Search

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

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

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

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

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

This study examined the surface-wetness effects in calculating dust generation in source regions, using Asian dust aerosol model version 3 (ADAM3, the control run, CNTL). Model sensitivity experiment was conducted in such a way that the dust generation in CNTL is compared against three ADAM3 versions with various surface-wetness effect schemes. The dust-generation algorithm in ADAM_RAIN utilizes precipitation, while the scheme in ADAM3_SM1 and ADAM3_SM2 employs soil water content to account for the surface-wetness effects on dust generation. Each run was evaluated for the spring (March–May) of 2020. ADAM3_SM1 shows the best performance for the dust source region in East Asia based on the root-mean-square error and the skill score, followed by ADAM3_SM2 and ADAM3_RAIN. Particularly, incorporation of the surface-wetness effects improves dust generation mostly in wet cases rather than dry cases. The three surface-wetness-effect runs reduce dust generation in the source regions compared to CNTL, hence, the inclusion of surface-wetness effects improves dust generation in the regions where CNTL overestimates dust generation.

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

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

The Tibetan Plateau is regarded as the Earth's Third Pole, which is the source region of several major rivers that impact more 20% the world population. This high‐altitude region is reported to have been undergoing much greater rate of weather changes under global warming, but the existing reanalysis products are inadequate for depicting the state of the atmosphere, particularly with regard to the amount of precipitation and its diurnal cycle. An ensemble Kalman filter (EnKF) data assimilation system based on the limited‐area Weather Research and Forecasting (WRF) model was evaluated for use in developing a regional reanalysis over the Tibetan Plateau and the surrounding regions. A 3‐month prototype reanalysis over the summer months (June−August) of 2015 using WRF‐EnKF at a 30‐km grid spacing to assimilate nonradiance observations from the Global Telecommunications System was developed and evaluated against independent sounding and satellite observations in comparison to the ERA‐Interim and fifth European Centre for Medium‐Range Weather Forecasts Reanalysis (ERA5) global reanalysis. Results showed that both the posterior analysis and the subsequent 6‐ to 12‐hr WRF forecasts of the prototype regional reanalysis compared favorably with independent sounding observations, satellite‐based precipitation versus those from ERA‐Interim and ERA5 during the same period. In particular, the prototype regional reanalysis had clear advantages over the global reanalyses of ERA‐Interim and ERA5 in the analysis accuracy of atmospheric humidity, as well as in the subsequent downscale‐simulated precipitation intensity, spatial distribution, diurnal evolution, and extreme occurrence., Key Points A pilot reanalysis over the Tibetan Plateau using the PSU WRF‐EnKF system was developed and evaluated against independent soundings and satellite observations as well as ERA‐Interim and ERA5The pilot reanalysis had clear advantage in atmospheric humidity than existing global reanalysisThe subsequent downscaled precipitation forecasts from the pilot reanalysis compared favorably against those from the global reanalyses of ERA‐Interim and ERA5

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)

Responses to COVID‐19 have resulted in unintended reductions of city‐scale carbon dioxide (CO2) emissions. Here, we detect and estimate decreases in CO2 emissions in Los Angeles and Washington DC/Baltimore during March and April 2020. We present three lines of evidence using methods that have increasing model dependency, including an inverse model to estimate relative emissions changes in 2020 compared to 2018 and 2019. The March decrease (25%) in Washington DC/Baltimore is largely supported by a drop in natural gas consumption associated with a warm spring whereas the decrease in April (33%) correlates with changes in gasoline fuel sales. In contrast, only a fraction of the March (17%) and April (34%) reduction in Los Angeles is explained by traffic declines. Methods and measurements used herein highlight the advantages of atmospheric CO2 observations for providing timely insights into rapidly changing emissions patterns that can empower cities to course‐correct CO2 reduction activities efficiently., Key Points Atmospheric CO2 observations can be used to detect the onset of the COVID‐19 response in Los Angeles and Washington DC/BaltimoreRelative reductions in April 2020 associated with COVID‐19 are ∼30% when compared to emissions in 2018 and 2019Decreases in vehicular traffic do not completely explain observed emissions reductions in both Los Angeles and Washington DC/Baltimore

