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

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

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

A particular strength of lightning remote sensing is the variety of lightning types observed, each with a unique occurrence context and characteristically different emission. Distinct energetic intracloud (EIC) lightning discharges—compact intracloud lightning discharges (CIDs) and energetic intracloud pulses (EIPs)—produce intense RF radiation, suggesting large currents inside the cloud, and they also have different production mechanisms and occurrence contexts. A Low‐Frequency (LF) lightning remote sensing instrument array was deployed during the RELAMPAGO field campaign in west central Argentina, designed to investigate convective storms that produce high‐impact weather. LF data from the campaign can provide a valuable data set for researching the lightning context of EICs in a variety of subtropical convective storms. This paper describes the production of an LF‐CID data set in RELAMPAGO and includes a preliminary analysis of CID prevalence. Geolocated lightning events and their corresponding observed waveforms from the RELAMPAGO LF data set are used in the classification of EICs. Height estimates based on skywave reflections are computed, where prefit residual data editing is used to improve robustness against outliers. Even if EIPs occurred within the network, given the low number of very high‐peak current events and receiver saturation, automatic classification of EIPs may not be feasible using this data. The classification of CIDs, on the other hand, is straightforward and their properties, for both positive and negative polarity, are investigated. A few RELAMPAGO case studies are also presented, where high variability of CID prevalence in ordinary storms and high‐altitude positive CIDs, possibly in overshooting tops, are observed., Key Points Classification and height estimation of energetic intracloud lightning are investigated using RELAMPAGO Low‐Frequency (LF) lightning waveformsA small number of high‐peak current events and saturation of LF receivers hinder the observation of Energetic In‐Cloud Pulses in RELAMPAGOA catalog of RELAMPAGO Compact Intracloud Discharges is produced to be used in future study of their occurrence in different storm types

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

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

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

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

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

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

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

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

Tropical deep convection can aggregate into large clusters, which can have impacts on the local humidity and precipitation. Sea surface temperature (SST) gradients have been shown to organize convection, yet there has been little work done to investigate the impact of diabatic heating perturbations in the atmosphere on the aggregation of convection. Here we investigate how anomalous diabatic heating of the atmospheric column, through an idealized aerosol plume, affects the existence and mechanisms of convective aggregation in non‐rotating, global radiative‐convective equilibrium simulations. We show that the aerosol forcing has the ability to increase the degree of aggregation, especially at lower SSTs. Detailed investigation shows that the diabatic heating source incites a thermally driven circulation, forced by the shortwave perturbation. The increase in aggregation is caused in part by this circulation, and in part by the longwave heating anomalies occurring due to the surface convergence of moisture and convection. At higher SSTs, longwave feedbacks are crucial for the aggregation of convection, even with the shortwave heating perturbation. At lower SSTs, convection is able to aggregate with the shortwave perturbation in the absence of longwave feedbacks. These perturbations provide a link to studying the effects of absorbing aerosol plumes on convection, for example during the Indian monsoon season. We argue that, as there is aggregation for plumes with realistic aerosol absorption optical depths, this could be an analogue for real‐world organization in regions with high pollution., Key Points Can increase the degree of aggregation by using a shortwave radiative heating perturbationRadiative heating perturbation incites a global thermally driven circulationShortwave radiative heating perturbation can aggregate convection at lower sea surface temperatures, even without typically crucial longwave feedbacks

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

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

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

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

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

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

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

Water resources and water scarcity are usually regarded as local aspects for which a watershed‐based management appears adequate. However, precipitation, as a main source of freshwater, may depend on moisture supplied through land evaporation from outside the watershed. This notion of evaporation as a local “green water” supply to precipitation is typically not considered in hydrological water assessments. Here we propose the concept of a watershed precipitation recycling network, which establishes atmospheric pathways and links land surface evaporation as a moisture supply to precipitation, hence contributing to local but also remote freshwater resources. Our results show that up to 74% of summer precipitation over European watersheds depends on moisture supplied from other watersheds, which contradicts the conventional consideration of autarkic watersheds. The proposed network approach illustrates atmospheric pathways and enables the objective assessment of freshwater vulnerability and water scarcity risks under global change. The illustrated watershed interdependence emphasizes the need for global water governance to secure freshwater availability., Key Points From 9% and up to 74% of summer precipitation over European watersheds is supplied by other European watershedsA watershed precipitation recycling network illustrates the dependencies and key suppliers of freshwater for Europe during summerThe strong interdependence of watersheds through precipitation recycling highlights the need of global water governance

