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We use the Weather Research and Forecast model using the Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) dust module (WRF‐CHEM) to simulate the particulate matter (PM) variations in the Sahel during the winter seasons (January–March) of 1960–2014. Two simulations are undertaken where the direct aerosol feedback is turned off, and only transport is considered and where the direct aerosol feedback is turned on. We find that simulated Sahelian PM10 and PM2.5 concentrations were lower in the 1960s and after 2003 and higher during the period between 1988 and 2002. Higher Sahelian PM10 concentrations are due to stronger winds between the surface and 925 hPa over the Sahara, which transport dust into the Sahel. Negative PM10 concentration anomalies are found over the Bodele Depression and associated with weaker 925 wind anomalies after 1997 through 2014. Further west, positive PM10 concentration anomalies are found across the Adrar Plateau in the Sahara and responsible for dust transport to the Western Sahel. The North Atlantic Oscillation (NAO) is positively correlated to Sahelian dust concentrations especially during the periods of 1960–1970 and 1988–2002. The temporal/spatial patterns of PM10 concentrations have significant respiratory health implications for inhabitants of the Sahel., Key Points Decadal variations in Sahelian PM10 and PM2.5 concentrations are strongly related to Saharan wind speed magnitude anomalies between the surface and 925 hPaThe highest correlations among the North Atlantic Oscillation and simulated Sahelian and Saharan PM10 seasonal anomalies between 1960–1970 and 1988–2002 are observedImproved ambient air pollution and health monitoring is a critical need for determining the connections between particulate matter and respiratory disease in the Sahel

Cold pools influence convective initiation and organization, dust lofting, and boundary layer properties, but little is known about their interactions with the land surface, particularly in dry continental environments. In this study, two‐way cold pool‐land surface interactions are investigated using high‐resolution idealized simulations of an isolated, transient cold pool evolving in a dry convective boundary layer. Results using a fully interactive land surface demonstrate that sensible heat fluxes are suppressed at the center of the cold pool but enhanced at the edge due to the spatial patterns of land surface cooling and the air temperature and wind speed perturbations. This leads to cold pool dissipation from the edge inward. Latent heat fluxes are primarily suppressed within the cold pool, and the magnitude of this suppression is controlled by competition between atmospheric and land surface effects. By comparing the fully interactive land surface simulation to a simulation with imposed surface fluxes, the land surface‐cold pool feedbacks are shown to reduce the cold pool lifetime, extent, and intensity by up to 50% and influence the pattern of boundary layer turbulent kinetic energy recovery, which have significant implications for cold pool‐induced convective initiation., Key Points An isolated cold pool evolving in a dry convective boundary layer is simulated at high resolutionSensible heat fluxes are enhanced at the cold pool's edge and suppressed in the center, leading to dissipation from the edge inwardLand surface interactions reduce the cold pool lifetime, area, and intensity by up to 50% and affect the boundary layer evolution

Bursts of X‐rays and γ‐rays are observed from lightning and laboratory sparks. They are bremsstrahlung from energetic electrons interacting with neutral air molecules, but it is still unclear how the electrons achieve the required energies. It has been proposed that the enhanced electric field of streamers, found in the corona of leader tips, may account for the acceleration; however, their efficiency is questioned because of the relatively low production rate found in simulations. Here we emphasize that streamers usually are simulated with the assumption of homogeneous gas, which may not be the case on the small temporal and spatial scales of discharges. Since the streamer properties strongly depend on the reduced electric field E/n, where n is the neutral number density, fluctuations may potentially have a significant effect. To explore what might be expected if the assumption of homogeneity is relaxed, we conducted simple numerical experiments based on simulations of streamers in a neutral gas with a radial gradient in the neutral density, assumed to be created, for instance, by a previous spark. We also studied the effects of background electron density from previous discharges. We find that X‐radiation and γ‐radiation are enhanced when the on‐axis air density is reduced by more than ∼25%. Pre‐ionization tends to reduce the streamer field and thereby the production rate of high‐energy electrons; however, the reduction is modest. The simulations suggest that fluctuations in the neutral densities, on the temporal and spacial scales of streamers, may be important for electron acceleration and bremsstrahlung radiation., Key Points Air perturbations significantly increase the velocity of streamer frontsAir perturbations facilitate the emission of X‐rays from streamer dischargesPre‐ionization moderately lowers the maximum energy of electrons and photons