Responding to the 2020 COVID‐19 outbreak, China imposed an unprecedented lockdown producing reductions in air pollutant emissions. However, the lockdown driven air pollution changes have not been fully quantified. We applied machine learning to quantify the effects of meteorology on surface air quality data in 31 major Chinese cities. The meteorologically normalized NO2, O3, and PM2.5 concentrations changed by −29.5%, +31.2%, and −7.0%, respectively, after the lockdown began. However, part of this effect was also associated with emission changes due to the Chinese Spring Festival, which led to ∼14.1% decrease in NO2, ∼6.6% increase in O3 and a mixed effect on PM2.5 in the studied cities that largely resulted from festival associated fireworks. After decoupling the weather and Spring Festival effects, changes in air quality attributable to the lockdown were much smaller: −15.4%, +24.6%, and −9.7% for NO2, O3, and PM2.5, respectively., Key Points The Chinese Spring Festival led to a 14.1% decrease in NO2, 6.6% increase in O3 and a mixed effect on PM2.5 across China in 2015–2019The 2020 lockdown resulted in −15.4%, −17.0%, −14.5%, −7.6%, −9.7%, and +24.6% changes for NO2, SO2, CO, PM10, PM2.5, and O3, respectivelyFireworks emissions contributed to haze in many cities such as an additional 29 μg m−3 PM2.5 in Beijing on the first lunar day of 2020

The Mesozoic era (∼252 to 66 million years ago) was a key interval in Earth's evolution toward its modern state, witnessing the breakup of the supercontinent Pangaea and significant biotic innovations like the early evolution of mammals. Plate tectonic dynamics drove a fundamental climatic transition from the early Mesozoic supercontinent toward the Late Cretaceous fragmented continental configuration. Here, key aspects of Mesozoic long‐term environmental changes are assessed in a climate model ensemble framework. We analyze so far the most extended ensemble of equilibrium climate states simulated for evolving Mesozoic boundary conditions covering the period from 255 to 60 Ma in 5 Myr timesteps. Global mean temperatures are generally found to be elevated above the present and exhibit a baseline warming trend driven by rising sea levels and increasing solar luminosity. Warm (Triassic and mid‐Cretaceous) and cool (Jurassic and end‐Cretaceous) anomalies result from pCO2 changes indicated by different reconstructions. Seasonal and zonal temperature contrasts as well as continental aridity show an overall decrease from the Late Triassic‐Early Jurassic to the Late Cretaceous. Meridional temperature gradients are reduced at higher global temperatures and less land area in the high latitudes. With systematic sensitivity experiments, the influence of paleogeography, sea level, vegetation patterns, pCO2, solar luminosity, and orbital configuration on these trends is investigated. For example, long‐term seasonality trends are driven by paleogeography, but orbital cycles could have had similar‐scale effects on shorter timescales. Global mean temperatures, continental humidity, and meridional temperature gradients are, however, also strongly affected by pCO2., Key Points We assess global long‐term climate trends through the Mesozoic era with an ensemble of climate model simulationsVarying carbon dioxide levels cause anomalies around an overall warming trend due to changing paleogeography and increasing insolationSeasonal and zonal temperature contrasts as well as aridity decrease with time, while meridional gradients vary with paleogeography

Understanding the complex interrelationships between wildfire and its environmental and anthropogenic controls is crucial for wildfire modeling and management. Although machine learning (ML) models have yielded significant improvements in wildfire predictions, their limited interpretability has been an obstacle for their use in advancing understanding of wildfires. This study builds an ML model incorporating predictors of local meteorology, land‐surface characteristics, and socioeconomic variables to predict monthly burned area at grid cells of 0.25° × 0.25° resolution over the contiguous United States. Besides these predictors, we construct and include predictors representing the large‐scale circulation patterns conducive to wildfires, which largely improves the temporal correlations in several regions by 14%–44%. The Shapley additive explanation is introduced to quantify the contributions of the predictors to burned area. Results show a key role of longitude and latitude in delineating fire regimes with different temporal patterns of burned area. The model captures the physical relationship between burned area and vapor pressure deficit, relative humidity (RH), and energy release component (ERC), in agreement with the prior findings. Aggregating the contribution of predictor variables of all the grids by region, analyses show that ERC is the major contributor accounting for 14%–27% to large burned areas in the western US. In contrast, there is no leading factor contributing to large burned areas in the eastern US, although large‐scale circulation patterns featuring less active upper‐level ridge‐trough and low RH two months earlier in winter contribute relatively more to large burned areas in spring in the southeastern US., Key Points US fire burned areas are well predicted by a machine learning model and SHAP improves interpretation of the contributing factorsIncluding large‐scale circulation patterns conducive to wildfires as predictors improve prediction of burned areas in several regionsFire‐season fuel dryness and dry winters are important contributors to large burned areas in western and southeastern US, respectively