The Southern Hemisphere zonal circulation manifests a downward influence of the stratosphere on the troposphere from late spring to early summer. However, the strength and timescale of the connection, given the stratospheric state, have not been explicitly quantified. Here, SH zonal wind reanalysis time series are analyzed with a methodology designed to detect the minimal set of statistical predictors of multiple interacting variables via conditional independence tests. Our results confirm from data that the variability of the stratospheric polar vortex is a predictor of the tropospheric eddy‐driven jet between September and January. The vortex variability explains about 40% of monthly mean jet variability at a lead time of 1 month and can entirely account for the observed jet persistence. Our statistical model can quantitatively connect the multidecadal trends observed in the vortex and jet during the satellite era. This shows how short‐term variability can help understand statistical links in long‐term changes., Key Points Autocorrelation of stratospheric polar vortex variability inflates persistence of cross correlations with the tropospheric eddy‐driven jetA conditional analysis identifies the vortex as statistical predictor for 40% of monthly jet variability in spring/summerThe inferred statistical model explains the enhanced jet persistence and connects the vortex and jet trends observed in the satellite era

In situ measurements of relative humidity (RH) on Mars have only been performed by the Phoenix (PHX) and Mars Science Laboratory (MSL) missions. Here we present results of our recalibration of the PHX thermal and electrical conductivity probe (TECP) RH sensor. This recalibration was conducted using a TECP engineering model subjected to the full range of environmental conditions at the PHX landing site in the Michigan Mars Environmental Chamber. The experiments focused on the warmest and driest conditions (daytime) because they were not covered in the original calibration (Zent et al., 2010, https://doi.org/10.1029/2009JE003420) and previous recalibration (Zent et al., 2016, https://doi.org/10.1002/2015JE004933). In nighttime conditions, our results are in excellent agreement with the previous 2016 recalibration, while in daytime conditions, our results show larger water vapor pressure values. We obtain vapor pressure values in the range ~0.005–1.4 Pa, while Zent et al. (2016, https://doi.org/10.1002/2015JE004933) obtain values in the range ~0.004–0.4 Pa. Our higher daytime values are in better agreement with independent estimates from the ground by the PHX Surface Stereo Imager instrument and from orbit by Compact Reconnaissance Imaging Spectrometer for Mars. Our results imply larger day‐to‐night ratios of water vapor pressure at PHX compared to MSL, suggesting a stronger atmosphere‐regolith interchange in the Martian arctic than at lower latitudes. Further, they indicate that brine formation at the PHX landing site via deliquescence can be achieved only temporarily between midnight and 6 a.m. on a few sols. The results from our recalibration are important because they shed light on the near‐surface humidity environment on Mars., Key Points We have recalibrated the relative humidity sensor of the Mars Phoenix landerWe obtain water vapor pressure values in the range ~0.005–1.4 Pa, while in previous recalibrations, values in the range ~0.004–0.4 PaOur results show a two‐order‐of‐magnitude diurnal variation of water vapor pressure, suggesting a strong atmosphere‐regolith interchange