Understanding the context from which evidence emerges is of paramount importance in reaching robust conclusions in scientific inquiries. This is as true of the present as it is of the past. In a trans‐disciplinary study such as More et al. (2017, https://doi.org/10.1002/2017GH000064) and many others appearing in this and similar journals, a proper analysis of context demands the use of historical evidence. This includes demographic, epidemiological, and socio‐economic data—common in many studies of the impact of anthropogenic pollution on human health—and, as in this specific case, also geoarchaeological evidence. These records anchor climate and pollution data in the geographic and human circumstances of history, without which we lose a fundamental understanding of the data itself. This article addresses Hinkley (2018, https://doi.org/10.1002/2017GH000105) by highlighting the importance of context, focusing on the historical and archaeological evidence, and then discussing atmospheric deposition and circulation in the specific region of our study. Since many of the assertions in Bindler (2018, https://doi.org/10.1002/2018GH000135) are congruent with our findings and directly contradict Hinkley (2018), this reply refers to Bindler (2018), whenever appropriate, and indicates where our evidence diverges., Key Points Historical and archaeological evidence and context are crucially important in understanding past health, climate, and pollution dataGeographic context and proximity to sources of lead (Pb) mining and smelting have a significant impact on the ice core record

The evolution of near‐surface air temperature is influenced by various dynamical, radiative, and surface‐atmosphere exchange processes whose contributions are still not completely quantified. Applying stepwise multiple linear regression to Coupled Model Intercomparison Project phase 5 (CMIP5) model simulations and focusing on radiation (diagnosed by incoming shortwave and incoming longwave radiation) and land surface conditions (diagnosed by soil moisture and albedo) about 79% of the interannual variability and 99% of the multidecadal trend of monthly mean daily maximum temperature over land can be explained. The linear model captures well the temperature variability in middle‐to‐high latitudes and in regions close to the equator, whereas its explanatory potential is limited in deserts. While radiation is an essential explanatory variable over almost all of the analyzed domain, land surface conditions show a pronounced relation to temperature in some confined regions. These findings highlight that considering local‐to‐regional processes is crucial for correctly assessing interannual temperature variability and future temperature trends., Key Points The used linear regression model explains about 79% of interannual temperature variability and 99% of multidecadal temperature trendRadiation explains on average about 75% to 85% of the total explained variance, while land surface conditions contribute about 15% to 25%Radiation is essential for explaining temperature variability almost everywhere, land surface conditions in some confined regions

An anelastic numerical model is employed to explore the dynamics of gravity waves (GWs) encountering a mesosphere inversion layer (MIL) having a moderate static stability enhancement and a layer of weaker static stability above. Instabilities occur within the MIL when the GW amplitude approaches that required for GW breaking due to compression of the vertical wavelength accompanying the increasing static stability. Thus, MILs can cause large‐amplitude GWs to yield instabilities and turbulence below the altitude where they would otherwise arise. Smaller‐amplitude GWs encountering a MIL do not lead to instability and turbulence but do exhibit partial reflection and transmission, and the transmission is a smaller fraction of the incident GW when instabilities and turbulence arise within the MIL. Additionally, greater GW transmission occurs for weaker MILs and for GWs having larger vertical wavelengths relative to the MIL depth and for lower GW intrinsic frequencies. These results imply similar dynamics for inversions due to other sources, including the tropopause inversion layer, the high stability capping the polar summer mesopause, and lower frequency GWs or tides having sufficient amplitudes to yield significant variations in stability at large and small vertical scales. MILs also imply much stronger reflections and less coherent GW propagation in environments having significant fine structure in the stability and velocity fields than in environments that are smoothly varying., Key Points Gravity waves exhibit complex dynamics in mesospheric inversion layersResponses include trapping, suppressed transmission, reflection, and enhanced shears, instabilities, and dissipationMIL penetration is enhanced for larger vertical wavelengths and lower intrinsic frequencies; larger GW amplitudes enable instabilities