Long-term sustainable water resources management requires comprehensive assessment of all water cycle components. The combined calibration of surface water (SW) and groundwater (GW) parameters is one of the biggest challenges in simulating SW-GW interactions. SWAT-MODFLOW has improved our ability to simulate SW and GW interactions, however there remain challenges associated with (i) understanding what parameters most influence model outputs and (ii) properly calibrating the GW related components of the water cycle (as represented within SWAT-MODFLOW). In this paper, we applied SWAT-MODFLOW to the 1452 km2 Shiraz catchment, located in southwestern Iran, for the period 2003–2019. A comprehensive sensitivity analysis (SA) of both SW and GW parameters was conducted to determine what controls runoff and GW level in the study catchment. The SA reveals that the Curve Number (CN2) and GW hydraulic conductivity (K) parameters have the most significant impact on runoff and GW level simulation. This work also presents a “GW Calibrator” (GWCal) tool along with a demonstration of how GWCal improves the performance of SWAT-MODFLOW in the Shiraz catchment as a result of the insights gained from the SA.

Coastal boulder deposits (CBD) provide what are sometimes the only remaining signatures of wave inundation on rocky coastlines; in recent decades, CBD combined with initiation of motion (IoM) analyses have repeatedly been used as primary evidence to infer the existence of ancient tsunamis. However, IoM storm wave heights inferred by these studies have been shown to be highly inaccurate, bringing some inferences into question. This work develops a dimensionless framework to relate CBD properties with storm‐wave hindcasts and measurements, producing data‐driven relations between wave climate and boulder properties. We present an elevation‐density‐size‐inland distance‐wave height analysis for individual storm‐transported boulders which delineates the dynamic space where storm‐wave CBD occur. Testing these new relations against presumed tsunami CBD demonstrates that some fall well within the capabilities of storm events, suggesting that some previous studies might be fruitfully reexamined within the context of this new framework., Key Points Size, elevation, and distance inland in storm‐wave‐generated coastal boulder deposits are all functions of climatological wave heightsClear data ranges exist where storm wave coastal boulder deposits do/do not existSome coastal boulder deposits previously identified as having tsunami origin may have been generated by storm waves

As of July 27, 2020, COVID‐19 has caused 640,000 deaths worldwide and has had a major impact on people's productivity and lives. Analyzing the spatial distribution characteristics of COVID‐19 cases and their relationships with meteorological and environmental factors might help enrich our knowledge of virus transmission and formulate reasonable epidemic prevention strategies. Taking the cumulative confirmed cases in Hubei province from January 23, 2020, to April 8, 2020, as an example, this study analyzed the spatial evolution characteristics of confirmed COVID‐19 cases in Hubei province using exploratory spatial data analysis and explored the spatial relationship between the main environmental and meteorological factors and confirmed COVID‐19 cases using a geographically weighted regression (GWR) model. Results show that there was no obvious spatial clustering of confirmed COVID‐19 cases in Hubei province, while the decline and end of the newly confirmed cases revealed relatively obvious negative spatial correlations. Due to the lockdown in Hubei province, the main air quality indexes (e.g., AQI and PM2.5) decreased significantly and environmental quality was better than historical contemporaneous levels. Meanwhile, the results of the GWR model suggest that the impacts of environmental and meteorological factors on the development of COVID‐19 were not significant. These findings indicate that measures such as social distancing and isolation played the primary role in controlling the development of the COVID‐19 epidemic., Key Points The spatiotemporal patterns of COVID‐19 cases in Hubei Province were analyzedAnomalies of environmental and meteorological factors contributed little to COVID‐19 developmentSocial distancing and isolation played a key role in controlling the epidemic