This paper describes the initial implementation of a new toolbox that seeks to balance accuracy, efficiency, and flexibility in radiation calculations for dynamical models. The toolbox consists of two related code bases: Radiative Transfer for Energetics (RTE), which computes fluxes given a radiative transfer problem defined in terms of optical properties, boundary conditions, and source functions; and RRTM for General circulation model applications—Parallel (RRTMGP), which combines data and algorithms to map a physical description of the gaseous atmosphere into such a radiative transfer problem. The toolbox is an implementation of well‐established ideas, including the use of a k‐distribution to represent the spectral variation of absorption by gases and the use of two‐stream, plane‐parallel methods for solving the radiative transfer equation. The focus is instead on accuracy, by basing the k‐distribution on state‐of‐the‐art spectroscopy and on the sometimes‐conflicting goals of flexibility and efficiency. Flexibility is facilitated by making extensive use of computational objects encompassing code and data, the latter provisioned at runtime and potentially tailored to specific problems. The computational objects provide robust access to a set of high‐efficiency computational kernels that can be adapted to new computational environments. Accuracy is obtained by careful choice of algorithms and through tuning and validation of the k‐distribution against benchmark calculations. Flexibility with respect to the host model implies user responsibility for maps between clouds and aerosols and the radiative transfer problem, although comprehensive examples are provided for clouds., Key Points RTE+RRTMGP is a new freely available toolbox for radiation calculations for dynamical modelsRTE+RRTMGP seeks to balance accuracy, efficiency, and flexibility, defined expansivelyBoth code and data continue to evolve to explore different balances among these goals

The atmospheric circulation response to global warming is important for accurate prediction of climate change on regional scales. For the midlatitudes, shifts in the extratropical jet streams have important consequences for precipitation, blocking, and extreme events. It has proven to be a challenge, however, to predict. For example, the North Atlantic jet stream plays a vital role in the climate of eastern North America and Europe; in the last intercomparison of state‐of‐the‐art climate models, the models did not even agree on the sign of its wintertime response to global warming. Perhaps this should not come as a surprise, as we also lack a comprehensive theory for the impact of warming on the midlatitude circulation. In a recent study, Tan et al. (2019, https://doi.org/10.1029/2018MS001492) constructed models of simpler atmospheres to explore the response of the midlatitude jet to global warming. Their idealized atmospheres highlight the difficulty of developing a comprehensive theory for the midlatitude circulation but also provide pathways to improve models of Earth's atmosphere. Models of simpler atmospheres allow one to isolate the impact of specific atmospheric processes and connect theoretical understanding with comprehensive climate prediction systems. Such models can also be used to explore very different atmospheric regimes, from Earth's past to distant planets., Key Points The atmospheric circulation response to global warming is both a challenge to predict and to understandModels of idealized atmospheres allow a process‐oriented investigation of the circulation responseA growing number of models of simpler atmospheres are being developed and shared

Tropopause‐penetrating convection is a frequent seasonal feature of the Central United States climate. This convection presents the potential for consistent transport of water vapor into the upper troposphere and lower stratosphere (UTLS) through the lofting of ice, which then sublimates. Water vapor enhancements associated with convective ice lofting have been observed in both in situ and satellite measurements. These water vapor enhancements can increase the probability of sulfate aerosol‐catalyzed heterogeneous reactions that convert reservoir chlorine (HCl and ClONO2) to free radical chlorine (Cl and ClO) that leads to catalytic ozone loss. In addition to water vapor transport, lofted ice may also scavenge nitric acid and further impact the chlorine activation chemistry of the UTLS. We present a photochemical model that resolves the vertical chemical structure of the UTLS to explore the effect of water vapor enhancements and potential additional nitric acid removal. The model is used to define the response of stratospheric column ozone to the range of convective water vapor transported and the temperature variability of the lower stratosphere currently observed over the Central United States in conjunction with potential nitric acid removal and to scenarios of elevated sulfate aerosol surface area density representative of possible future volcanic eruptions or solar radiation management. We find that the effect of HNO3 removal is dependent on the magnitude of nitric acid removal and has the greatest potential to increase chlorine activation and ozone loss under UTLS conditions that weakly favor the chlorine activation heterogeneous reactions by reducing NOx sources., Key Points Photochemical box model defines the vertically resolved response of the stratosphere chemical composition to convective perturbationEffect of nitric acid removal on column ozone loss is dependent on favorability of initial conditions to heterogeneous chlorine activationNitric acid removal has the most impact on column ozone loss under conditions of mild favorability to chlorine activation