We compared the modeled energy spectral density of very low frequency (VLF) radio emissions from terrestrial gamma ray flashes (TGFs) with the energy spectral density of VLF radio sferics recorded by Duke VLF receiver simultaneously with those TGFs. In total, six events with world wide lightning location network (WWLLN) defined locations were analyzed to exhibit a good fit between the modeled and observed energy spectral densities. In VLF range the energy spectral density of the TGF source current moment is found to be dominated by the contribution of secondary low-energy electrons and independent of the relativistic electrons which play their role in low-frequency (LF) range. Additional spectral modulation by the multiplicity of TGF peaks was found and demonstrated a good fit for two TGFs whose VLF sferics consist of two overlapping pulses each. The number of seeding pulses in TGF defines the spectral shape in VLF range, which allows to retrieve this number from VLF sferics, assuming they were radiated by TGFs. For two events it was found that the number of seeding pulses is small, of the order of 10. For the rest of the events the lower boundary of the number of seeding pulses was found to be between 10 to 10.©2017. The Authors., This study was supported by the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement 320839 and the Research Council of Norway under contracts 208028/F50 and 223252/F50 (CoE). The contributions of SAC were supported by NSF grant ATM-1565606. The work of FJPI was supported by the Spanish Ministry of Science and Innovation, MINECO under projects ESP2015-69909-C5-2-R and FIS2014-61774-EXP. F.J.P.I. acknowledges a PhD research contract, code BES-2014-069567.

An Airbus A340 aircraft flew over Northern Australia with the In‐Flight Lightning Damage Assessment System (ILDAS) installed onboard. A long‐duration gamma ray emission was detected. The most intense emission was observed at 12 km altitude and lasted for 20 s. Its intensity was 20 times the background counts, and it was abruptly terminated by a distant lightning flash. In this work we reconstruct the aircraft path and event timeline. The glow‐terminating flash triggered a discharge from the aircraft wing that was recorded by a video camera operating onboard. Another count rate increase was observed 6 min later and lasted for 30 s. The lightning activity as reported by ground networks in this region was analyzed. The measured spectra characteristics of the emission were estimated., Key Points Two gamma ray glows were observed at 12 km altitude over Northern AustraliaThe observation was conducted by the In‐flight Lightning Damage Assessment System (ILDAS)Gamma ray glow was terminated by a distant lightning flash

NASA's Goddard Earth Observing System (GEOS) Earth System Model (ESM) is a modular, general circulation model (GCM), and data assimilation system (DAS) that is used to simulate and study the coupled dynamics, physics, chemistry, and biology of our planet. GEOS is developed by the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center. It generates near‐real‐time analyzed data products, reanalyses, and weather and seasonal forecasts to support research targeted to understanding interactions among Earth System processes. For chemistry, our efforts are focused on ozone and its influence on the state of the atmosphere and oceans, and on trace gas data assimilation and global forecasting at mesoscale discretization. Several chemistry and aerosol modules are coupled to the GCM, which enables GEOS to address topics pertinent to NASA's Earth Science Mission. This paper describes the atmospheric chemistry components of GEOS and provides an overview of its Earth System Modeling Framework (ESMF)‐based software infrastructure, which promotes a rich spectrum of feedbacks that influence circulation and climate, and impact human and ecosystem health. We detail how GEOS allows model users to select chemical mechanisms and emission scenarios at run time, establish the extent to which the aerosol and chemical components communicate, and decide whether either or both influence the radiative transfer calculations. A variety of resolutions facilitates research on spatial and temporal scales relevant to problems ranging from hourly changes in air quality to trace gas trends in a changing climate. Samples of recent GEOS chemistry applications are provided., Key Points The GEOS Earth System Model's architecture is based on ESMF and the GMAO's middleware layer called MAPLGEOS uses a common code base of interchangeable chemical components for data assimilation, forecasting, and researchGEOS is applied to atmospheric chemistry topics ranging from diurnal air quality variations to climate change

