, which are often considered to be the key factors of the localized spatial clustering of an epidemic. In this study, the localized spatial clustering of COVID‐19 cases, which originated in the Xinfadi market in Beijing from June–July 2020, was investigated by exploring the spatiotemporal characteristics of the clustering using descriptive statistics, point pattern analysis, and spatial autocorrelation calculation approaches. Spatial lag zero‐inflated negative binomial regression model and geographically weighted Poisson regression with spatial effects were also introduced to explore the factors which influenced the clustering of COVID‐19 cases at the micro spatial scale. It was found that the local epidemic can be significantly divided into two stages which are asymmetric in time. A significant spatial spillover effect of COVID‐19 was identified in both global and local modeling estimation. The dominant role of the “source‐case” distance effect, which was reflected in both global and local scales, was revealed. Relatively, the role of gathering places is not significant at the initial stage of the epidemic, but the upward trend of the significance of some places is obvious. The trend from "distance‐driven" to "density‐driven" of the localized spatial clustering of COVID‐19 was predicted. The effectiveness of blocking the transformation trend will be a key issue for the global response to the local COVID‐19., Key Points The spatiotemporal characteristics of localized spatial clustering of COVID‐19 were quantitively analyzed by multimethodThe improved quantitative research methods were applied in the modeling of the spatial clustering factors at the micro scaleThe trend from "distance‐driven" to "density‐driven" of the localized spatial clustering of COVID‐19 was predicted

Estimates of Amazon rainforest gross primary productivity (GPP) differ by a factor of 2 across a suite of three statistical and 18 process models. This wide spread contributes uncertainty to predictions of future climate. We compare the mean and variance of GPP from these models to that of GPP at six eddy covariance (EC) towers. Only one model's mean GPP across all sites falls within a 99% confidence interval for EC GPP, and only one model matches EC variance. The strength of model response to climate drivers is related to model ability to match the seasonal pattern of the EC GPP. Models with stronger seasonal swings in GPP have stronger responses to rain, light, and temperature than does EC GPP. The model to data comparison illustrates a trade‐off inherent to deterministic models between accurate simulation of a mean (average) and accurate responsiveness to drivers. The trade‐off exists because all deterministic models simplify processes and lack at least some consequential driver or interaction. If a model's sensitivities to included drivers and their interactions are accurate, then deterministically predicted outcomes have less variability than is realistic. If a GPP model has stronger responses to climate drivers than found in data, model predictions may match the observed variance and seasonal pattern but are likely to overpredict GPP response to climate change. High or realistic variability of model estimates relative to reference data indicate that the model is hypersensitive to one or more drivers., Key Points Regression logic is reason to doubt the accurate climate sensitivity of predictions whose variability is realistic or higherA suite of models poorly reproduces eddy covariance estimates of Amazon rainforest gross primary productivityHighly seasonal models predict stronger primary productivity responsiveness to meteorology than is likely to be true

The effects of peat burning on organic‐rich agricultural soils of the Mezzano Lowland (NE Italy) were evaluated on soil profiles variously affected by smoldering. Profiles were investigated for pH, electrical conductivity, bulk density, elemental and isotopic composition of distinct carbon (and nitrogen) fractions. The results suggest that the horizons affected by carbon loss lie at depths 10–70 cm, where the highest temperatures are developed. We suggest that the exothermal oxidation of methane (mediated by biological activity) plays a significant role in the triggering mechanism. In the interested soils we estimated a potential loss of Soil Organic Carbon of approximately 110 kg m −2 within the first meter, corresponding to 580 kg CO2 m −3. The released greenhouse gas is coupled with a loss of soil structure and nutrients. Moreover, the process plausibly triggers mobility of metals bound in organometallic complexes. All these consequences negatively affect the environment, the agricultural activities and possibly also health of the local people., Key Points Peat burning in the Mezzano Lowland is a process active since wetland reclamation and variously affects the soil structure and carbon contentThe main carbon loss occurs in the depth interval 10–70 cm, where temperature up to 750°C is recordedThe process induces negative effects to surrounding environment, agriculture activities and possibly also human health

We develop a generalized interpolation material point method (GIMPM) for the shallow shelf approximation (SSA) of ice flow. The GIMPM, which can be viewed as a particle version of the finite element method, is used here to solve the shallow shelf approximations of the momentum balance and ice thickness evolution equations. We introduce novel numerical schemes for particle splitting and integration at domain boundaries to accurately simulate the spreading of an ice shelf. The advantages of the proposed GIMPM‐SSA framework include efficient advection of history or internal state variables without diffusion errors, automated tracking of the ice front and grounding line at sub‐element scales, and a weak formulation based on well‐established conventions of the finite element method with minimal additional computational cost. We demonstrate the numerical accuracy and stability of the GIMPM using 1‐D and 2‐D benchmark examples. We also compare the accuracy of the GIMPM with the standard material point method (sMPM) and a reweighted form of the sMPM. We find that the grid‐crossing error is very severe for SSA simulations with the sMPM, whereas the GIMPM successfully mitigates this error. While the grid‐crossing error can be reasonably reduced in the sMPM by implementing a simple material point reweighting scheme, this approach it not as accurate as the GIMPM. Thus, we illustrate that the GIMPM‐SSA framework is viable for the simulation of ice sheet‐shelf evolution and enables boundary tracking and error‐free advection of history or state variables, such as ice thickness or damage., Key Points Our material point method for ice shelf flow enables error‐free advection of history variables, such as damage, and ice front trackingThe method can be readily implemented into existing finite element software (here, Elmer/Ice), and is suitable for large‐scale applicationThe method is verified against analytical solutions for steady‐state flow and front evolution, and tested on an idealized marine ice sheet