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

Estimates of the kinetic energy transfer from the wind to the ocean are often limited by the spatial and temporal resolution of surface currents and surface winds. Here we examine the wind work in a pair of global, very high‐resolution (1/48° and 1/24°) MIT general circulation model simulations in Latitude‐Longitude‐polar Cap (LLC) configuration that provide hourly output at spatial resolutions of a few kilometers and include tidal forcing. A cospectrum analysis of wind stress and ocean surface currents shows positive contribution at large scales (>300 km) and near‐inertial frequency and negative contribution from mesoscales, tidal frequencies, and internal gravity waves. Larger surface kinetic energy fluxes are in the Kuroshio in winter at large scales (40 mW/m2) and mesoscales (−30 mW/m2). The Kerguelen region is dominated by large scale (∼20 mW/m2), followed by inertial oscillations in summer (13 mW/m2) and mesoscale in winter (−12 mW/m2). Kinetic energy fluxes from internal gravity waves (−0.1 to −9.9 mW/m2) are generally stronger in summer. Surface kinetic energy fluxes in the LLC simulations are 4.71 TW, which is 25–85% higher than previous global estimates from coarser (1/6–1/10°) general ocean circulation models; this is likely due to improved representation of wind variability (6‐hourly, 0.14°, operational European Center for Medium‐Range Weather Forecasts). However, the low wind power input to the near‐inertial frequency band obtained with LLC (0.16 TW) compared to global slab models suggests that wind variability on time scales less than 6 hr and spatial scales less than 15 km are critical to better representing the wind power input in ocean circulation models., Key Points Surface kinetic energy fluxes from LLC simulations lead to 4.71 TW, this is 25–85% higher than previous global estimatesThere is positive contribution to wind power input (WPI) at large scale and near‐inertial (NI) band, and negative WPI at mesoscale, tidal frequencies and IGWsLow WPI at NI band (0.16 TW) suggests that wind variability on scales

With increasing temperatures, it is likely that precipitation extremes increase as well. While, on larger spatial and longer temporal scales, the amplification of rainfall extremes often follows the Clausius‐Clapeyron relation, it has been shown that local short‐term convective precipitation extremes may well exceed the Clausius‐Clapeyron rate of around 6.5%/K. Most studies on this topic have focused exclusively on the intensity aspect, while only few have examined (with contradictory results) how warmer and moister conditions modulate the spatial characteristics of convective precipitation extremes and how these connect to increased intensities. Here we study this relation by using a large eddy simulation model. We simulate one diurnal cycle of heavy convective precipitation activity based on a realistic observation‐based strongly forced case setup. Systematically perturbed initial conditions of temperature and specific humidity enable an examination of the response of intensities and spatial characteristics of the precipitation field over an 8° dew point temperature range. We find that warmer and moister conditions result in an overall increase of both intensities and spatial extent of individual rain cells. Colder conditions favor the development of many but smaller rain cells. Under warmer conditions, we find a reduced number of individual cells, but their size significantly grows along with an increase of intensities over a large part of a rain cell. Combined, these factors lead to larger and more intense rain cells that can produce up to almost 20% more rain per degree warming and therefore have a large impact., Key Points Warmer and moister conditions reshape the precipitation field to fewer but larger rain cellsLarger rain cells produce more intense rainRain rates aggregated over a rain cell can increase by up to almost 20% per degree warming

Extremely high temperatures pose an immediate threat to humans and ecosystems. In recent years, many regions on land and in the ocean experienced heat waves with devastating impacts that would have been highly unlikely without human‐induced climate change. Impacts are particularly severe when heat waves occur in regions with high exposure of people or crops. The recent 2018 spring‐to‐summer season was characterized by several major heat and dry extremes. On daily average between May and July 2018 about 22% of the populated and agricultural areas north of 30° latitude experienced concurrent hot temperature extremes. Events of this type were unprecedented prior to 2010, while similar conditions were experienced in the 2010 and 2012 boreal summers. Earth System Model simulations of present‐day climate, that is, at around +1 °C global warming, also display an increase of concurrent heat extremes. Based on Earth System Model simulations, we show that it is virtually certain (using Intergovernmental Panel on Climate Change calibrated uncertainty language) that the 2018 north hemispheric concurrent heat events would not have occurred without human‐induced climate change. Our results further reveal that the average high‐exposure area projected to experience concurrent warm and hot spells in the Northern Hemisphere increases by about 16% per additional +1 °C of global warming. A strong reduction in fossil fuel emissions is paramount to reduce the risks of unprecedented global‐scale heat wave impacts., Key Points Twenty‐two percent of populated and agricultural areas of the Northern Hemisphere concurrently experienced hot extremes between May and July 2018It is virtually certain that these 2018 northhemispheric concurrent heat events could not have occurred without human‐induced climate changeWe would experience a GCWH18‐like event nearly 2 out of 3 years at +1.5 °C and every year at +2 °C global warming