The processes that lead to changes in the propagation and maintenance of the Madden‐Julian Oscillation (MJO) as a response to increasing CO2 are examined by analyzing moist static energy budget of the MJO in a series of NASA GISS model simulations. It is found changes in MJO propagation is dominated by several key processes. Horizontal moisture advection, a key process for MJO propagation, is found to enhance predominantly due to an increase in the mean horizontal moisture gradients. The terms that determine the strength of the advecting wind anomalies, the MJO horizontal scale and the dry static stability, are found to exhibit opposing trends that largely cancel out. Furthermore, reduced sensitivity of precipitation to changes in column moisture, i.e., a lengthening in the convective moisture adjustment time scale, also opposes enhanced propagation. The dispersion relationship of Adames and Kim, which accounts for all these processes, predicts an acceleration of the MJO at a rate of ∼3.5% K−1, which is consistent with the actual phase speed changes in the simulation. For the processes that contribute to MJO maintenance, it is found that damping by vertical MSE advection is reduced due to the increasing vertical moisture gradient. This weaker damping is nearly canceled by weaker maintenance by cloud‐radiative feedbacks, yielding the growth rate from the linear moisture mode theory nearly unchanged with the warming. Furthermore, the estimated growth rates are found to be a small, negative values, suggesting that the MJO in the simulation is a weakly damped mode., Key Points The moisture mode framework of Adames and Kim is used to understand the MJO's response to increasing CO2 in the GISS GCMThe moisture mode framework successfully predicts the rate of MJO's phase speed increase with the warmingThe acceleration of the MJO in a warmer climate is due to the changes in the mean state, moisture‐convection coupling, and the MJO's scale

Multimodel ensembles are widely analyzed to estimate the range of future regional climate change projections. For an ensemble of climate models, the result is often portrayed by showing maps of the geographical distribution of the multimodel mean results and associated uncertainties represented by model spread at the grid point scale. Here we use a set of CMIP5 models to show that presenting statistics this way results in an overestimation of the projected range leading to physically implausible patterns of change on global but also on regional scales. We point out that similar inconsistencies occur in impact analyses relying on multimodel information extracted using statistics at the regional scale, for example, when a subset of CMIP models is selected to represent regional model spread. Consequently, the risk of unwanted impacts may be overestimated at larger scales as climate change impacts will never be realized as the worst (or best) case everywhere., Key Points The spread in projected climate change is inflated at the global scale when multimodel ensembles are analyzed using grid point statisticsIf the largest regional changes projected by the model ensemble are selected in each region, this is not consistent at the global scaleImpact analyses based on grid point statistics may indicate a risk of unwanted future impacts that are not physically realizable

The recently developed maximum entropy production (MEP) model, an alternative parameterization of surface heat fluxes, is incorporated into the Weather Research and Forecasting (WRF) model. A pair of WRF cloud‐resolving experiments (5 km grids) using the bulk transfer model (WRF default) and the MEP model of surface heat fluxes are performed to hindcast the October Madden‐Julian oscillation (MJO) event observed during the 2011 Dynamics of the MJO (DYNAMO) field campaign. The simulated surface latent and sensible heat fluxes in the MEP and bulk transfer model runs are in general consistent with in situ observations from two research vessels. Compared to the bulk transfer model, the convection envelope is strengthened in the MEP run and shows a more coherent propagation over the Maritime Continent. The simulated precipitable water in the MEP run is in closer agreement with the observations. Precipitation in the MEP run is enhanced during the active phase of the MJO with significantly reduced regional dry and wet biases. Large‐scale ocean evaporation is stronger in the MEP run leading to stronger boundary layer moistening to the east of the convection center, which facilitates the eastward propagation of the MJO., Key Points The new surface heat fluxes parameterization based upon maximum entropy production (MEP) produces stronger MJO convection in the WRF modelThe enhanced total heat fluxes in the MEP model result in a closer agreement of precipitable water and precipitation with the observationLocal precipitation biases in the default WRF are significantly reduced through enhanced (reduced) heavy (light) rain in the WRF‐MEP model

