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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

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

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

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

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

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

Ocean evaporative fluxes are a critical component of the Earth's energy and water cycle, but their estimation remains uncertain. Near‐surface humidity is a required input to bulk flux algorithms that relate mean surface values to the turbulent fluxes. Several satellite‐derived turbulent flux products have been developed over the last decade that utilize passive microwave imager observations to estimate the surface humidity. It is known, however, that these estimates tend to diverge from one another and from in situ observations. Analysis of current state‐of‐the‐art satellite estimates provided herein reveals that regional‐scale biases in these products remain significant. Investigations reveal a link between the spatial coherency of the observed biases to atmospheric dynamical controls of water vapor vertical stratification, cloud liquid water, and sea surface temperature. This information is used to develop a simple state‐dependent bias correction that results in more consistent ocean surface humidity estimates. A principal conclusion is that further improvements to ocean near‐surface humidity estimation using microwave radiometers requires incorporation of prior information on water vapor stratification and sea surface temperature., Key Points Current remote sensing estimates of ocean surface humidity exhibit large‐scale and spatially coherent biasesThese biases are organized at the regional scale by atmospheric dynamics that control the vertical structure of water vapor and cloud amountAnalyses show that state‐dependent bias corrections can be developed to result in more consistent ocean surface humidity estimates

The northern part of India, adjoining the Himalaya, is considered as one of the global hot spots of pollution because of various natural and anthropogenic factors. Throughout the year, the region is affected by pollution from various sources like dust, biomass burning, industrial and vehicular pollution, and myriad other anthropogenic emissions. These sources affect the air quality and health of millions of people who live in the Indo‐Gangetic Plains. The dust storms that occur during the premonsoon months of March–June every year are one of the principal sources of pollution and originate from the source region of Arabian Peninsula and the Thar desert located in north‐western India. In the year 2018, month of May, three back‐to‐back major dust storms occurred that caused massive damage, loss of human lives, and loss to property and had an impact on air quality and human health. In this paper, we combine observations from ground stations, satellites, and radiosonde networks to assess the impact of dust events in the month of May 2018, on meteorological parameters, aerosol properties, and air quality. We observed widespread changes associated with aerosol loadings, humidity, and vertical advection patterns with displacements of major trace and greenhouse gasses. We also notice drastic changes in suspended particulate matter concentrations, all of which can have significant ramifications in terms of human health and changes in weather pattern., Key Points Intense uplift phases were observed associated with displacement of trace and greenhouse gassesIncreased aerosol loading was associated with changes in aerosol volume size distributionsIncreased surface ozone was observed in areas under the direct influence of dust

Earth observations offer potential pathways for accurately closing the water and energy balance of watersheds, a fundamental challenge in hydrology. However, previous attempts based on purely satellite‐based estimates have focused on closing the water and energy balances separately. They are hindered by the lack of estimates of key components, such as runoff. Here, we posit a novel approach based on Budyko’s water and energy balance constraints. The approach is applied to quantify the degree of long‐term closure at the watershed scale, as well as its associated uncertainties, using an ensemble of global satellite data sets. We find large spatial variability across aridity, elevation, and other environmental gradients. Specifically, we find a positive correlation between elevation and closure uncertainty, as derived from the Budyko approach. In mountainous watersheds the uncertainty in closure is 3.9 ± 0.7 (dimensionless). Our results show that uncertainties in terrestrial evaporation contribute twice as much as precipitation uncertainties to errors in the closure of water and energy balance. Moreover, our results highlight the need for improving satellite‐based precipitation and evaporation data in humid temperate forests, where the closure error in the Budyko space is as high as 1.1 ± 0.3, compared to only 0.2 ± 0.03 in tropical forests. Comparing the results with land surface model‐based data sets driven by in situ precipitation, we find that Earth observation‐based data sets perform better in regions where precipitation gauges are sparse. These findings have implications for improving the understanding of global hydrology and regional water management and can guide the development of satellite remote sensing‐based data sets and Earth system models., Key Points A Budyko‐based approach to water and energy balance closure mitigates the need for runoff dataErrors in water and energy balance closure are influenced more by uncertainties in evaporation rather than precipitationInability of Earth observations to close the water and energy balance of temperate forests

Humboldt Gletscher is a 100‐km wide, slow‐moving glacier in north Greenland which holds a 19‐cm global sea level equivalent. Humboldt has been the fourth largest contributor to sea level rise since 1972 but the cause of its mass loss has not been elucidated. Multi‐beam echo sounding data collected in 2019 indicate a seabed 200 m deeper than previously known. Conductivity temperature depth data reveal the presence of warm water of Atlantic origin at 0°C at the glacier front and a warming of the ocean waters by 0.9 ± 0.1°C since 1962. Using an ocean model, we reconstruct grounded ice undercutting by the ocean, combine it with calculated retreat caused by ice thinning to floatation, and are able to fully explain the observed retreat. Two thirds of the retreat are caused by undercutting of grounded ice, which is a physical process not included in most ice sheet models., Key Points The 100‐km wide Humboldt Gletscher holds a 19‐cm sea level rise equivalent, lost 161 billion tons of mass, and retreated 13 km since 1972Warm waters at 0°C flood a 350–400 m deep trough on its northern flank that remains below sea level more than 100 km inlandWe explain the glacier retreat as 70% from ocean‐induced undercutting and 30% from thinning‐induced retreat

Irrigated rice requires intense water management under typical agronomic practices. Cost effective tools to improve the efficiency and assessment of water use is a key need for industry and resource managers to scale ecosystem services. In this research we advance model‐based decomposition and machine learning to map inundated rice using time‐series polarimetric, L‐band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) observations. Simultaneous ground truth observations recorded water depth inundation during the 2019 crop season using instrumented fields across the study site in Arkansas, USA. A three‐component model‐based decomposition generated metrics representing surface‐, double bounce‐, and volume‐scattering along with a shape factor, randomness factor, and the Radar Vegetation Index (RVI). These physically meaningful metrics characterized crop inundation status independent of growth stage including under dense canopy cover. Machine learning (ML) comparisons employed Random Forest (RF) using the UAVSAR derived parameters to identify cropland inundation status across the region. Outcomes show that RVI, proportion of the double‐bounce within total scattering, and the relative comparison between the double‐bounce and the volume scattering have moderate to strong mechanistic ability to identify rice inundation status with Overall Accuracy (OA) achieving 75%. The use of relative ratios further helped mitigate the impacts of far range incidence angles. The RF approach, which requires training data, achieved a higher OA and Kappa of 88% and 71%, respectively, when leveraging multiple SAR parameters. Thus, the combination of physical characterization and ML provides a powerful approach to retrieving cropland inundation under the canopy. The growth of polarimetric L‐band availability should enhance cropland inundation metrics beyond open water that are required for tracking water quantity at field scale over large areas., Key Points Cropland inundation assessment has largely focused on open waterQuad polarized L‐band SAR can help detect under canopy inundationThe underlying physical mechanisms driving scattering responses and machine learning algorithms had similar outcomes