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 past developments in the predictability of weather and climate are discussed from the point of view of nonlinear dynamical systems. The problems ahead for long‐range predictability extending into the climate time scale are also presented. The sensitive dependence of chaos on initial conditions and the imperfections in the models limit reliable predictability of the instantaneous state of the weather to less than 10 days in present‐day operational forecasts. The existence of slowly varying components such as the sea surface temperature, soil moisture, snow cover, and sea ice may provide basis for predicting certain aspects of climate at long range. The regularly varying nonlinear oscillations, such as the Madden‐Julian Oscillation, monsoon intraseasonal oscillations, and El Niño‐Southern Oscillation, are also possible sources of extended‐range predictability at the climate time scale. A prediction model based on phase space reconstruction has demonstrated that monsoon intraseasonal oscillation can be better predicted at long leads., Key Points The predictability of weather forecast models is limited to less than 10 days because of the limit imposed by chaos and model imperfectionsThe existence of slowly varying components of the climate system and regularly varying phenomena provide basis for climate predictabilityA prediction model of monsoon intraseasonal oscillation using phase space reconstruction shows that some aspects of climate can be predicted

There is growing evidence that the atmospheric dynamics of the Euro‐Atlantic sector during winter is driven in part by the presence of quasi‐persistent regimes. However, general circulation models typically struggle to simulate these with, for example, an overly weakly persistent blocking regime. Previous studies have showed that increased horizontal resolution can improve the regime structure of a model but have so far only considered a single model with only one ensemble member at each resolution, leaving open the possibility that this may be either coincidental or model dependent. We show that the improvement in regime structure due to increased resolution is robust across multiple models with multiple ensemble members. However, while the high‐resolution models have notably more tightly clustered data, other aspects of the regimes may not necessarily improve and are also subject to a large amount of sampling variability that typically requires at least three ensemble members to surmount., Key Points Climate models have difficulty representing Euro‐Atlantic regime structure correctlyIncreasing horizontal resolution improves the significance of regime clustering across multiple modelsSpatial patterns and persistence levels of regimes do not necessarily improve with increased resolution

We analyze the atmospheric processes that explain the large changes in radiative feedbacks between the two latest climate configurations of the Hadley Centre Global Environmental model. We use a large set of atmosphere‐only climate change simulations (amip and amip‐p4K) to separate the contributions to the differences in feedback parameter from all the atmospheric model developments between the two latest model configurations. We show that the differences are mostly driven by changes in the shortwave cloud radiative feedback in the midlatitudes, mainly over the Southern Ocean. Two new schemes explain most of the differences: the introduction of a new aerosol scheme and the development of a new mixed‐phase cloud scheme. Both schemes reduce the strength of the preexisting shortwave negative cloud feedback in the midlatitudes. The new aerosol scheme dampens a strong aerosol‐cloud interaction, and it also suppresses a negative clear‐sky shortwave feedback. The mixed‐phase scheme increases the amount of cloud liquid water path (LWP) in the present day and reduces the increase in LWP with warming. Both changes contribute to reducing the negative radiative feedback of the increase of LWP in the warmer climate. The mixed‐phase scheme also enhances a strong, preexisting, positive cloud fraction feedback. We assess the realism of the changes by comparing present‐day simulations against observations and discuss avenues that could help constrain the relevant processes., Key Points Large changes in shortwave cloud feedbacks exist between two recent configurations of Met Office modelChanges in midlatitude feedbacks explain most of the global‐mean differencesThe new aerosol and mixed‐phase schemes explain most of the feedback differences

