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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.

Burial driven recycling is an important process in the natural gas hydrate (GH) systems worldwide, characterized by complex multiphysics interactions like gas migration through an evolving gas hydrate stability zone (GHSZ), competing gas-water-hydrate (i.e., fluid-fluid-solid) phase transitions, locally appearing and disappearing phases, and evolving sediment properties (e.g., permeability, reaction surface area, and capillary entry pressure). Such a recycling process is typically studied in homogeneous or layered sediments. However, there is mounting evidence that structural heterogeneity and anisotropy linked to normal and inclined fault systems or anomalous sediment layers have a strong impact on the GH dynamics. Here, we consider the impacts of such a structurally complex media on the recycling process. To capture the properties of the anomalous layers accurately, we introduce a fully mass conservative, high-order, discontinuous Galerkin (DG) finite element based numerical scheme. Moreover, to handle the rapidly switching thermodynamic phase states robustly, we cast the problem of phase transitions as a set of variational inequalities, and combine our DG discretization scheme with a semi-smooth Newton solver. Here, we present our new simulator, and demonstrate using synthetic geological scenarios, (a) how the presence of an anomalous high-permeability layer, like a fracture or brecciated sediment, can alter the recycling process through flow-localization, and more importantly, (b) how an incorrect or incomplete approximation of the properties of such a layer can lead to large errors in the overall prediction of the recycling process. Key Points Structural heterogeneity linked to inclined fault systems or anomalous sediment layers have a strong impact on the gas hydrate dynamics The presence of anomalous high-permeability layers within gas hydrate stability zone alters the recycling process through flow-localization The presented discontinuous Galerkin scheme is able to accurately capture the gas hydrate recycling processes through strongly anisotropic materials

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

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

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

Satellite measurements from Terra's Moderate Resolution Imaging Spectroradiometer (MODIS) represent our longest, single‐platform, global record of the effective radius (Re) of the cloud drop size distribution. Quantifying its error characteristics has been challenging because systematic errors in retrieved Re covary with the structural characteristics of the cloud and the Sun‐view geometry. Recently, it has been shown that the bias in MODIS Re can be estimated by fusing MODIS data with data from Terra's Multi‐angle Imaging SpectroRadiometer (MISR). Here, we relate the bias to the observed underlying conditions to derive regional‐scale, bias‐corrected, monthly‐mean Re 1.6, Re 2.1, and Re 3.7 values retrieved from the 1.6, 2.1, and 3.7 μm MODIS spectral channels. Our results reveal that monthly‐mean bias in Re 2.1 exhibits large regional dependency, ranging from at least ~1 to 10 μm (15 to 60%) varying with scene heterogeneity, optical depth, and solar zenith angle. Regional bias‐corrected monthly‐mean Re 2.1 ranges from 4 to 17 μm, compared to 10 to 25 μm for uncorrected Re 2.1, with estimated uncertainties of 0.1 to 1.8 μm. The bias‐corrected monthly‐mean Re 3.7 and Re 2.1 show difference of approximately +0.6 μm in the coastal marine stratocumulus regions and down to approximately −2 μm in the cumuliform cloud regions, compared to uncorrected values of about −1 to −6 μm, respectively. Bias‐corrected Re values compare favorably to other independent data sources, including field observations, global model simulations, and satellite retrievals that do not use retrieval techniques similar to MODIS. This work changes the interpretation of global Re distributions from MODIS Re products and may further impact studies, which use the original MODIS Re products to study, for example, aerosol‐cloud interactions and cloud microphysical parameterization., Key Points Regional monthly mean MODIS Re is biased high by at least 1 to 10 μm and depends on cloud horizontal heterogeneity and Sun angleBias‐corrected values of Re compare favorably to other independent satellite retrievals, field observations, and global model simulationsSpectral channel differences in bias‐corrected Re is reduced but still suggests that vertical variability of Re is cloud‐type dependent