The ongoing coronavirus disease 2019 (COVID‐19) pandemic has caused more than 150 million cases of infection to date and poses a serious threat to global public health. In this study, global COVID‐19 data were used to examine the dynamical variations from the perspectives of immunity and contact of 84 countries across the five climate regions: tropical, arid, temperate, and cold. A new approach named Yi Hua Jie Mu is proposed to obtain the transmission rates based on the COVID‐19 data between the countries with the same climate region over the Northern Hemisphere and Southern Hemisphere. Our results suggest that the COVID‐19 pandemic will persist over a long period of time or enter into regular circulation in multiple periods of 1–2 years. Moreover, based on the simulated results by the COVID‐19 data, it is found that the temperate and cold climate regions have higher infection rates than the tropical and arid climate regions, which indicates that climate may modulate the transmission of COVID‐19. The role of the climate on the COVID‐19 variations should be concluded with more data and more cautions. The non‐pharmaceutical interventions still play the key role in controlling and prevention this global pandemic., Key Points A new approached is proposed to predict the future COVID‐19 variations rather than relying on information on other corona virusesCOVID‐19 pandemic will persist in multiple periods of 1–2 yearsThe temperate and cold climate regions have higher infection rates than the tropical and arid climate regions

The ongoing Coronavirus Disease 2019 (COVID‐19) has posed a serious threat to human public health and global economy. Population mobility is an important factor that drives the spread of COVID‐19. This study aimed to quantitatively evaluate the impact of population flow on the spread of COVID‐19 from a spatiotemporal perspective. To this end, a case study was carried out in Hubei Province, which was once the most affected area of COVID‐19 outbreak in Mainland China. The geographically and temporally weighted regression (GTWR) model was applied to model the spatiotemporal association between COVID‐19 epidemic and population mobility. Two patterns of population flows, including the population inflow from Wuhan and intra‐city population movement, were considered to construct explanatory variables. Results indicate that the GTWR model can reveal the spatial–temporal‐varying relationships between COVID‐19 and population mobility. Moreover, the association between COVID‐19 case counts and population movements presented three stages of temporal variation characteristics due to the virus incubation period and implementation of strict lockdown measures. In the spatial dimension, evident geographical disparities were observed across Hubei Province. These findings can provide policymakers useful knowledge about the impact of population movement on the spatio‐temporal transmission of COVID‐19. Thus, targeted interventions, if necessary in certain time periods, can be implemented to restrict population flow in cities with high transmission risk., Key Points To quantitatively evaluate how the effect of population flows on COVID‐19 spread varies over time and space in Hubei Province, ChinaThe association showed three stages of temporal variation characteristics and strong geographical disparities across Hubei ProvinceThe implementation of control measures by restricting population movements can play a key role in mitigating the spread of this epidemic

Six conceptually different transport models were applied to the macrodispersion experiment (MADE)-1 field tracer experiment as a first major attempt for model comparison. The objective was to show that complex mass distributions in heterogeneous aquifers can be predicted without calibration of transport parameters, solely making use of structural and flow data. The models differ in their conceptualization of the heterogeneous aquifer structure, computational complexity, and use of conductivity data obtained from various observation methods (direct push injection logging, DPIL, grain size analysis, pumping tests and flowmeter). They share the same underlying physical transport process of advection by the velocity field solely. Predictive capability is assessed by comparing results to observed longitudinal mass distributions of the MADE-1 experiment. The decreasing mass recovery of the observed plume is attributed to sampling and no physical process like mass transfer is invoked by the models. Measures like peak location and strength are used in comparing the modeled and measured plume mass distribution. Comparison of models reveals that the predictions of the solute plume agree reasonably well with observations, if the models are underlain by a few parameters of close values: mean velocity, a parameter reflecting log-conductivity variability, and a horizontal length scale related to conductivity spatial correlation. The robustness of the results implies that conservative transport models with appropriate conductivity upscaling strategies of various observation data provide reasonable predictions of plumes longitudinal mass distribution, as long as key features are taken into account., The authors thank Marco Bianchi and Boris Baeumer for their support during the development of the study. A. Fiori and A. Bellin acknowledge funding from the Italian Ministry of Education, University and Research (MIUR) in the frame of the Departments of Excellence Initiative 2018–2022 granted to Dept. of Engineering of Roma Tre University, and to the Dept. of Civil, Environmental and Mechanical Engineering of the University of Trento, respectively. M. Dentz acknowledges funding of the European Research Council (ERC) through the project MHetScale (contract number 617511), and the Spanish Research Agency (AEI) through the project HydroPore (contract number PID2019-106887GB-C31).