The terrestrial net biome production (NBP) is considered as one of the major drivers of interannual variation in atmospheric CO2 levels. However, the determinants of variability in NBP under the background climate (i.e., preindustrial conditions) remain poorly understood, especially on decadal‐to‐centennial timescales. We analyzed 1,000‐year simulations spanning 850‐1,849 from the Community Earth System Model (CESM) and found that the variability in NBP and heterotrophic respiration (RH) were largely driven by fluctuations in the net primary production (NPP) and carbon turnover rates in response to climate variability. On interannual to multidecadal timescales, variability in NBP was dominated by variation in NPP, while variability in RH was driven by variation in turnover rates. However, on centennial timescales (100‐1,000 years), the RH variability became more tightly coupled to that of NPP. The NBP variability on centennial timescales was low, due to the near cancellation of NPP and NPP‐driven RH changes arising from climate internal variability and external forcings: preindustrial greenhouse gases, volcanic eruptions, land use changes, orbital change, and solar activity. Factorial experiments showed that globally on centennial timescales, the forcing of changes in greenhouse gas concentrations were the largest contributor (51%) to variations in both NPP and RH, followed by volcanic eruptions impacting NPP (25%) and RH (31%). Our analysis of the carbon‐cycle suggests that geoengineering solutions by injection of stratospheric aerosols might be ineffective on longer timescales., Key Points We analyzed terrestrial carbon fluxes as simulated by an earth system model over the last 1,000 years to study the variability across timescalesVariability in NBP was largely driven by carbon input through NPP, with an increasing contribution from the response of carbon residence time to external forcing at longer timescalesOn centennial timescales, preindustrial greenhouse gases were the dominant forcing of the land carbon cycle, followed by huge volcanic eruptions

The synoptic‐scale meteorological conditions leading up to the 30 most extreme subdaily summer rain events for two regions of the United Kingdom (northwest and southeast) were examined for the period 1979–2013. Using a recently available, quality controlled, national hourly rain gauge data set, we were able to identify extreme 3‐hr rainfall accumulations that may be indicative of flash flooding. Composites of the state of the atmosphere leading up to these dates were produced to investigate synoptic‐scale processes, thus potentially allowing for them to be identified in coarse resolution reanalyses and in climate models. The results show that the two regions have different dominant synoptic‐scale conditions leading to extreme 3‐hr rainfall, which is thought to be related to the type of rainfall typically experienced in each region. In particular, positive anomalies in mean sea level pressure and the geopotential height at 200 hPa over the United Kingdom are associated with extreme rainfall in the northwest, where the position of the westerly jet is also important. For the southeast, no clear anomalous synoptic‐scale conditions could be identified; however, localized moisture sources and unstable air masses were observed in association with extremes. These results indicate the importance of better understanding of both synoptic‐scale and thermodynamic drivers of short‐duration extreme rainfall, with potential implications in forecasting and flood warning, as well as for understanding the representation of key processes by regional climate models., Key Points Different synoptic‐scale meteorological processes dominate on days with JJA subdaily extreme rainfall depending on the region of interestA positive anomaly in the geopotential height is associated with extreme rain events in the northwest of the United KingdomLocalized moisture sources and unstable air masses observed with extreme rain events in the southeast of the United Kingdom

The changing risk of extreme precipitation is difficult to project. Events are rare by definition, and return periods of heavy precipitation events are often calculated assuming a stationary climate. Furthermore, ensembles of climate model projections are not large enough to fully categorize the tails of the distribution. To address this, we cluster the contiguous United States into self‐similar hydroclimates to estimate changes in the expected frequency of extremely rare events under scenarios of global mean temperature change. We find that, although there is some regional variation, record events are projected in general to become more intense, with 500‐year events intensifying by 10–50% under 2 °C of warming and by 40–100% under 4 °C of warming. This analysis could provide information to inform regional prioritization of resources to improve the resilience of U.S. infrastructure., Key Points Spatial clustering can be used to create stable projections of extreme precipitation eventsAnalysis over the United States suggests 1,000‐year events will be up to 5 times more frequent under 2 degree C warmingDifferences in projections between regions are evident, with the U.S. East Coast and mountain regions showing large intensification