We present a new solar irradiance reference spectrum representative of solar minimum conditions between solar cycles 24 and 25. The Total and Spectral Solar Irradiance Sensor‐1 (TSIS‐1) Hybrid Solar Reference Spectrum (HSRS) is developed by applying a modified spectral ratio method to normalize very high spectral resolution solar line data to the absolute irradiance scale of the TSIS‐1 Spectral Irradiance Monitor (SIM) and the CubeSat Compact SIM (CSIM). The high spectral resolution solar line data are the Air Force Geophysical Laboratory ultraviolet solar irradiance balloon observations, the ground‐based Quality Assurance of Spectral Ultraviolet Measurements In Europe Fourier transform spectrometer solar irradiance observations, the Kitt Peak National Observatory solar transmittance atlas, and the semi‐empirical Solar Pseudo‐Transmittance Spectrum atlas. The TSIS‐1 HSRS spans 202–2730 nm at 0.01 to ∼0.001 nm spectral resolution with uncertainties of 0.3% between 460 and 2365 nm and 1.3% at wavelengths outside that range., Key Points The TSIS‐1 Spectral Irradiance Monitor and Compact SIM instruments observe the Sun's irradiance spectrum at high accuracyThe TSIS‐1 Hybrid Solar Reference Spectrum consists of high resolution solar line data normalized to the TSIS‐1 SIM irradiance spectrumThe TSIS‐1 Hybrid Solar Reference Spectrum has at least 0.01 nm spectral resolution, spans 202–2730 nm, and is accurate to 0.3%–1.3%

We introduce new parameterizations for autoconversion and accretion rates that greatly improve representation of the growth processes of warm rain. The new parameterizations capitalize on machine‐learning and optimization techniques and are constrained by in situ cloud probe measurements from the recent Atmospheric Radiation Measurement Program field campaign at Azores. The uncertainty in the new estimates of autoconversion and accretion rates is about 15% and 5%, respectively, outperforming existing parameterizations. Our results confirm that cloud and drizzle water content are the most important factors for determining accretion rates. However, for autoconversion, in addition to cloud water content and droplet number concentration, we discovered a key role of drizzle number concentration that is missing in current parameterizations. The robust relation between autoconversion rate and drizzle number concentration is surprising but real, and furthermore supported by theory. Thus, drizzle number concentration should be considered in parameterizations for improved representation of the autoconversion process., Key Points Machine‐learning trained by in situ data constrains autoconversion and accretion rates with uncertainty of 15% and 5%, respectivelyThere is a surprising relation between autoconversion rate and drizzle number concentration that significantly improves parameterizationsThe exponent of autoconversion rate dependence on cloud number concentration is 0.75, lower than that in existing parameterizations

Sea surface temperature (SST) is an essential climate variable, that is directly used in climate monitoring. Although satellite measurements can offer continuous global coverage, obtaining a long‐term homogeneous satellite‐derived SST data set suitable for climate studies based on a single instrument is still a challenge. In this work, we assess a homogeneous SST data set derived from reprocessed Infrared Atmospheric Sounding Interferometer (IASI) level‐1 (L1C) radiance data. The SST is computed using Planck's Law and simple atmospheric corrections. We assess the data set using the ERA5 reanalysis and the EUMETSAT‐released IASI level‐2 SST product. Over the entire period, the reprocessed IASI SST shows a mean global difference with ERA5 close to zero, a mean absolute bias under 0.5°C, with a SD of difference around 0.3°C and a correlation coefficient over 0.99. In addition, the reprocessed data set shows a stable bias and SD, which is an advantage for climate studies. The interannual variability and trends were compared with other SST data sets: ERA5, Hadley Centre's SST (HadISST), and NOAA's Optimal Interpolation SST Analysis (OISSTv2). We found that the reprocessed SST data set is able to capture the patterns of interannual variability well, showing the same areas of high interannual variability (>1.5°C), including over the tropical Pacific in January corresponding to the El Niño Southern Oscillation. Although the period studied is relatively short, we demonstrate that the IASI data set reproduces the same trend patterns found in the other data sets (i.e., cooling trend in the North Atlantic, warming trend over the Mediterranean)., Key Points First IASI algorithm focused on sea surface temperature (SST) suitable for climate studiesThe IASI‐derived SST data set is compared with other available data setsClimate variability and trends are shown and compared to other data sets

This study estimates the influence of anthropogenic emission reductions on nitrogen dioxide ((Formula presented.)) and ozone ((Formula presented.)) concentration changes in Germany during the COVID-19 pandemic period using in-situ surface and Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) satellite column measurements and GEOS-Chem model simulations. We show that reductions in anthropogenic emissions in eight German metropolitan areas reduced mean in-situ (& column) (Formula presented.) concentrations by 23 (Formula presented.) (& 16 (Formula presented.)) between March 21 and June 30, 2020 after accounting for meteorology, whereas the corresponding mean in-situ (Formula presented.) concentration increased by 4 (Formula presented.) between March 21 and May 31, 2020, and decreased by 3 (Formula presented.) in June 2020, compared to 2019. In the winter and spring, the degree of (Formula presented.) saturation of ozone production is stronger than in the summer. This implies that future reductions in (Formula presented.) emissions in these metropolitan areas are likely to increase ozone pollution during winter and spring if appropriate mitigation measures are not implemented. TROPOMI (Formula presented.) concentrations decreased nationwide during the stricter lockdown period after accounting for meteorology with the exception of North-West Germany which can be attributed to enhanced (Formula presented.) emissions from agricultural soils., Journal of Geophysical Research: Atmospheres, 126 (19), ISSN:0148-0227, ISSN:2169-897X

TanSat is the 1st Chinese carbon dioxide (CO2) measurement satellite, launched in 2016. In this study, the University of Leicester Full Physics (UoL‐FP) algorithm is implemented for TanSat nadir mode XCO2 retrievals. We develop a spectrum correction method to reduce the retrieval errors by the online fitting of an 8th order Fourier series. The spectrum‐correction model and its a priori parameters are developed by analyzing the solar calibration measurement. This correction provides a significant improvement to the O2 A band retrieval. Accordingly, we extend the previous TanSat single CO2 weak band retrieval to a combined O2 A and CO2 weak band retrieval. A Genetic Algorithm (GA) has been applied to determine the threshold values of post‐screening filters. In total, 18.3% of the retrieved data is identified as high quality compared to the original measurements. The same quality control parameters have been used in a footprint independent multiple linear regression bias correction due to the strong correlation with the XCO2 retrieval error. Twenty sites of the Total Column Carbon Observing Network (TCCON) have been selected to validate our new approach for the TanSat XCO2 retrieval. We show that our new approach produces a significant improvement on the XCO2 retrieval accuracy and precision when compared to TCCON with an average bias and RMSE of −0.08 ppm and 1.47 ppm, respectively. The methods used in this study can help to improve the XCO2 retrieval from TanSat and subsequently the Level‐2 data production, and hence will be applied in the TanSat operational XCO2 processing., Key Points First using O2 A and 1.61 um CO2 band approaching TanSat XCO2 retreivalDevelopment a method on radiometric correction of TanSat L1B data in O2 A and 1.61 um CO2 Validation of new TanSat retrieval against TCCON and recived significant improved results compare to previously retrieval

We use a global transport model and satellite retrievals of the carbon dioxide (CO2) column average to explore the impact of CO2 emissions reductions that occurred during the economic downturn at the start of the Covid‐19 pandemic. The changes in the column averages are substantial in a few places of the model global grid, but the induced gradients are most often less than the random errors of the retrievals. The current necessity to restrict the quality‐assured column retrievals to almost cloud‐free areas appears to be a major obstacle in identifying changes in CO2 emissions. Indeed, large changes have occurred in the presence of clouds and, in places that were cloud‐free in 2020, the comparison with previous years is hampered by different cloud conditions during these years. We therefore recommend to favor all‐weather CO2 monitoring systems, at least in situ, to support international efforts to reduce emissions., Key Points Covid‐19 impacted the CO2 column mostly in the vicinity of a few emission locations that changed over time.These places have not been well observed by the OCO‐2 satellite because of frequent or persistent cloud conditions.To support the Paris Agreement on climate, priority should be given on the development of all‐weather carbon‐monitoring systems.