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

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%

Carbonate minerals have been detected in Jezero crater, an ancient lake basin that is the landing site of the Mars 2020 Perseverance rover, and within the regional olivine‐bearing (ROB) unit in the Nili Fossae region surrounding this crater. It has been suggested that some carbonates in the margin fractured unit, a rock unit within Jezero crater, formed in a fluviolacustrine environment, which would be conducive to preservation of biosignatures from paleolake‐inhabiting lifeforms. Here, we show that carbonate‐bearing rocks within and outside of Jezero crater have the same range of visible‐to‐near‐infrared carbonate absorption strengths, carbonate absorption band positions, thermal inertias, and morphologies. Thicknesses of exposed carbonate‐bearing rock cross‐sections in Jezero crater are ∼75–90 m thicker than typical ROB unit cross‐sections in the Nili Fossae region, but have similar thicknesses to ROB unit exposures in Libya Montes. These similarities in carbonate properties within and outside of Jezero crater is consistent with a shared origin for all of the carbonates in the Nili Fossae region. Carbonate absorption minima positions indicate that both Mg‐ and more Fe‐rich carbonates are present in the Nili Fossae region, consistent with the expected products of olivine carbonation. These estimated carbonate chemistries are similar to those in martian meteorites and the Comanche carbonates investigated by the Spirit rover in Columbia Hills. Our results indicate that hydrothermal alteration is the most likely formation mechanism for non‐deltaic carbonates within and outside of Jezero crater., Key Points Carbonates within and outside of Jezero crater have similar spectra, thermal inertias, morphologies, and thicknessesCarbonates within and outside of Jezero crater likely formed via the same processesHydrothermal alteration and evaporation are the most likely processes for carbonate formation within and outside of Jezero crater

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)

Global aviation dropped precipitously during the covid‐19 pandemic, providing an unprecedented opportunity to study aviation‐induced cirrus (AIC). AIC is believed to be responsible for over half of aviation‐related radiative forcing, but until now, its radiative impact has only been estimated from simulations. Here, we show that satellite observations of cirrus cloud do not exhibit a detectable global response to the dramatic aviation reductions of spring 2020. These results indicate that previous model‐based estimates may overestimate AIC. In addition, we find no significant response of diurnal surface air temperature range to the 2020 aviation changes, reinforcing the findings of previous studies. Though aviation influences the climate through multiple pathways, our analysis suggests that its warming effect from cirrus changes may be smaller than previously estimated., Key Points Aviation reductions during COVID‐19 provide an opportunity to test the impact of aviation on cirrus cloud and diurnal temperature rangeNeither variable exhibits a detectable large‐scale response in satellite observationsComparison with previous model analyses of contrail cirrus suggests that warming by aviation‐induced cirrus may have been overestimated

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

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

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

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

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

The ACT‐America project is a NASA Earth Venture Suborbital‐2 mission designed to study the transport and fluxes of greenhouse gases. The open and freely available ACT‐America data sets provide airborne in situ measurements of atmospheric carbon dioxide, methane, trace gases, aerosols, clouds, and meteorological properties, airborne remote sensing measurements of aerosol backscatter, atmospheric boundary layer height and columnar content of atmospheric carbon dioxide, tower‐based measurements, and modeled atmospheric mole fractions and regional carbon fluxes of greenhouse gases over the Central and Eastern United States. We conducted 121 research flights during five campaigns in four seasons during 2016–2019 over three regions of the US (Mid‐Atlantic, Midwest and South) using two NASA research aircraft (B‐200 and C‐130). We performed three flight patterns (fair weather, frontal crossings, and OCO‐2 underflights) and collected more than 1,140 h of airborne measurements via level‐leg flights in the atmospheric boundary layer, lower, and upper free troposphere and vertical profiles spanning these altitudes. We also merged various airborne in situ measurements onto a common standard sampling interval, which brings coherence to the data, creates geolocated data products, and makes it much easier for the users to perform holistic analysis of the ACT‐America data products. Here, we report on detailed information of data sets collected, the workflow for data sets including storage and processing of the quality controlled and quality assured harmonized observations, and their archival and formatting for users. Finally, we provide some important information on the dissemination of data products including metadata and highlights of applications of ACT‐America data sets., Key Points Atmospheric Carbon and Transport – America (ACT‐America) provides a unique, weather‐oriented collection of atmospheric CO2, CH4, trace gases, and meteorological properties measurementsACT‐America data are free and open to the public from the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC)ACT‐America data are uniquely suited to improve the accuracy and precision of regional inverse greenhouse gas (GHG) flux estimates