Climate change is expected to increase waterborne diseases especially in developing countries. However, we lack understanding of how different types of water sources (both improved and unimproved) are affected by climate change, and thus, where to prioritize future investments and improvements to maximize health outcomes. This is due to limited knowledge of the relationships between source water quality and the observed variability in climate conditions. To address this gap, a 20‐month observational study was conducted in Tanzania, aiming to understand how water quality changes at various types of sources due to short‐term climate variability. Nine rounds of microbiological water quality sampling were conducted for Escherichia coli and total coliforms, at three study sites within different climatic regions. Each round included approximately 233 samples from water sources and 632 samples from households. To identify relationships between water quality and short‐term climate variability, Bayesian hierarchical modeling was adopted, allowing these relationships to vary with source types and sampling regions to account for potentially different physical processes. Across water sources, increases in E. coli/total coliform levels were most closely related to increases in recent heavy rainfall. Our key recommendations to future longitudinal studies are (a) demonstrated value of high sampling frequency and temporal coverage (a minimum of 3 years) especially during wet seasons; (b) utility of the Bayesian hierarchical models to pool data from multiple sites while allowing for variations across space and water sources; and (c) importance of a multidisciplinary team approach with consistent commitment and sharing of knowledge., Key Points We present a longitudinal study in a developing country on water quality changes at a range of water sources under climate variabilityIncreases in E. coli and Total Coliform levels were most closely related to recent heavy rainfallRecommendations for future cross disciplinary studies on drinking water quality and relationships with climate variability are made

Three different methods of specifying ozone in an atmosphere‐only version of the HadGEM3‐A global circulation model are compared to the coupled chemistry configuration of this model. These methods include a specified zonal‐mean ozone climatology, a specified 3‐D ozone climatology, and a calculated‐asymmetry scheme in which a specified zonal‐mean ozone field is adapted online to be consistent with dynamically produced zonal asymmetries. These simulations all use identical boundary conditions and, by construction, have the same climatological zonal‐mean ozone, that of the coupled chemistry configuration of the model. Prescribing ozone, regardless of scheme, results in a simulation which is 3–4 times faster than the coupled chemistry‐climate model (CCM). Prescribing climatological zonal asymmetries leads to a vortex which is the correct intensity but which is systematically displaced over regions with lower prescribed ozone. When zonal asymmetries in ozone are free to evolve interactively with model dynamics, the modeled wintertime stratospheric vortex shape and mean sea level pressure patterns closely resemble that produced by the full CCM in both hemispheres, in terms of statistically significant differences. Further, we separate out the two distinct pathways by which zonal ozone asymmetries influence modeled dynamics. We present this interactive‐ozone zonal‐asymmetry scheme as an inexpensive tool for accurately modeling the impacts of dynamically consistent ozone fields as seen in a CCM which ultimately influence mean sea level pressure and tropospheric circulation (particularly during wintertime in the Northern Hemisphere, when ozone asymmetries are generally largest), without the computational burden of simulating interactive chemistry., Key Points Dynamical feedbacks resulting from ozone zonal asymmetries are important for accurately representing Northern Hemisphere circulation patterns during wintertimeThree simulations using different methods of specifying ozone mixing ratios are compared with a fully coupled CCM simulation in order to examine the pathways by which zonal ozone asymmetries influence modeled dynamicsDynamical feedbacks occurring in a coupled CCM can be reproduced, at a greatly reduced computational cost, by appropriately placing a prescribed ozone climatology onto a dynamically evolving coordinate