We use TROPOMI (TROPOspheric Monitoring Instrument) tropospheric nitrogen dioxide (NO2) measurements to identify cropland soil nitrogen oxide (NOx = NO + NO2) emissions at daily to seasonal scales in the U.S. Southern Mississippi River Valley. Evaluating 1.5 years of TROPOMI observations with a box model, we observe seasonality in local NOx enhancements and estimate maximum cropland soil NOx emissions (15–34 ng N m−2 s−1) early in growing season (May–June). We observe soil NOx pulsing in response to daily decreases in volumetric soil moisture (VSM) as measured by the Soil Moisture Active Passive (SMAP) satellite. Daily NO2 enhancements reach up to 0.8 × 1015 molecules cm−2 4–8 days after precipitation when VSM decreases to ~30%, reflecting emissions behavior distinct from previously defined soil NOx pulse events. This demonstrates that TROPOMI NO2 observations, combined with observations of underlying process controls (e.g., soil moisture), can constrain soil NOx processes from space., Key Points Daily TROPOMI data provide new opportunities to observe regional cropland NOx emissions from spaceSoil NOx pulsing is identified throughout the growing season with a NOx maximum observed when soils dry to ~30% volumetric soil moistureCropland NOx emissions peak at the onset of the growing season as determined by TROPOMI NO2 enhancements and a box model framework

Lakes are often defined by seasonal cycles. The seasonal timing, or phenology, of many lake processes are changing in response to human activities. However, long‐term records exist for few lakes, and extrapolating patterns observed in these lakes to entire landscapes is exceedingly difficult using the limited number of available in situ observations. Limited landscape‐level observations mean we do not know how common shifts in lake phenology are at macroscales. Here, we use a new remote sensing data set, LimnoSat‐US, to analyze U.S. summer lake color phenology between 1984 and 2020 across more than 26,000 lakes. Our results show that summer lake color seasonality can be generalized into five distinct phenology groups that follow well‐known patterns of phytoplankton succession. The frequency with which lakes transition from one phenology group to another is tied to lake and landscape level characteristics. Lakes with high inflows and low variation in their seasonal surface area are generally more stable, while lakes in areas with high interannual variations in climate and catchment population density show less stability. Our results reveal previously unexamined spatiotemporal patterns in lake seasonality and demonstrate the utility of LimnoSat‐US, which, with over 22 million remote sensing observations of lakes, creates novel opportunities to examine changing lake ecosystems at a national scale., Key Points Summer lake color phenology can be generalized into five distinct seasonal patterns of greening and blueing eventsSince the mid‐1990s, the number of lakes with color patterns corresponding to eutrophic waterbodies has been increasingWe observe these patterns using a new U.S. lake remote sensing data set that contains over 22 million lake observations

The ongoing coronavirus disease 2019 (COVID‐19) pandemic has imposed tight mobility restrictions in urban areas, causing substantial reduction in roadway traffic. Many public parking lots are nearly vacant as people across the world have gone on lockdown since mid‐March. This environmental change may have impacts on Global Navigation Satellite System (GNSS) sensors installed on roof of buildings. Here, we use a monitoring site in Boston to exemplify a likely sensitivity of precise GNSS sensors to their nearby dynamic environments including parked vehicles in parking lots. We show that reduced number of parked vehicles since 23 March has decreased the reflector roughness, resulting in an increase in the reflected signal power whose amplitude is quantified by GNSS interferometric reflectometry technique. The uncertainty of retrieved GNSS antenna height drops with beginning of lockdown, allowing more accurate estimate of reflector height, which could have a general implication for better understanding of the fundamental limitations of the technique., Key Points The reflected signal (RS) from a parking lot next to a precise GNSS sensor is analyzed using GNSS interferometric reflectometry techniqueThe amplitude of RS increases since 23 March 2020 with the reflector surface becoming more planer due to absence of cars in the parking lotThe uncertainty of retrieved antenna height from RS drops with beginning of lockdown, allowing more accurate estimate of antenna height

Significant recent advances in satellite remote sensing allow environmental managers to detect and monitor cyanobacterial harmful algal blooms (cyanoHAB), and these capabilities are being used more frequently in water quality management. A quantitative estimate of the socioeconomic benefits generated from these new capabilities, known as an impact assessment, was missing from the growing literature on cyanoHABs and remote sensing. In this paper, we present an impact assessment framework to characterize the socioeconomic benefits of satellite remote sensing for detecting cyanoHABs and managing recreational advisories at freshwater lakes. We then apply this framework to estimate the socioeconomic benefits of satellite data that were used to manage a 2017 cyanoHAB event in Utah Lake. CyanoHAB events on Utah Lake can pose health risks to people who interact with the blooms through recreation. We find that the availability of satellite data yielded socioeconomic benefits by improving human health outcomes valued at approximately $370,000, though a sensitivity analysis reveals that this central estimate can vary significantly ($55,000–$1,057,000 in benefits) as a result of different assumptions regarding the time delay in posting a recreational advisory, the number of people exposed to the cyanoHAB, the number of people who experience gastrointestinal symptoms, and the cost per case of illness., Key Points Cyanobacterial harmful algal blooms (cyanoHABs) are a human health risk that is typically mitigated by recreational advisories and warningsWe present a framework to quantify the socioeconomic benefits of using remote sensing technology to test for the presence of cyanoHABsSatellite technology yielded between $55,000 and $1,057,000 in socioeconomic benefits associated with improved human health outcomes

The Atmospheric Infrared Sounder (AIRS) Observations for Model Intercomparison Projects (Obs4MIPs) Version 2.0 (V2.0) monthly mean tropospheric air temperature, specific humidity, and relative humidity profile data were designed for climate model evaluation in the context of the Coupled Model Intercomparison Project (CMIP). Due to the limitations of the Aqua satellite orbit and the AIRS retrieval algorithm, the sampling biases of the AIRS Obs4MIPs V2.0 data can be large for certain cases and must be considered when the AIRS Obs4MIPs V2.0 data are used for climate model evaluation. In this study, we estimate the sampling biases of the AIRS Obs4MIPs V2.0 data based on the fifth generation of the European Centre for Medium‐Range Weather Forecasts (ECMWF) (ERA5) reanalysis and cross‐check them using the Modern‐Era Retrospective Analysis for Research and Application, Version 2 (MERRA‐2) reanalysis. We then remove the estimated sampling biases from the AIRS Obs4MIPs V2.0 data and produce the sampling‐bias‐corrected AIRS Obs4MIPs V2.1 data that have been published at the Earth System Grid Federation (ESGF) data centers and should be used in the future for climate model evaluation., Key Points The sampling biases of the AIRS Obs4MIPs V2.0 data are estimated based on ERA5 reanalysis and cross‐checked based on MERRA‐2 reanalysisThe sampling‐bias‐corrected AIRS Obs4MIPs V2.1 data are produced by removing the sampling bias estimates based on ERA5The new AIRS Obs4MIPs V2.1 data have been published on ESGF and should be used in the future for climate model evaluation