Ocean surface warming is commonly associated with a more stratified, less productive, and less oxygenated ocean. Such an assertion is mainly based on consistent projections of increased near‐surface stratification and shallower mixed layers under global warming scenarios. However, while the observed sea surface temperature (SST) is rising at midlatitudes, the concurrent ocean record shows that stratification is not unequivocally increasing nor is MLD shoaling. We find that while SST increases at three study areas at midlatitudes, stratification both increases and decreases, and MLD deepens with enhanced deepening of winter MLDs at rates over 10 m decade−1. These results rely on the estimation of several MLD and stratification indexes of different complexity on hydrographic profiles from long‐term hydrographic time‐series, ocean reanalysis, and Argo floats. Combining this information with estimated MLDs from buoyancy fluxes and the enhanced deepening/attenuation of the winter MLD trends due to changes in the Ekman pumping, MLD variability involves a subtle interplay between circulation and atmospheric forcing at midlatitudes. Besides, it is highlighted that the density difference between the surface and 200 m, the most widely used stratification index, should not be expected to reliably inform about changes in the vertical extent of mixing., Key Points Ocean observations show that surface warming is not definitively linked to a more stratified and less mixed oceanSubtle interplay between changes in circulation and atmospheric forcing contribute to find warmer and deeper MLDsHow our findings reconcile with the observation of a less productive and oxygenated ocean is discussed

Difficulties in obtaining accurate precipitation measurements have limited meaningful hydrologic assessment for over a century due to performance challenges of conventional snowfall and rainfall gauges in windy environments. Here, we compare snowfall observations and bias adjusted snowfall to end‐of‐winter snow accumulation measurements on the ground for 16 years (1999–2014) and assess the implication of precipitation underestimation on the water balance for a low‐gradient tundra wetland near Utqiagvik (formerly Barrow), Alaska (2007–2009). In agreement with other studies, and not accounting for sublimation, conventional snowfall gauges captured 23–56% of end‐of‐winter snow accumulation. Once snowfall and rainfall are bias adjusted, long‐term annual precipitation estimates more than double (from 123 to 274 mm), highlighting the risk of studies using conventional or unadjusted precipitation that dramatically under‐represent water balance components. Applying conventional precipitation information to the water balance analysis produced consistent storage deficits (79 to 152 mm) that were all larger than the largest actual deficit (75 mm), which was observed in the unusually low rainfall summer of 2007. Year‐to‐year variability in adjusted rainfall (±33 mm) was larger than evapotranspiration (±13 mm). Measured interannual variability in partitioning of snow into runoff (29% in 2008 to 68% in 2009) in years with similar end‐of‐winter snow accumulation (180 and 164 mm, respectively) highlights the importance of the previous summer's rainfall (25 and 60 mm, respectively) on spring runoff production. Incorrect representation of precipitation can therefore have major implications for Arctic water budget descriptions that in turn can alter estimates of carbon and energy fluxes., Key Points Water storage deficits are consistently overestimated when precipitation is not adjusted for underestimationEnd‐of‐snowmelt through July rainfall is a more effective measure of drought conditions than total warm season rainfallPrecipitation bias lead to underestimated snowmelt runoff, due to overestimated antecedent water storage deficits and underestimated snowfall