Improved understanding of how our coasts will evolve over a range of time scales (years‐decades) is critical for effective and sustainable management of coastal infrastructure. A robust knowledge of the spatial, directional and temporal variability of the inshore wave climate is required to predict future coastal evolution and hence vulnerability. However, the variability of the inshore directional wave climate has received little attention, and an improved understanding could drive development of skillful seasonal or decadal forecasts of coastal response. We examine inshore wave climate at 63 locations throughout the United Kingdom and Ireland (1980–2017) and show that 73% are directionally bimodal. We find that winter‐averaged expressions of six leading atmospheric indices are strongly correlated (r = 0.60–0.87) with both total and directional winter wave power (peak spectral wave direction) at all studied sites. Regional inshore wave climate classification through hierarchical cluster analysis and stepwise multi‐linear regression of directional wave correlations with atmospheric indices defined four spatially coherent regions. We show that combinations of indices have significant skill in predicting directional wave climates (R 2 = 0.45–0.8; p, Key Points Over 70% of inshore wave climates analyzed throughout the United Kingdom and Ireland were directionally bimodalCombinations of winter atmospheric indices NAO, WEPA, SCAND, and EA are significantly correlated with directional wave climate in all regionsRegression models using multiple winter atmospheric indices enable skillful reconstructions of directional wave climate in all regions

Many nations responded to the corona virus disease‐2019 (COVID‐19) pandemic by restricting travel and other activities during 2020, resulting in temporarily reduced emissions of CO2, other greenhouse gases and ozone and aerosol precursors. We present the initial results from a coordinated Intercomparison, CovidMIP, of Earth system model simulations which assess the impact on climate of these emissions reductions. 12 models performed multiple initial‐condition ensembles to produce over 300 simulations spanning both initial condition and model structural uncertainty. We find model consensus on reduced aerosol amounts (particularly over southern and eastern Asia) and associated increases in surface shortwave radiation levels. However, any impact on near‐surface temperature or rainfall during 2020–2024 is extremely small and is not detectable in this initial analysis. Regional analyses on a finer scale, and closer attention to extremes (especially linked to changes in atmospheric composition and air quality) are required to test the impact of COVID‐19‐related emission reductions on near‐term climate., Key Points Lockdown restrictions during COVID‐19 have reduced emissions of aerosols and greenhouse gases12 CMIP6 Earth system models have performed coordinated experiments to assess the impact of this on climateAerosol amounts are reduced over southern and eastern Asia but there is no detectable change in annually averaged temperature or precipitation

Geoenergy and geoengineering applications usually involve fluid injection into and production from fractured media. Accounting for fractures is important because of the strong poromechanical coupling that ties pore pressure changes and deformation. A possible approach to the problem uses equivalent porous media to reduce the computational cost and model complexity instead of explicitly including fractures in the models. We investigate the validity of this simplification by comparing these two approaches. Simulation results show that pore pressure distribution significantly differs between the two approaches even when both are calibrated to predict identical values at the injection and production wells. Additionally, changes in fracture stability are not well captured with the equivalent porous medium. We conclude that explicitly accounting for fractures in numerical models may be necessary under some circumstances to perform reliable coupled thermohydromechanical simulations, which could be used in conjunction with other tools for induced seismicity forecasting., A.Z. acknowledges the financial support received from the “Iran's Ministry of Science, Research and Technology” (PhD students' sabbatical grants) for visiting IDAEA‐CSIC. The contribution of F.P. is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—project number PA 3451/1‐1. R.M. is thankful for the support from US DOE through Carbon SAFE Macon County Project DE‐FE0029381. V.V. acknowledges funding from the Spanish National Research Council (CSIC) through the Intramural project 201730I100 and from the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Program through the Starting Grant GEoREST (www.georest.eu) (Grant agreement No. 801809). IDAEA‐CSIC is a Center of Excellence Severo Ochoa (Spanish Ministry of Science and Innovation, Project CEX2018‐000794‐S). The authors declare no conflict of interest.