We report the first simultaneous, common‐volume lidar observations of thermosphere‐ionosphere Fe (TIFe) and Na (TINa) layers in Antarctica. We also report the observational discovery of nearly one‐to‐one correspondence between TIFe and aurora activity, enhanced ionization layers, and converging electric fields. Distinctive TIFe layers have a peak density of ~384 cm−3 and the TIFe mixing ratio peaks around 123 km, ~5 times the mesospheric layer maximum. All evidence shows that Fe+ ion‐neutralization is the major formation mechanism of TIFe layers. The TINa mixing ratio often exhibits a broad peak at TIFe altitudes, providing evidence for in situ production via Na+ neutralization. However, the tenuous TINa layers persist long beyond TIFe disappearance and reveal gravity wave perturbations, suggesting a dynamic background of neutral Na, but not Fe, above 110 km. The striking differences between distinct TIFe and diffuse TINa suggest differential transport between Fe and Na, possibly due to mass separation., Key Points First simultaneous lidar observations show striking differences between distinct TIFe and diffuse TINa, suggesting differential transportWe report the observational discovery of the correlation of TIFe with aurora and enhanced ionization, revealing plasma‐neutral couplingAll observational evidence points to the Fe+ ion‐neutralization origin of TIFe while suggesting a background of neutral Na above 110 km

Convectively injected water vapor (H2O) in the North American (NA) summer lowermost stratosphere results in significant outliers in the 100‐hPa H2O measurements from the Aura Microwave Limb Sounder (MLS). MLS statistics from 15 years confirm that the NA region contains over 60% of global 100‐hPa H2O > 12 ppmv, despite having only ∼1.8% of all MLS observations. A profile sampled in August 2019 stands out, with H2O=26.3 ppmv, far exceeding the prior record and the median ∼4.5‐ppmv abundance in NA. This particular outlier is associated with a large overshooting convective event (OCE) that spanned multiple U.S. states and persisted for several hours. Colocation of the MLS data over NA with cloud observations from Aqua's Moderate Resolution Imaging Spectroradiometer (MODIS) reveals the unique character of this case, as only 2.3% of MLS profiles are as close to an OCE and only 0.024% of OCEs cover as large an area within a 500‐km perimeter of a profile., Key Points A profile with unprecedented stratospheric humidity is associated with large overshooting convectionLarge lower stratospheric humidity outliers are in close proximity to strong overshooting convectionMLS humidity indicates trend toward larger stratospheric outliers and strengthening of North American monsoon

The retreat of glaciers in response to global warming has the potential to trigger landslides in glaciated regions around the globe. Landslides that enter fjords or lakes can cause tsunamis, which endanger people and infrastructure far from the landslide itself. Here we document the ongoing movement of an unstable slope (total volume of 455 × 106 m3) in Barry Arm, a fjord in Prince William Sound, Alaska. The slope moved rapidly between 2010 and 2017, yielding a horizontal displacement of 120 m, which is highly correlated with the rapid retreat and thinning of Barry Glacier. Should the entire unstable slope collapse at once, preliminary tsunami modeling suggests a maximum runup of 300 m near the landslide, which may have devastating impacts on local communities. Our findings highlight the need for interdisciplinary studies of recently deglaciated fjords to refine our understanding of the impact of climate change on landslides and tsunamis., Key Points This study quantifies the motion of a large periglacial landslideIt is unique that such a landslide is detected before potential failureThis study calls attention to the glacier‐retreat‐landslide‐tsunami hazards cascade

A new model was recently introduced to correct for higher‐order ionospheric residual biases in radio occultation (RO) data. The model depends on the α 1 and α 2 dual‐frequency bending angle difference squared, and a factor κ, which varies with time, season, solar activity, and height, needing only the F10.7 solar radio flux index as additional background information. To date, this kappa‐correction was analyzed in simulation studies. In this study, we test it on real observed Metop‐A RO data. The goal is to improve the accuracy of monthly mean RO climate records, potentially raising the accuracy of RO data toward higher stratospheric altitudes. We performed a thorough analysis of the kappa‐correction, evaluating its ionospheric sensitivity during the solar cycle for monthly RO climatologies and comparing the kappa‐corrected RO stratospheric climatologies to three other data sets from reanalysis and passive infrared sounding. We find a clear dependence of the kappa‐correction on solar activity, geographic location, and altitude; hence, it reduces systematic errors that vary with the solar cycle. From low to high solar activity conditions, the correction can increase from values of about 0.2 K to more than 2.0 K at altitudes between 40 to 45 km. The correction shifts RO climatologies toward warmer temperatures. With respect to other data sets, however, we found it difficult to draw firm conclusions, because the biases in the other data sets appear to be at similar magnitude as the size of the kappa‐correction. Further validation with more accurate data will be useful., Key Points The kappa‐correction model is tested for the impact of its correction of higher‐order ionospheric biases in radio occultation dataThe magnitude of the kappa‐correction varies over the solar cycle from 0.2 K to more than 2.0 K at about 40 kmThe correction shifts the stratospheric data toward warmer temperatures; firm validation based on other data sets was difficult

The 2018 NASA Health and Air Quality Applied Science Team (HAQAST) “Indicators” Tiger Team collaboration between NASA‐supported scientists and civil society stakeholders aimed to develop satellite‐derived global air pollution and climate indicators. This Commentary shares our experience and lessons learned. Together, the team developed methods to track wildfires, dust storms, pollen counts, urban green space, nitrogen dioxide concentrations and asthma burdens, tropospheric ozone concentrations, and urban particulate matter mortality. Participatory knowledge production can lead to more actionable information but requires time, flexibility, and continuous engagement. Ground measurements are still needed for ground truthing, and sustained collaboration over time remains a challenge., Key Points The NASA Health and Air Quality Applied Science Team “Indicators” Tiger Team developed satellite‐based air quality and climate indicatorsParticipatory knowledge production can lead to more useful information for stakeholders but requires continuous engagement and flexibilityGround measurements are still needed, and sustained collaboration between the researchers and stakeholders over time remains a challenge

TROPOMI satellite data show substantial drops in nitrogen dioxide (NO2) during COVID‐19 physical distancing. To attribute NO2 changes to NOX emissions changes over short timescales, one must account for meteorology. We find that meteorological patterns were especially favorable for low NO2 in much of the U.S. in spring 2020, complicating comparisons with spring 2019. Meteorological variations between years can cause column NO2 differences of ~15% over monthly timescales. After accounting for sun angle and meteorological considerations, we calculate that NO2 drops ranged between 9.2 – 43.4% among twenty cities in North America, with a median of 21.6%. Of the studied cities, largest NO2 drops (>30%) were in San Jose, Los Angeles, and Toronto, and smallest drops (, Plain‐Language Summary Nitrogen dioxide (NO2) is an air pollutant whose prevalence in urban areas is linked to fossil fuel combustion. The NO2 concentrations in our atmosphere are primarily a function of the magnitude of nitrogen oxide (NOx) emissions and weather factors such as sun angle, wind speed, and temperature. In this work, we developed two novel methods to account for weather impacts on daily pollution levels during COVID‐19 precautions. Once we accounted for favorable weather conditions that in some cases kept air pollution low independent of tail‐pipe emissions, calculated air pollutant emission reductions varied dramatically (9 – 43%) among twenty North American cities. Results can be used to understand factors contributing to inconsistent NO2 changes during physical distancing, which can inform the effectiveness of COVID‐19 protocols and aid future policy development. These methodologies will allow us to respond more quickly in future unintended experiments when emissions change suddenly.