Vb cyclones typically emerge in the Western Mediterranean and propagate to the Northeast into Central Europe. This paper explores the temporal characteristics of Vb cyclone occurrence based on cyclone tracks identified at the atmospheric levels of Z700 and sea level pressure, using JRA‐55 reanalysis data for the period 1959–2015. The risk of Vb occurrence was significantly high in the 1960s and has remained at a lower level since then. Vb cyclones do not occur fully randomly according to a Poisson point process. Eleven well‐separated and distinct clusters as well as 11 hiatus periods are identified, with average occurrence rates of 21.5 and 5.2 yrea−1, respectively. During the event of Vb, the large‐scale atmospheric circulation is changed into a state favoring the development of successive Vb cyclones. Clustering is very prominent in the case of Genoan Vb cyclones in summer as well as those Vb cyclones developing over the Iberian Peninsula or the North African Coast in winter. Superposition of the polar and the subtropical jet stream over the Western Mediterranean is identified as a main feature at the onset of Vb cyclones. Vb cyclone occurrence appears to be synchronized with the Northern Atlantic Oscillation (NAO; at Z500) and Arctic Oscillation (AO; at Z1000). Clusters have occurred when both NAO and AO were negative. This relation applies to Western Mediterranean cyclones not following a Vb track as well, however to a much weaker extent. In contrast, Vb cyclone frequency was particularly low from 1988 to 1997 during a sustained positive phase of both NAO and AO., Key Points In the past 50 years, the frequency of Vb cyclones was high in the 1960s and lower since thenVb cyclones were strongly synchronized with NAO and AO; high frequency clusters occurred when NAO and AO were negativeThe coupling of the polar and the subtropical jet stream over the Western Mediterranean is a driving mechanism of the onset of Vb cyclones

The NASA Cyclone Global Navigation Satellite System (CYGNSS) was launched in December 2016, providing an unprecedented opportunity to obtain ocean surface wind speeds including wind estimates over the hurricane inner‐core region. This study demonstrates the influence of assimilating an early version of CYGNSS observations of ocean surface wind speeds on numerical simulations of two notable landfalling hurricanes, Harvey and Irma (2017). A research version of the National Centers for Environmental Prediction operational Hurricane Weather Research and Forecasting model and the Gridpoint Statistical Interpolation‐based hybrid ensemble three‐dimensional variational data assimilation system are used. It is found that the assimilation of CYGNSS data results in improved track, intensity, and structure forecasts for both hurricane cases, especially for the weak phase of a hurricane, implying potential benefits of using such data for future research and operational applications., Key Points The NASA Cyclone Global Navigation Satellite System (CYGNSS) provides an unprecedented opportunity to obtain ocean surface wind data over a hurricane inner‐core regionThis study found that the assimilation of CYGNSS data results in improved track, intensity, and structure forecasts for two notable landfalling hurricanes, Harvey and Irma (2017)

The ability of FLake, WRF‐Lake, and CoLM‐Lake models in simulating the thermal features of Lake Nam Co in Central Tibetan Plateau has been evaluated in this study. All the three models with default settings exhibited distinct errors in the simulated vertical temperature profile. Then model calibration was conducted by adjusting three (four) key parameters within FLake and CoLM‐Lake (WRF‐Lake) in a series of sensitive experiments. Results showed that each model's performance is sensitive to the key parameters and becomes much better when adjusting all the key parameters relative to tuning single parameter. Overall, setting the temperature of maximum water density to 1.1 °C instead of 4 °C in the three models consistently leads to improved vertical thermal structure simulation during cold seasons; reducing the light extinction coefficient in FLake results in much deeper mixed layer and warmer thermocline during warm seasons in better agreement with the observation. The vertical thermal structure can be clearly improved by decreasing the light extinction coefficient and increasing the turbulent mixing in WRF‐Lake and CoLM‐Lake during warm seasons. Meanwhile, the modeled water temperature profile in warm seasons can be significantly improved by further replacing the constant surface roughness lengths by a parameterized scheme in WRF‐Lake. Further intercomparison indicates that among the three calibrated models, FLake (WRF‐Lake) performs the best to simulate the temporal evolution and intensity of temperature in the layers shallower (deeper) than 10 m, while WRF‐Lake is the best at simulating the amplitude and pattern of the temperature variability at all depths., Key Points The performance of three one‐dimensional lake models in simulating the thermal structure of Nam Co Lake is evaluated and improvedKey processes related to the simulated thermal regime of alpine lakes on the Tibetan Plateau are indicated and revealed

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