Due to the specific hydrothermal conditions of dry‐hot valleys, temperature changes caused by the development of large‐scale hydropower projects may be more extreme than they are in other regions. In this study, we analyzed these temperature changes at four hydropower stations in both dry‐hot and non‐dry‐hot valleys. Based on the calculated relative temperatures of the downstream river and the areas surrounding the reservoirs, we employed two indices to quantify the influence of the reservoirs on the temperatures of these two regions: the downstream river temperature change and the reservoir effect change intensity. Our results are as follows: (a) In the downstream rivers, the temperature regulation effect was more pronounced in the wet season; in the regions surrounding the reservoirs, the temperature regulation effect was more pronounced in the dry season. (b) The downstream river temperature in both the dry‐hot and wet‐hot valleys exhibited noticeable warming in both the wet and dry seasons, while the cold‐dry valley was characterized by cooling in the dry season and warming in the wet season. With the exception of the Liyuan station (where the influence of the reservoir on the downstream temperatures only extended to a distance of 9 km from the dam) during the dry season, the existence of the hydropower stations affected the temperatures of the entire downstream region. (c) For the areas surrounding the reservoir, the presence of a hydropower station mainly caused the temperatures in the dry‐hot valleys to rise and the temperatures in the non‐dry‐hot valleys to decrease., Key Points The difference of surface temperature affected by reservoirs was compared in dry‐hot and non‐dry‐hot valleysTo facilitate comparison of the different downstream river temperature changes, downstream river temperature change index was establishedThe reservoir effect change intensity was established to explore how reservoirs regulate the surface temperature in the surrounding areas

Micromagnetic tomography aims at reconstructing large numbers of individual magnetizations of magnetic particles from combining high‐resolution magnetic scanning techniques with micro X‐ray computed tomography (microCT). Previous work demonstrated that dipole moments can be robustly inferred, and mathematical analysis showed that the potential field of each particle is uniquely determined. Here, we describe a mathematical procedure to recover higher orders of the magnetic potential of the individual magnetic particles in terms of their spherical harmonic expansions (SHE). We test this approach on data from scanning superconducting quantum interference device microscopy and microCT of a reference sample. For particles with high signal‐to‐noise ratio of the magnetic scan we demonstrate that SHE up to order n = 3 can be robustly recovered. This additional level of detail restricts the possible internal magnetization structures of the particles and provides valuable rock magnetic information with respect to their stability and reliability as paleomagnetic remanence carriers. Micromagnetic tomography therefore enables a new approach for detailed rock magnetic studies on large ensembles of individual particles., Key Points Micromagnetic Tomography uniquely recovers higher‐order multipole terms for several individual grains in a sampleHigher order multipole moments are an expression of the internal domain structure of magnetic grainsUltimately, this enables to select individual grains for rock‐ and paleomagnetic studies based on domain configuration

Intensive observations and WRF‐Chem simulations are applied in this study to investigate the adverse impacts of regional transport on the PM2.5 (fine particulate matter; diameter ≤2.5 μm) changes in Shanghai during the Coronavirus Disease 2019 lockdown. As the local atmospheric oxidation capacity was observed to be generally weakened, strong regional transport carried by the frequent westerly winds is suggested to be the main driver of the unexpected pollution episodes, increasing the input of both primary and secondary aerosols. Contributing 40%–80% to the PM2.5, the transport contributed aerosols are simulated to exhibit less decreases (13.2%–21.8%) than the local particles (37.1%–64.8%) in urban Shanghai due to the lockdown, which largely results from the less decreased industrial and residential emissions in surrounding provinces. To reduce the influence of the transport, synergetic emission control, especially synergetic ammonia control, measures are proved to be effective strategies, which need to be considered in future regulations., Key Points PM2.5 pollution in Shanghai were mostly attributed to the regional transport carried by frequent westerly winds during the COVID‐lockdownThe transport contributed aerosols in Shanghai are less affected by the lockdown measures due to the less decreased surrounding emissionsSynergetic emission control measures, especially those on ammonia emissions, are effective in mitigating the adverse transport impacts