Tropical anvil clouds have a profound impact on Earth's weather and climate. Their role in Earth's energy balance and hydrologic cycle is heavily modulated by the vertical structure of the microphysical properties for various hydrometeors in these clouds and their dependence on the ambient environmental conditions. Accurate representations of the variability and covariability of such vertical structures are key to both the satellite remote sensing of cloud and precipitation and numerical modeling of weather and climate, which remain a challenge. This study presents a new method to combine vertically resolved observations from CloudSat radar reflectivity and Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation cloud masks with probability distributions of cloud microphysical properties and the ambient atmospheric conditions from detailed in situ measurements on tropical anvils sampled during the National Aeronautics and Space Administration TC4 (Tropical Composition, Cloud and Climate Coupling) mission. We focus on the microphysical properties of the vertical distribution of ice water content, particle size distributions, and effective sizes for different hydrometeors, including ice particles and supercooled liquid droplets. Results from this method are compared with those from in situ data alone and various CloudSat/Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation cloud retrievals. The sampling limitation of the field experiment and algorithm limitations in the current retrievals is highlighted, especially for the liquid cloud particles, while a generally good agreement with ice cloud microphysical properties is seen from different methods. While the method presented in this study is applied to tropical anvil clouds observed during TC4, it can be readily employed to study a broad range of ice clouds sampled by various field campaigns., Key Points The vertical structure of ice cloud microphysics is studied by combining field campaign in situ measurements with satellite observationsA generally good agreement with ice cloud microphysical properties below 12 km is seen from in situ observations and satellite retrievalsResults indicate the sampling limitation of the field experiment and algorithm limitations in the satellite retrievals

A detailed analysis of double tropopause (DT) occurrences requires vertically well resolved, accurate, and globally distributed information on the troposphere‐stratosphere transition zone. Here, we use radio occultation observations from 2001 to 2018 with such properties. We establish a connection between El Niño‐Southern Oscillation (ENSO) phases and the distribution of DTs by analyzing the global and seasonal DT characteristics. The seasonal distribution of DTs reveals several hotspot locations, such as near the subtropical jet stream and over high mountain ranges, where DTs occur particularly often. In this study, we detect a higher number of DTs during the cold La Niña state while warmer El Niño events result in lower DT rates, affecting the structure of the tropopause region. Close to the Niño 3 region, this relates to a much lower first lapse rate tropopause altitude during La Niña and corresponds to an apparent narrowing of the tropical belt there., Key Points Double tropopause characteristics and the connection to ENSO are analyzed using vertically high resolved GPS RO observationsMore double tropopauses are detected during the cold La Niña phase and less during El NiñoDuring La Niña, a much lower first lapse rate tropopause corresponds to a narrowing of the tropical belt around the Niño 3 region

This paper presents measurements of the amplitudes and timings of the combined, annual, and semiannual variations of thermospheric neutral density, and a comparison of these density variations with measurements of the infrared emissions from carbon dioxide and nitric oxide in the thermosphere. The density values were obtained from measurements of the atmospheric drag experienced by the Challenging Minisatellite Payload, Gravity Recovery and Climate Experiment A, Gravity field and Ocean Circulation Explorer, and three Swarm satellites, while the optical emissions were measured with the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument on the Thermosphere Ionosphere Mesosphere Energetics and Dynamics satellite. These data span a time period of 16 years. A database containing global average densities that were derived from the orbits of about 5,000 objects (Emmert, 2009, https://doi.org/10.1029/2009JA014102, 2015b, https://doi.org/10.1002/2015JA021047) was employed for calibrating these density data. A comparison with the NRLMSISE‐00 model was used to derive measurements of how much the density changes over time due to these seasonal variations. It is found that the seasonal density oscillations have significant variations in amplitude and timing. In order to test the practicality of using optical emissions as a monitoring tool, the SABER data were fit to the measured variations. Even the most simple fit that used only filtered carbon dioxide emissions had good correlations with the measured oscillations. However, the density oscillations were also well predicted by a simple Fourier series, contrary to original expectations. Nevertheless, measurements of the optical emissions from the thermosphere are expected to have a role in future understanding and prediction of the semiannual variations., Key Points Four satellite missions are used to derive thermosphere density variations caused by annual and semiannual oscillations over a 16‐year periodVariations in thermospheric density are compared with emissions from carbon dioxide and nitric oxide measured with the SABER instrumentThe carbon dioxide emissions and simple Fourier series fits have good correlations with thermosphere density oscillations

The accelerated rate of decline in groundwater levels across California's Central Valley results from overdrafting and low rates of natural recharge and is exacerbated by droughts. The lack of observations with an adequate spatiotemporal resolution to constrain the evolution of groundwater resources poses severe challenges to water management efforts. Here we present SAR interferometric measurements of high‐resolution vertical land motion across the valley, revealing multiscale patterns of aquifer hydrogeological properties and groundwater storage change. Investigating the depletion and degradation of the aquifer‐system during 2007–2010, when the entire valley experienced a severe drought, we find that ~2% of total aquifer‐system storage was permanently lost, owing to irreversible compaction of the system. Over this period, the seasonal groundwater storage change amplitude of 10.11 ± 2.5 km3 modulates a long‐term groundwater storage decline of 21.32 ± 7.2 km3. Estimates for subbasins show more complex patterns, most likely associated with local hydrogeology, recharge, demand, and underground flow. Presented measurements of aquifer‐system compaction provide a more complete understanding of groundwater dynamics and can potentially be used to improve water security., Key Points Time‐dependent vertical land motion in California's Central Valley reflects the evolution of groundwater stocks during 2007–2010Drought‐related dynamics of the aquifer‐system across the valley are investigated using deformation and groundwater level dataRegional‐scale distribution of mechanical properties of the aquifer‐system is resolved.

Information about wetland dynamics remains a major missing gap in characterizing, understanding, and projecting changes in atmospheric methane and terrestrial water storage. A review of current satellite methods to delineate and monitor wetland change shows some recent advances, but much improved sensing technologies are still needed for wetland mapping, not only to provide more accurate global inventories but also to examine changes spanning multiple decades. Global Navigation Satellite Systems Reflectometry (GNSS‐R) signatures from aircraft over the Ebro River Delta in Spain and satellite measurements over the Mississippi River and adjacent watersheds demonstrate that inundated wetlands can be identified under different vegetation conditions including a dense rice canopy and a thick forest with tall trees, where optical sensors and monostatic radars provide limited capabilities. Advantages as well as constraints of GNSS‐R are presented, and the synergy with various satellite observations are considered to achieve a breakthrough capability for multidecadal wetland dynamics monitoring with frequent global coverage at multiple spatial and temporal scales., Key Points Information of wetland extent and dynamics remains a major missing gap as current observation methods are insufficientAircraft and satellite data demonstrate that GNSS‐R can identify inundated wetlands even under thick and dense vegetation coverGNSS‐R contributes to a potential breakthrough in wetland dynamics monitoring for global water and energy cycle research and applications