Losses from natural hazards are escalating dramatically, with more properties and critical infrastructure affected each year. Although the magnitude, intensity, and/or frequency of certain hazards has increased, development contributes to this unsustainable trend, as disasters emerge when natural disturbances meet vulnerable assets and populations. To diagnose development patterns leading to increased exposure in the conterminous United States (CONUS), we identified earthquake, flood, hurricane, tornado, and wildfire hazard hotspots, and overlaid them with land use information from the Historical Settlement Data Compilation data set. Our results show that 57% of structures (homes, schools, hospitals, office buildings, etc.) are located in hazard hotspots, which represent only a third of CONUS area, and ∼1.5 million buildings lie in hotspots for two or more hazards. These critical levels of exposure are the legacy of decades of sustained growth and point to our inability, lack of knowledge, or unwillingness to limit development in hazardous zones. Development in these areas is still growing more rapidly than the baseline rates for the nation, portending larger future losses even if the effects of climate change are not considered., Key Points More than half of the structures in the conterminous United States are exposed to potentially devastating natural hazardsGrowth rates in hazard hotspots exceed the national trendRisk assessments can be improved by considering multiple hazards, mitigation history and fine‐scale data on the built environment

Familiarity with the use of face coverings to reduce the risk of respiratory disease has increased during the coronavirus pandemic; however, recommendations for their use outside of the pandemic remains limited. Here, we develop a modeling framework to quantify the potential health benefits of wearing a face covering or respirator to mitigate exposure to particulate air pollution. This framework accounts for the wide range of available face coverings and respirators, fit factors and efficacy, air pollution characteristics, and exposure‐response data. Our modeling shows that N95 respirators offer robust protection against different sources of particulate matter, reducing exposure by more than a factor of 14 when worn with a leak rate of 5%. Synthetic‐fiber masks offer less protection with a strong dependence on aerosol size distribution (protection factors ranging from 4.4 to 2.2), while natural‐fiber and surgical masks offer reductions in the exposure of 1.9 and 1.7, respectively. To assess the ability of face coverings to provide population‐level health benefits to wildfire smoke, we perform a case study for the 2012 Washington state fire season. Our models suggest that although natural‐fiber masks offer minor reductions in respiratory hospitalizations attributable to smoke (2%–11%) due to limited filtration efficiency, N95 respirators and to a lesser extent surgical and synthetic‐fiber masks may lead to notable reductions in smoke‐attributable hospitalizations (22%–39%, 9%–24%, and 7%–18%, respectively). The filtration efficiency, bypass rate, and compliance rate (fraction of time and population wearing the device) are the key factors governing exposure reduction potential and health benefits during severe wildfire smoke events., Key Points We developed a framework to quantify potential health benefits of wearing a face mask or respirator during episodes of severe air pollutionN95 respirators offer protection against wildfire PM2.5, reducing exposure by more than a factor of 14 and hospitalizations by 22%–39%Natural‐fiber (e.g., cotton) masks offer only minor protection against wildfire PM2.5, reducing hospitalizations by only 2%–11%

The Tien Shan and Pamir mountains host over 28,000 glaciers providing essential water resources for increasing water demand in Central Asia. A disequilibrium between glaciers and climate affects meltwater release to Central Asian rivers, challenging the region's water availability. Previous research has neglected temporal variability. We present glacier mass balance estimates based on transient snowline and geodetic surveys with unprecedented spatiotemporal resolution from 1999/00 to 2017/18. Our results reveal spatiotemporal heterogeneity characterized by two mass balance clusters: (a) positive, low variability, and (b) negative, high variability. This translates into variable glacial meltwater release (≈1–16%) of annual river runoff for two watersheds. Our study reveals more complex climate forcing‐runoff responses and importance of glacial meltwater variability for the region than suggested previously., Key Points Annual glacier mass balance for Central Asia (1999/00–2017/18) is derived by combining transient snowlines, geodetic surveys, and modelingStrong spatiotemporal heterogeneity with contrasting patterns of mass gain and loss are foundHot spots of heterogeneous mass balance variability are associated with highly variable glacier melt water runoff