Although uncommon, nontuberculous mycobacterial (NTM) pulmonary infection in the Hawaiian Islands has a relatively high incidence and mortality compared to the mainland U.S. As a result, this study examines the possible geological and hydrological pathways by which NTM patients may become infected, including the environmental conditions that may favor growth and transport. Previously suggested infection routes include the inhalation of NTM attached to micro‐droplets from infected home plumbing systems and aerosolized dust from garden soil. In this study, we evaluate the possible routes NTM may take from riparian environments, into groundwater, into public water supplies and then into homes. Because NTM are notoriously hydrophobic and prone to attach to surfaces, mineralogy, and surface chemistry of suspended sediment in streams, soils, and rock scrapings suggest that NTM may especially attach to Fe‐oxides/hydroxides, and be transported as particles from losing streams to the aquifer on time‐scales of minutes to days. Within the aquifer, flow models indicate that water may be drawn into production wells on time scales (months) that permit NTM to survive and enter domestic water supplies. These processes depend on the presence of interconnected fracture networks with sufficient aperture to preclude complete autofiltration. The common occurrence of NTM in and around streams, in addition to wells, implies that the natural and built environments are capable of introducing a source of NTM into domestic water supplies via groundwater withdrawals. This may produce a persistent source of NTM infection to individuals through the presence of NTM‐laden biofilms in home plumbing., Key Points Nontuberculous mycobacterial (NTM) are found in soils and biofilms of riparian environments in Hawai'iNTM are likely transported from losing streams to aquifersPumped wells draw NTM into culinary water supplies and into homes. NTM are chlorine resistant and outcompete other taxa in home plumbing

In a rapidly changing world, what is today an unprecedented extreme may soon become the norm. As a result, extreme‐related disasters are expected to become more frequent and intense. This will have widespread socio‐economic consequences and affect the ability of different societal groups to recover from and adapt to rapidly changing environmental conditions. Therefore, there is the need to decipher the relation between genesis of unprecedented events, accumulation and distribution of risk, and recovery trajectories across different societal groups. Here, we develop an analytical approach to unravel the complexity of future extremes and multiscalar societal responses—from households to national governments and from immediate impacts to longer term recovery. This requires creating new forms of knowledge that integrate analyses of the past—that is, structural causes and political processes of risk accumulation and differentiated recovery trajectories—with plausible scenarios of future environmental extremes grounded in the event‐specific literature. We specifically seek to combine the physical characteristics of the extremes with examinations of how culture, politics, power, and policy visions shape societal responses to unprecedented events, and interpret the events as social‐environmental extremes. This new approach, at the nexus between social and natural sciences, has the concrete advantage of providing an impact‐focused vision of future social‐environmental risks, beyond what is achievable within conventional disciplinary boundaries. In this paper, we focus on extreme flooding events and the societal responses they elicit. However, our approach is flexible and applicable to a wide range of extreme events. We see it as the first building block of a new field of research, allowing for novel and integrated theoretical explanations and forecasting of social‐environmental extremes., Key Points We conceptualize unprecedented extremes as social‐environmental processes shaped by institutional, political, and economic changeAs social‐environmental extremes become more frequent, there is an urgency to unravel their genesis and the possible societal responsesThis approach is the first building block of a new field of research in social‐environmental extreme event forecasts

A growing constellation of satellites is providing near‐global coverage of column‐averaged CO2 observations. Launched in 2019, NASA’s OCO‐3 instrument is set to provide XCO2 observations at a high spatial and temporal resolution for regional domains (100 × 100 km). The atmospheric column version of the Stochastic Time‐Inverted Lagrangian Transport (X‐STILT) model is an established method of determining the influence of upwind sources on column measurements of the atmosphere, providing a means of analysis for current OCO‐3 observations and future space‐based column‐observing missions. However, OCO‐3 is expected to provide hundreds of soundings per targeted observation, straining this already computationally intensive technique. This work proposes a novel scheme to be used with the X‐STILT model to generate upwind influence footprints with less computational expense. The method uses X‐STILT generated influence footprints from a key subset of OCO‐3 soundings. A nonlinear weighted averaging is applied to these footprints to construct additional footprints for the remaining soundings. The effects of subset selection, meteorological data, and topography are investigated for two test sites: Los Angeles, California, and Salt Lake City, Utah. The computational time required to model the source sensitivities for OCO‐3 interpretation was reduced by 62% and 78% with errors smaller than other previously acknowledged uncertainties in the modeling system (OCO‐3 retrieval error, atmospheric transport error, prior emissions error, etc.). Limitations and future applications for future CO2 missions are also discussed., Key Points Determining sources of spatially dense XCO2 observations with LPDM techniques can become time intensive and strain computational resourcesPresented in this work is an interpolation scheme that eases the computational burden of spatially dense XCO2 source determination studiesEvaluating the efficiency of this interpolation scheme revealed reductions of >50% in computational time at two testing locations