Small volcanic eruptions and their effects have recently come into research focus. While large eruptions are known to strongly affect stratospheric temperature, the impacts of smaller eruptions are hard to quantify because their signals are masked by natural variability. Here, we quantify the temperature signals from small volcanic eruptions between 2002 and 2016 using new vertically resolved aerosol data and precise temperature observations from radio occultation. We find characteristic space‐time signals that can be associated with specific eruptions. In the lower stratosphere, robust warming signals are observed, while in the midstratosphere also cooling signals of some eruptions appear. We find that the volcanic contribution to the temperature trend is up to 20%, depending on latitude and altitude. We conclude that detailed knowledge of the vertical structure of volcanic temperature impacts is crucial for comprehensive trend analysis in order to separate natural from anthropogenic temperature changes., Key Points The imprints of minor volcanic eruptions on stratospheric temperature are precisely quantifiedVertically high resolved aerosol and temperature observations facilitate the accurate detection of volcanic signals in space and timeMinor volcanic eruptions clearly affect the short‐term temperature trend in the lower stratosphere

Using CALIPSO‐CloudSat‐Clouds and the Earth's Radiant Energy System‐Moderate Resolution Imaging Spectrometer data set, this study documents the seasonal variation of sea ice, cloud, and atmospheric properties in the Arctic (70°N–82°N) for 2007–2010. A surface‐type stratification—consisting Permanent Ocean, Land, Permanent Ice, and Transient Sea Ice—is used to investigate the influence of surface type on low‐level Arctic cloud liquid water path (LWP) seasonality. The results show significant variations in the Arctic low‐level cloud LWP by surface type linked to differences in thermodynamic state. Subdividing the Transient Ice region (seasonal sea ice zone) by melt/freeze season onset dates reveals a complex influence of sea ice variations on low cloud LWP seasonality. We find that lower tropospheric stability is the primary factor affecting the seasonality of cloud LWP. Our results suggest that variations in sea ice melt/freeze onset have a significant influence on the seasonality of low‐level cloud LWP by modulating the lower tropospheric thermal structure and not by modifying the surface evaporation rate in late spring and midsummer. We find no significant dependence of the May low‐level cloud LWP peak on the melt/freeze onset dates, whereas and September/October low‐level cloud LWP maximum shifts later in the season for earlier melt/later freeze onset regions. The Arctic low cloud LWP seasonality is controlled by several surface‐atmosphere interaction processes; the importance of each varies seasonally due to the thermodynamic properties of sea ice. Our results demonstrate that when analyzing Arctic cloud‐sea ice interactions, a seasonal perspective is critical., Key Points The Arctic low cloud liquid water path seasonal cycle exhibits a surface‐type dependenceSea ice affects liquid water path seasonality by modulating lower tropospheric stabilityRegions of earlier sea ice melt and later freeze onset show a larger cloud liquid water path variance

Moving beyond the widely used kinematic models for the deformation sources, we present a new dynamic model to describe the process of injecting magma into an existing magma reservoir. To validate this model, we derive an analytical solution and compare its results to those calculated using the Finite Element Method. A Newtonian fluid characterized by its viscosity, density, and overpressure (relative to the lithostatic value) flows through a vertical conduit, intruding into a reservoir embedded in an elastic domain, leading to an increase in reservoir pressure and time‐dependent surface deformation. We apply our injection model to Interferometric Synthetic Aperture Radar (InSAR) data from the ongoing unrest episode at Laguna del Maule (Chile) volcanic field that started in 2007. Using a grid search optimization, we minimize the misfit to the InSAR displacement data and vary the three parameters governing the analytical solution: the characteristic timescale τ P for magma propagation, the maximum injection pressure, and the inflection time when the acceleration switches from positive to negative. For a spheroid with semimajor axis a = 6200 m, semiminor axis c = 100 m, located at a depth of 4.5 km in a purely elastic half‐space, the best fit to the InSAR displacement data occurs for τ P=9.5 years and an injection pressure rising up to 11.5 MPa for 2 years. The volume flow rate increased to 1.2 m3/s for 2 years and then decreased to 0.7 m3/s in 2014. In 7.3 years, at least 187 × 106 m3 of magma was injected., Key Points Our analytical and numerical model describes viscous magma propagation into a reservoirIncreasing conduit inlet pressure and volumetric flow rate accounts for the accelerating upliftAt least 187 million cubic meters of magma with viscosity 100 MPa s was injected between 2007 and 2014

Climate and reanalysis models contain large water and energy budget errors over tropical land related to the misrepresentation of diurnally forced moist convection. Motivated by recent work suggesting that the water and energy budget is influenced by the sensitivity of the convective diurnal cycle to atmospheric state, this study investigates the relationship between convective intensity, the convective diurnal cycle, and atmospheric state in a region of frequent convection—the Amazon. Daily, 3‐hourly satellite observations of top of atmosphere (TOA) fluxes from Clouds and the Earth's Radiant Energy System Ed3a SYN1DEG and precipitation from Tropical Rainfall Measuring Mission 3B42 data sets are collocated with twice daily Integrated Global Radiosonde Archive observations from 2002 to 2012 and hourly flux tower observations. Percentiles of daily minimum outgoing longwave radiation are used to define convective intensity regimes. The results indicate a significant increase in the convective diurnal cycle amplitude with increased convective intensity. The TOA flux diurnal phase exhibits 1–3 h shifts with convective intensity, and precipitation phase is less sensitive. However, the timing of precipitation onset occurs 2–3 h earlier and the duration lasts 3–5 h longer on very convective compared to stable days. While statistically significant changes are found between morning atmospheric state and convective intensity, variations in upper and lower tropospheric humidity exhibit the strongest relationships with convective intensity and diurnal cycle characteristics. Lastly, convective available potential energy (CAPE) is found to vary with convective intensity but does not explain the variations in Amazonian convection, suggesting that a CAPE‐based convective parameterization will not capture the observed behavior without incorporating the sensitivity of convection to column humidity., Key Points Amazon convective diurnal cycle characteristics vary with convective intensityColumn humidity is the most important factor explaining convective behaviorCAPE is a poor predictor of the Amazonian convective diurnal cycle

A new, satellite‐based methodology is developed to evaluate convective mass flux and large‐scale total mass flux. To derive the convective mass flux, candidate profiles of in‐cloud vertical velocity are first constructed with a simple plume model under the constraint of ambient sounding and then narrowed down to the solution that matches satellite‐derived cloud top buoyancy. Meanwhile, the large‐scale total mass flux is provided separately from satellite soundings by a method developed previously. All satellite snapshots are sorted into a composite time series that delineates the evolution of a vigorous and organized convective system. Principal findings are the following. First, convective mass flux is modulated primarily by convective cloud cover, with the intensity of individual convection being less variable over time. Second, convective mass flux dominates the total mass flux only during the early hours of the convective evolution; as convective system matures, a residual mass flux builds up in the mass flux balance that is reminiscent of stratiform dynamics. The method developed in this study is expected to be of unique utility for future observational diagnosis of tropical convective dynamics and for evaluation of global climate model cumulus parameterizations in a global sense., Key Points A new strategy is proposed to analyze convective and large‐scale mass fluxes from satellite dataThe systematic evolution of the mass fluxes is captured in association with convective developmentThe findings have the potential to serve as an observational basis to assess GCM parameterizations