Elevated concentrations of atmospheric bromine are known to cause ozone depletion in the Arctic, which is most frequently observed during springtime. We implement a detailed description of bromine and chlorine chemistry within the WRF‐Chem 4.1.1 model, and two different descriptions of Arctic bromine activation: (1) heterogeneous chemistry on surface snow on sea ice, triggered by ozone deposition to snow (Toyota et al., 2011 https://doi.org/10.5194/acp-11-3949-2011), and (2) heterogeneous reactions on sea salt aerosols emitted through the sublimation of lofted blowing snow (Yang et al., 2008, https://doi.org/10.1029/2008gl034536). In both mechanisms, bromine activation is sustained by heterogeneous reactions on aerosols and surface snow. Simulations for spring 2012 covering the entire Arctic reproduce frequent and widespread ozone depletion events, and comparisons with observations of ozone show that these developments significantly improve model predictions during the Arctic spring. Simulations show that ozone depletion events can be initiated by both surface snow on sea ice, or by aerosols that originate from blowing snow. On a regional scale, in spring 2012, snow on sea ice dominates halogen activation and ozone depletion at the surface. During this period, blowing snow is a major source of Arctic sea salt aerosols but only triggers a few depletion events., Key Points Halogen activation and its role in Arctic surface ozone depletion events (ODEs) is modeled using WRF‐ChemTwo halogen activation mechanisms are implemented (1) surface snow and (2) blowing snowA spring 2012 case study indicates that both mechanisms can trigger near‐surface ODEs, but that surface snow dominates

Funder: U.K. China Research and Innovation Partnership Fund, Funder: National Centre for Atmospheric Science (NCAS); Id: http://dx.doi.org/10.13039/501100000662, Reconstructions of volcanic aerosol radiative forcing are required to understand past climate variability. Currently, reconstructions of pre‐20th century volcanic forcing are derived from sulfate concentrations measured in polar ice cores, mainly using a relationship between the average ice‐sheet sulfate deposition and stratospheric sulfate aerosol burden based on a single explosive eruption—the 1991 eruption of Mt. Pinatubo. Here we estimate volcanic radiative forcings and associated uncertainty ranges from ice‐core sulfate records of eight of the largest bipolar deposition signals in the last 2,500 years using statistical emulation of a perturbed parameter ensemble of aerosol‐climate model simulations of explosive eruptions. Extensive sampling of different combinations of eruption source parameters using the emulators reveals that a very wide range of eruptions in different seasons with different sulfur dioxide emissions, eruption latitudes, and emission altitudes produce ice‐sheet sulfate deposition consistent with ice‐core records. Consequently, we find a large range in the volcanic forcing that can be directly attributed to the unknown eruption source parameters. We estimate that the uncertainty in volcanic forcing caused by many plausible eruption realizations leads to uncertainties in the global mean surface cooling of around 1°C for the largest unidentified historical eruptions. Our emulators are available online (https://cemac.github.io/volcanic-forcing-deposition) where eruption realizations for given ice‐sheet sulfate depositions can be explored.

Global air pollution and climate change are major threats to planetary health. These threats are strongly linked through the short‐lived climate forcers (SLCFs); ozone (O3), aerosols, and methane (CH4). Understanding the impacts of ambitious SLCF mitigation in different source emission sectors on planetary health indicators can help prioritize international air pollution control strategies. A global Earth system model is applied to quantify the impacts of idealized 50% sustained reductions in year 2005 emissions in the eight largest global anthropogenic source sectors on the SLCFs and three indicators of planetary health: global mean surface air temperature change (∆GSAT), avoided PM2.5‐related premature mortalities and gross primary productivity (GPP). The model represents fully coupled atmospheric chemistry, aerosols, land ecosystems and climate, and includes dynamic CH4. Avoided global warming is modest, with largest impacts from 50% cuts in domestic (−0.085 K), agriculture (−0.034 K), and waste/landfill (−0.033 K). The 50% cuts in energy, domestic, and agriculture sector emissions offer the largest opportunities to mitigate global PM2.5‐related health risk at around 5%–7% each. Such small global impacts underline the challenges ahead in achieving the World Health Organization aspirational goal of a 2/3 reduction in the number of deaths from air pollution by 2030. Uncertainty due to natural climate variability in PM2.5 is an important underplayed dimension in global health risk assessment that can vastly exceed uncertainty due to the concentration‐response functions at the large regional scale. Globally, cuts to agriculture and domestic sector emissions are the most attractive targets to achieve climate and health co‐benefits through SLCF mitigation., Key Points Quantify impacts of deep short‐lived climate forcer (SLCF) emission mitigation by source sector on global temperature and human and ecosystem healthUncertainties due to year‐to‐year meteorological variability in PM2.5 is an important dimension in global human health risk assessmentAchieving planetary health benefits from SLCF mitigation requires ambitious mitigation pathways that tackle multiple source sectors