The intensification of heat stress reduces the labor capacity and hence poses a threat to socio‐economic development. The reliable projection of the changing climate and the development of sound adaptation strategies are thus desired for adapting to the decreasing labor productivity under climate change. In this study, an optimization modeling approach coupled with dynamical downscaling is proposed to design the optimal adaptation strategies for improving labor productivity under heat stress in China. The future changes in heat stress represented by the wet‐bulb globe temperature (WBGT) are projected with a spatial resolution of 25 × 25 km by a regional climate model (RCM) through the dynamical downscaling of its driving global climate model (GCM). Uncertain information such as system costs, environmental costs, and subsidies are also incorporated into the optimization process to provide reliable decision alternatives for improving labor productivity. Results indicate that the intensification of WBGT is overestimated by the GCM compared to the RCM. Such an overestimation can lead to more losses in working hours derived from the GCM than those from the RCM regardless of climate scenarios. Nevertheless, the overestimated heat stress does not alter the regional measures taken to adapt to decreasing labor productivity. Compared to inland regions, the monsoon‐affected regions tend to improve labor productivity by applying air conditioning rather than working overtime due to the cost differences. Consequently, decision‐makers need to optimally make a balance between working overtime and air conditioning measures to meet sustainable development goals., Key Points An optimization model coupled with dynamical downscaling is developed to improve labor productivity under intensified heat stress in ChinaCivilian workers in inland regions will be more vulnerable to the intensified heat stress in a changing climateCompared to the regional climate model, the global climate model overestimates heat stress and thus exaggerates related system costs to recover from the working hour loss

Supraglacial debris affects glacier mass balance as a thin layer enhances surface melting, while a thick layer reduces it. While many glaciers are debris‐covered, global glacier models do not account for debris because its thickness is unknown. We provide the first globally distributed debris thickness estimates using a novel approach combining sub‐debris melt and surface temperature inversion methods. Results are evaluated against observations from 22 glaciers. We find the median global debris thickness is ∼0.15 ± 0.06 m. In all regions, the net effect of accounting for debris is a reduction in sub‐debris melt, on average, by 37%, which can impact regional mass balance by up to 0.40 m water equivalent (w.e.) yr‐1. We also find recent observations of similar thinning rates over debris‐covered and clean ice glacier tongues is primarily due to differences in ice dynamics. Our results demonstrate the importance of accounting for debris in glacier modeling efforts., Key Points We produce the first distributed global debris thickness estimatesAccounting for debris significantly reduces regional glacier mass lossThe similar thinning rates of debris‐covered and clean ice glaciers in High Mountain Asia is primarily caused by differences in ice dynamics

The decline in global emissions of carbon dioxide due to the COVID‐19 pandemic provides a unique opportunity to investigate the sensitivity of the global carbon cycle and climate system to emissions reductions. Recent efforts to study the response to these emissions declines has not addressed their impact on the ocean, yet ocean carbon absorption is particularly susceptible to changing atmospheric carbon concentrations. Here, we use ensembles of simulations conducted with an Earth system model to explore the potential detection of COVID‐related emissions reductions in the partial pressure difference in carbon dioxide between the surface ocean and overlying atmosphere (ΔpCO2), a quantity that is regularly measured. We find a unique fingerprint in global‐scale ΔpCO2 that is attributable to COVID, though the fingerprint is difficult to detect in individual model realizations unless we force the model with a scenario that has four times the observed emissions reduction., Key Points COVID‐related emissions reductions will be imperceptible in surface ocean pH observationsThe CanESM5 COVID ensemble predicts a unique fingerprint of COVID‐related emissions reductions in global mean ΔpCO2 (pCO2oc ‐ pCO2atm)The fingerprint is potentially detectable in global‐scale observations of ΔpCO2, but only with large emissions reductions