We report on a systematic record of SO2 flux emissions from individual vents of Etna volcano (Sicily), which we obtained using a permanent UV camera network. Observations were carried out in summer 2014, a period encompassing two eruptive episodes of the New South East Crater (NSEC) and a fissure‐fed eruption in the upper Valle del Bove. We demonstrate that our vent‐resolved SO2 flux time series allow capturing shifts in activity from one vent to another and contribute to our understanding of Etna's shallow plumbing system structure. We find that the fissure eruption contributed ~50,000 t of SO2 or ~30% of the SO2 emitted by the volcano during the 5 July to 10 August eruptive interval. Activity from this eruptive vent gradually vanished on 10 August, marking a switch of degassing toward the NSEC. Onset of degassing at the NSEC was a precursory to explosive paroxysmal activity on 11–15 August., Key Points Records of SO2 flux emissions from Etna's individual vents allow capturing shifts in volcanic activityVent‐resolved SO2 flux time series provide constraints on geometry of the shallow plumbing systemVent‐resolved SO2 flux time series demonstrate SO2 flux increase precursory to paroxysmal (lava fountaining) activity

Amazonian deep convection experiences a strong diurnal cycle driven by the cycle in surface sensible heat flux, which contributes to a significant diurnal cycle in the top of the atmosphere (TOA) radiative flux. Even when accounting for seasonal variability, the TOA flux diurnal cycle varies significantly on the monthly timescale. Previous work shows evidence supporting a connection between variability in the convective and radiative cycles, likely modulated by variability in monthly atmospheric state (e.g., convective instability). The hypothesized relationships are further investigated with regression analysis of the radiative diurnal cycle and atmospheric state using additional meteorological variables representing convective instability and upper tropospheric humidity. The results are recalculated with three different reanalyses to test the reliability of the results. The radiative diurnal cycle sensitivity to upper tropospheric humidity is about equal in magnitude to that of convective instability. In addition, the results are recalculated with the data subdivided into the wet and dry seasons. Overall, clear‐sky radiative effects have a dominant role in radiative diurnal cycle variability during the dry season. Because of this, even in a convectively active region, the clear‐sky radiative effects must be accounted for in order to fully explain the monthly variability in diurnal cycle. Finally, while there is general agreement between the different reanalysis‐based results when examining the full data time domain (without regard to time of year), there are significant disagreements when the data are divided into wet and dry seasons. The questionable reliability of reanalysis data is a major limitation., Key Points Upper tropospheric humidity alters the TOA flux diurnal cycle as strongly as vertical instabilityClear‐sky effects control TOA flux diurnal amplitude, while cloud forcing controls timingDisagreement in reanalysis monthly variability greatly affects assessment of TOA flux sensitivity

Observations of thermospheric infrared radiative cooling by carbon dioxide (CO2) and nitric oxide (NO) from 2002 to 2018 are presented. The time span covers more than 6,000 days including most of solar cycle (SC) 23 and the entirety of SC 24 to date. Maxima of infrared cooling rate profiles (nW/m3) are smaller during SC 24 than SC 23, indicating a cooler thermosphere. Rates of global infrared power (W) from CO2 are now at levels observed during the deep solar minimum of 2009. Rates of NO power are still larger than those observed during 2009 and are being maintained at an elevated level by geomagnetic activity. During SC 24 to date, the thermosphere has radiated 70% of the energy of the mean of the past five cycles and would require an additional 1,690 days at current infrared radiation rates to reach that amount., Key Points Global infrared power radiated by NO and CO2 from thermosphere during solar cycle 24 are, to date, only 50% and 73% of solar cycle 23SC 24 would have to last 1,690 more days (making it 1 year longer than SC 23) for its infrared power to equal mean of past five cyclesNO power levels currently are still 34% larger than at solar minimum conditions of 2009, due to higher geomagnetic activity in 2018

Enhancement of precipitation processes aloft over complex terrain is documented using reflectivity data from an S‐band scanning radar (NPOL) that was deployed on the west coast of Washington State during the Olympic Mountains Experiment (OLYMPEX). From November 2015 through mid‐January 2016, NPOL obtained high‐resolution data within sectors over the ocean and over the windward slopes of the Olympic Mountains. Contoured Frequency by Altitude Diagrams of radar reflectivity highlight a higher frequency of occurrence of larger reflectivities for all heights between 2 and 8 km over land compared to ocean, with the largest difference in the 4‐ to 6‐km range indicating a robust signature of enhancement aloft over the windward slopes. This enhancement pattern is found to some degree under all environmental conditions considered but is especially pronounced during periods of high vapor transport, high melting level height, southwest low‐level winds, and neutral stability. These conditions are generally associated with warm sectors of midlatitude cyclones and atmospheric rivers. Past studies have postulated that a secondary enhancement in reflectivity aloft was an intrinsic part of atmospheric river type systems. However, these results show that further significant enhancement of this signature occurs as deep moist‐neutral, high water vapor content flow is lifted when it encounters a mountain range. Reflectivity data from the dual‐precipitation radar aboard the Global Precipitation Measurement satellite also documents this reflectivity increase aloft over the Olympic Mountains compared to the adjacent ocean, showing the potential for Global Precipitation Measurement to provide reliable estimates of precipitation structure over remote mountainous regions., Key Points Enhancement of precipitation processes is found in radar reflectivity data above the melting level over complex terrainEnhancement aloft is especially pronounced during periods of high vapor transport, onshore flow, and neutral low‐level static stabilitySatellite‐borne radar detects this enhancement so that reliable estimates of precipitation over remote mountain regions are possible

NASA's Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne lidar mission which will produce near global (51.6°S to 51.6°N) maps of forest structure and above‐ground biomass density during its 2‐year mission. GEDI uses a waveform simulator for calibration of algorithms and assessing mission accuracy. This paper implements a waveform simulator, using the method proposed in Blair and Hofton (1999; https://doi.org/10.1029/1999GL010484), and builds upon that work by adding instrument noise and by validating simulated waveforms across a range of forest types, airborne laser scanning (ALS) instruments, and survey configurations. The simulator was validated by comparing waveform metrics derived from simulated waveforms against those derived from observed large‐footprint, full‐waveform lidar data from NASA's airborne Land, Vegetation, and Ice Sensor (LVIS). The simulator was found to produce waveform metrics with a mean bias of less than 0.22 m and a root‐mean‐square error of less than 5.7 m, as long as the ALS data had sufficient pulse density. The minimum pulse density required depended upon the instrument. Measurement errors due to instrument noise predicted by the simulator were within 1.5 m of those from observed waveforms and 70–85% of variance in measurement error was explained. Changing the ALS survey configuration had no significant impact on simulated metrics, suggesting that the ALS pulse density is a sufficient metric of simulator accuracy across the range of conditions and instruments tested. These results give confidence in the use of the simulator for the pre‐launch calibration and performance assessment of the GEDI mission., Key Points GEDI's simulator has been validated and found accurate enough for pre‐launch calibration activitiesThe uncertainties of the simulator have been quantified and ALS beam density identified as a sufficient measure of accuracyInteresting quirks of full‐waveform metrics have been highlighted and investigated