GNSS Reflectometry (GNSS‐R) measurements over inland water bodies, such as lakes, rivers, and wetlands exhibit strong coherent signals. The strength of the coherent reflections is highly sensitive to small‐scale surface roughness. For inland waters, this roughness is primarily due to wind‐driven surface waves. The sensitivity of the coherent reflections to surface roughness can be leveraged to estimate wave height profiles across inland waters. Coupled with a wind wave model, an approach to retrieve a wind vector is described using a forward model, which is potentially able to predict scattered power profiles for different wind speeds and directions and choosing the minimum‐squared error solution. The ability for spaceborne or airborne GNSS‐R to measure an inland water wind vector and wave heights could contribute to scientific applications focused on understanding nearshore ecosystems, monitoring climate change effects on inland waters, sediment resuspension, biomass production, fish habitat, and others. This paper presents a novel approach to potentially retrieve wind vector and wave heights over inland waters using GNSS‐R and discusses the issues with performing such retrievals using simulation and very few available raw signals recorded from CYGNSS satellites., Key Points Global Navigation Satellite Systems (GNSS) signals reflected from inland waters exhibit coherent scattering properties that make them highly sensitive to water surface roughnessWind‐induced roughness will vary across a water body, with a strong dependence on wind speed, wind direction, and the water depthDiscuss potentials of a novel model‐based approach for retrieval a of wind vector and surface wave heights over lakes using CYGNSS data

By injecting a mixture of gas and pyroclasts into the atmosphere, explosive volcanic eruptions frequently generate vortex rings, which are toroidal vortices formed by the jet's initial momentum. Here, we report high‐speed imaging and acoustic measurements of vortex rings sourcing from gas‐rich eruptive jets at Stromboli volcano (Italy). Volcanic vortex rings (VVRs) form at the vent together with an initial compression acoustic wave, VVRs maximum rise velocity being directly proportional to the amplitude and inversely proportional to the duration of the compression wave. The axial rise and acoustic signature of VVRs match well those predicted by recent fluid‐dynamic experiments. This good match allows using the high‐frequency (80–1,000 Hz) component of the jet sound and the time‐dependent rise of VVRs to retrieve two key eruption parameters: the Mach number of the eruptive jets (, Key Points Volcanic vortex rings are formed by gas‐rich, jet‐forming Strombolian‐style explosionsThe explosion acoustic signals bear the signature of the vortex rings and reveal supersonic eruptive jets with Mach number up to 1.5Vent diameter can be estimated from the time‐dependent rise of vortex rings at 0.7 m, in agreement with direct observation from Uncrewed Aerial Vehicle

We present and discuss the use of a high‐dimensional computational method for atmospheric inversions that incorporates the space‐time structure of transport and dispersion errors. In urban environments, transport and dispersion errors are largely the result of our inability to capture the true underlying transport of greenhouse gas (GHG) emissions to observational sites. Motivated by the impact of transport model error on estimates of fluxes of GHGs using in situ tower‐based mole‐fraction observations, we specifically address the need to characterize transport error structures in high‐resolution large‐scale inversion models. We do this using parametric covariance functions combined with shrinkage‐based regularization methods within an Ensemble Transform Kalman Filter inversion setup. We devise a synthetic data experiment to compare the impact of transport and dispersion error component of the model‐data mismatch covariance choices on flux retrievals and study the robustness of the method with respect to fewer observational constraints. We demonstrate the analysis in the context of inferring CO2 fluxes starting with a hypothesized prior in the Washington D.C. /Baltimore area constrained by a synthetic set of tower‐based CO2 measurements within an observing system simulation experiment framework. This study demonstrates the ability of these simple covariance structures to substantially improve the estimation of fluxes over standard covariance models in flux estimation from urban regions., Key Points Urban‐scale transport errors are correlated in space and time, which should be included in atmospheric inversions that estimate greenhouse gas emissionsIn a synthetic data study, multiple methods to characterize correlated errors with model‐data covariance matrices demonstrate a better performanceOverall, dynamically adaptive covariance structures perform better than standard parametric models in both recovering total fluxes and their spatial distributions