24 pages, 8 figures, supporting information https://doi.org/10.1029/2021JD034811.-- Data Availability Statement: The data set used in this study are available in (1) Antoine et al. (2019) available at https://doi.org/10.5281/zenodo.3406983; (2) Chen et al. (2019) available at https://doi.org/10.5281/zenodo.3559982; (3) Landwehr et al. (2020) available at https://doi.org/10.5281/zenodo.3836439; (4) Landwehr, Thurnherr et al. (2020) available at https://doi.org/10.5194/amt-13-3487-2020; (5) Schmale et al. (2019) available at https://doi.org/10.5281/zenodo.2636709; (6) Tatzelt et al. (2020) available at https://doi.org/10.5281/zenodo.3922147; (7) Thomalla et al., (2020) available at https://doi.org/10.5281/zenodo.3859515; (8) Thurnherr et al. (2020) available at https://doi.org/10.5281/zenodo.4031705; In addition, the fluorescent aerosol and gel–like POM measurements have been uploading as supplementary information supporting this article, In this study, we investigate the occurrence of primary biological aerosol particles (PBAP) over all sectors of the Southern Ocean (SO) based on a 90-day data set collected during the Antarctic Circumnavigation Expedition (ACE) in austral summer 2016–2017. Super-micrometer PBAP (1–16 µm diameter) were measured by a wide band integrated bioaerosol sensor (WIBS-4). Low (3σ) and high (9σ) fluorescence thresholds are used to obtain statistics on fluorescent and hyper-fluorescent PBAP, respectively. Our focus is on data obtained over the pristine ocean, that is, more than 200 km away from land. The results indicate that (hyper-)fluorescent PBAP are correlated to atmospheric variables associated with sea spray aerosol (SSA) particles (wind speed, total super-micrometer aerosol number concentration, chloride and sodium concentrations). This suggests that a main source of PBAP over the SO is SSA. The median percentage contribution of fluorescent and hyper-fluorescent PBAP to super-micrometer SSA was 1.6% and 0.13%, respectively. We demonstrate that the fraction of (hyper-)fluorescent PBAP to total super-micrometer particles positively correlates with concentrations of bacteria and several taxa of pythoplankton measured in seawater, indicating that marine biota concentrations modulate the PBAP source flux. We investigate the fluorescent properties of (hyper-)fluorescent PBAP for several events that occurred near land masses. We find that the fluorescence signal characteristics of particles near land is much more variable than over the pristine ocean. We conclude that the source and concentration of fluorescent PBAP over the open ocean is similar across all sampled sectors of the SO, The authors acknowledge funding for ACE-SPACE, ACE-SORPASSO by the Swiss Polar Institute, and Ferring Pharmaceuticals. ACE-SPACE was carried out with additional support from the European FP7 project BACCHUS (Grant Agreement 49603445). Gang Chen received funding from the cost action of Chemical On-Line cOmpoSition and Source Apportionment of fine aerosol (COLOSSAL, CA16109), a COST related project of the Swiss National Science Foundation, Source apportionment using long-term Aerosol Mass Spectrometry and Aethalometer Measurements (SAMSAM, IZCOZ0_177063). Sebastian Landwehr received funding from the Swiss Data Science Center project c17-02. Andrea Baccarin received funding from the Swiss National Science Foundation (Grant 200021_169090). Julia Schmale holds the Ingvar Kamprad Chair. Martin Gysel-Beer received funding from the ERC under Grant ERC-CoG-615922-BLACARAT. Charlotte Robinson received funding from the Australian Research Council (Grand ARC DP160103387), With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)