The cloud droplet number concentration (N d) is of central interest to improve the understanding of cloud physics and for quantifying the effective radiative forcing by aerosol‐cloud interactions. Current standard satellite retrievals do not operationally provide N d, but it can be inferred from retrievals of cloud optical depth (τ c) cloud droplet effective radius (r e) and cloud top temperature. This review summarizes issues with this approach and quantifies uncertainties. A total relative uncertainty of 78% is inferred for pixel‐level retrievals for relatively homogeneous, optically thick and unobscured stratiform clouds with favorable viewing geometry. The uncertainty is even greater if these conditions are not met. For averages over 1° ×1° regions the uncertainty is reduced to 54% assuming random errors for instrument uncertainties. In contrast, the few evaluation studies against reference in situ observations suggest much better accuracy with little variability in the bias. More such studies are required for a better error characterization. N d uncertainty is dominated by errors in r e, and therefore, improvements in r e retrievals would greatly improve the quality of the N d retrievals. Recommendations are made for how this might be achieved. Some existing N d data sets are compared and discussed, and best practices for the use of N d data from current passive instruments (e.g., filtering criteria) are recommended. Emerging alternative N d estimates are also considered. First, new ideas to use additional information from existing and upcoming spaceborne instruments are discussed, and second, approaches using high‐quality ground‐based observations are examined., Key Points Satellite cloud droplet concentration uncertainties of 78% for pixel‐level retrievals and 54% for 1 by 1 degree retrievals are estimatedThe effective radius retrieval is the most important aspect for improvement, and more in situ evaluation is neededPotential improvements using passive and active satellite, and ground‐based instruments are discussed

The Moderate Resolution Imaging Spectroradiometer (MODIS) C6 L3 and the European Centre for Medium‐Range Weather Forecasts (ECMWF) ERA‐Interim reanalysis data from 2003 to 2016 are employed to study aerosol‐cloud correlations over three industrial regions and their adjacent oceans, as well as explore the impact of meteorological conditions on the correlations. The analysis focusing on liquid and single‐layer clouds indicates an opposite aerosol‐cloud correlation between land and ocean; namely, cloud effective radius is positively correlated with aerosol index over industrial regions (positive slopes), but negatively correlated over their adjacent oceans (negative slopes), for a quasi‐constant liquid water path. The positive slopes are relatively large under low lower‐tropospheric stability (LTS; weakly stable condition), but much weaker or even become negative under high LTS (stable conditions) and high liquid water path. The occurrence frequency of cloud top height (CTH) and LTS suggests that positive correlations are more likely corresponding to relatively high CTH and low LTS, while negative to low CTH and high LTS., Key Points Cloud effective radius is positively correlated to aerosol index over three industrial regions, but negatively over adjacent oceansPositive slopes over land are relatively large under low lower‐tropospheric stability (LTS), but much weaker or negative under high LTSPositive correlations possibly correspond to relatively high cloud top height (CTH) and low LTS, while negative to low CTH and high LTS

Satellite‐derived surface soil moisture data are available for the Arctic, but detailed validation is still lacking. Previous studies have shown low correlations between in situ and modeled data. It is hypothesized that soil temperature variations after soil thaw impact MetOp ASCAT satellite‐derived surface soil moisture (SSM) measurements in wet tundra environments, as C band backscatter is sensitive to changes in dielectric properties. We compare in situ measurements of water content within the active layer at four sites across the Arctic in Alaska (Barrow, Sagwon, Toolik) and Siberia (Tiksi), taken in the spring after thawing and in autumn prior to freezing. In addition to the long‐term measurement fields, where sensors are installed deeper in the ground, we designed a monitoring setup for measuring moisture very close to the surface in the Lena River Delta, Siberia. The volumetric water content (VWC) and soil temperature sensors were placed in the moss organic layer in order to account for the limited penetration depth of the radar signal. ASCAT SSM variations are generally very small, in line with the low variability of in situ VWC. Short‐term changes after complete thawing of the upper organic layer, however, seem to be mostly influenced by soil temperature. Correlations between SSM and in situ VWC are generally very low, or even negative. Mean standard deviation matching results in a comparably high root‐mean‐square error (on average 11%) for predictions of VWC. Further investigations and measurement networks are needed to clarify factors causing temporal variation of C band backscatter in tundra regions., Key Points Organic layer temperature variations explain temporal behavior of soil moisture derived from C band radar in unfrozen wet tundraTemporal variability of volumetric water content from in situ point measurements is representative over distances of several kilometers in tundraNear‐surface volumetric water content predicted from matched ASCAT data has an average RMSE of 11% across five sites in Alaska and Siberia

Groundwater is a finite resource under continuous external pressures. Current unsustainable groundwater use threatens the resilience of aquifer systems and their ability to provide a long‐term water source. Groundwater storage is considered to be a factor of groundwater resilience, although the extent to which resilience can be maintained has yet to be explored in depth. In this study, we assess the limit of groundwater resilience in the world's largest groundwater systems with remote sensing observations. The Total Groundwater Stress (TGS) ratio, defined as the ratio of total storage to the groundwater depletion rate, is used to explore the timescales to depletion in the world's largest aquifer systems and associated groundwater buffer capacity. We find that the current state of knowledge of large‐scale groundwater storage has uncertainty ranges across orders of magnitude that severely limit the characterization of resilience in the study aquifers. Additionally, we show that groundwater availability, traditionally defined as recharge and redefined in this study as total storage, can alter the systems that are considered to be stressed versus unstressed. We find that remote sensing observations from NASA's Gravity Recovery and Climate Experiment can assist in providing such information at the scale of a whole aquifer. For example, we demonstrate that a groundwater depletion rate in the Northwest Sahara Aquifer System of 2.69 ± 0.8 km3/yr would result in the aquifer being depleted to 90% of its total storage in as few as 50 years given an initial storage estimate of 70 km3., Key Points: Groundwater resilience is defined and quantified with remote sensing from GRACETimescales of aquifer depletion are assessed as a Total Groundwater Stress ratioThe volume of usable global groundwater storage is found to be largely unknown

Successful climate change mitigation will involve not only technological innovation, but also innovation in how we understand the societal and individual behaviors that shape the demand for energy services. Traditionally, individual energy behaviors have been described as a function of utility optimization and behavioral economics, with price restructuring as the dominant policy lever. Previous research at the macro‐level has identified economic activity, power generation and technology, and economic role as significant factors that shape energy use. However, most demand models lack basic contextual information on how dominant social phenomenon, the changing demographics of cities, and the sociocultural setting within which people operate, affect energy decisions and use patterns. Here we use high‐quality Suomi‐NPP VIIRS nighttime environmental products to: (1) observe aggregate human behavior through variations in energy service demand patterns during the Christmas and New Year's season and the Holy Month of Ramadan and (2) demonstrate that patterns in energy behaviors closely track sociocultural boundaries at the country, city, and district level. These findings indicate that energy decision making and demand is a sociocultural process as well as an economic process, often involving a combination of individual price‐based incentives and societal‐level factors. While nighttime satellite imagery has been used to map regional energy infrastructure distribution, tracking daily dynamic lighting demand at three major scales of urbanization is novel. This methodology can enrich research on the relative importance of drivers of energy demand and conservation behaviors at fine scales. Our initial results demonstrate the importance of seating energy demand frameworks in a social context., Key Points Science‐quality VIIRS night‐sky products can capture short‐term changes in energy service demandWe explore the influence of cultural, socioeconomic, and geopolitical groupings on energy use patternsWe conclude that energy decision‐making and demand is both an individual and sociocultural process