The current knowledge about spatial and temporal dynamics of the Black Sea's thermohaline structure is incomplete because of missing data and sparse distribution of existing measurements in space and time. This study presents 56 year continuous simulations of the Black Sea's hydrodynamics using the 3D General Estuarine Transport Model (GETM), without incorporating any relaxation toward climatological or observational data fields. This property of the model allows us to estimate independent temporal trends, in addition to resolving the spatial structure. The simulations suggest that the intermediate layer temperature is characterized by a weak positive trend (warming), whereas the surface temperature does not show a clear linear trend. Different salinity trends have been established at the surface (negative), upper (weaker negative) and main halocline (positive). Three distinct dynamic periods are identified (1960–1970, 1970–1995, 1995–2015), which exhibit pronounced changes in the Black Sea's thermohaline properties and basin circulation. Strengthening of the main cyclonic circulation, accompanied by intensification of the mesoscale anticyclonic eddy formation is found. Both events strongly affect the sea surface salinity but contribute in opposing directions. Specifically, strong composite large‐scale circulation leads to an increase in sea surface salinity, while enhanced formation of mesoscale anticyclones decreases it. Salinity evolution with time is thus the result of the competition of these two opposing yet interdependent processes., Key Points Long‐term evolution of the Black Sea thermohaline properties based on a fully prognostic model is simulatedChanges in basin‐scale and mesoscale circulations are shown to be an important factor for the surface salinity variationTrends in thermohaline properties over specific time periods are analyzed

Stratocumulus clouds are the most common type of boundary layer cloud; their radiative effects strongly modulate climate. Large eddy simulations (LES) of stratocumulus clouds often struggle to maintain fidelity to observations because of the sharp gradients occurring at the entrainment interfacial layer at the cloud top. The challenge posed to LES by stratocumulus clouds is evident in the wide range of solutions found in the LES intercomparison based on the DYCOMS‐II field campaign, where simulated liquid water paths for identical initial and boundary conditions varied by a factor of nearly 12. Here we revisit the DYCOMS‐II RF01 case and show that the wide range of previous LES results can be realized in a single LES code by varying only the numerical treatment of the equations of motion and the nature of subgrid‐scale (SGS) closures. The simulations that maintain the greatest fidelity to DYCOMS‐II observations are identified. The results show that using weighted essentially non‐oscillatory (WENO) numerics for all resolved advective terms and no explicit SGS closure consistently produces the highest‐fidelity simulations. This suggests that the numerical dissipation inherent in WENO schemes functions as a high‐quality, implicit SGS closure for this stratocumulus case. Conversely, using oscillatory centered difference numerical schemes for momentum advection, WENO numerics for scalars, and explicitly modeled SGS fluxes consistently produces the lowest‐fidelity simulations. We attribute this to the production of anomalously large SGS fluxes near the cloud tops through the interaction of numerical error in the momentum field with the scalar SGS model., Key Points Fidelity of LES simulations of stratocumulus clouds depends on advection schemes and SGS closuresWENO schemes without explicit SGS closures simulate stratocumulus wellA simple model shows how advection schemes and SGS closures interact and affect cloud simulations

Contrary to widespread assumptions, next‐generation high (annual to multiannual) and ultra‐high (subannual) resolution analyses of an Alpine glacier reveal that true historical minimum natural levels of lead in the atmosphere occurred only once in the last ~2000 years. During the Black Death pandemic, demographic and economic collapse interrupted metal production and atmospheric lead dropped to undetectable levels. This finding challenges current government and industry understanding of preindustrial lead pollution and its potential implications for human health of children and adults worldwide. Available technology and geographic location have limited previous ice core investigations. We provide new high‐ (discrete, inductively coupled plasma mass spectrometry, ICP‐MS) and ultra‐high resolution (laser ablation inductively coupled plasma mass spectrometry, LA‐ICP‐MS) records of atmospheric lead deposition extracted from the high Alpine glacier Colle Gnifetti, in the Swiss‐Italian Alps. We show that contrary to the conventional wisdom, low levels at or approaching natural background occurred only in a single 4 year period in ~2000 years documented in the new ice core, during the Black Death (~1349–1353 C.E.), the most devastating pandemic in Eurasian history. Ultra‐high chronological resolution allows for the first time detailed and decisive comparison of the new glaciochemical data with historical records. Historical evidence shows that mining activity ceased upwind of the core site from ~1349 to 1353, while concurrently on the glacier lead (Pb) concentrations—dated by layer counting confirmed by radiocarbon dating—dropped to levels below detection, an order of magnitude beneath figures deemed low in earlier studies. Previous assumptions about preindustrial “natural” background lead levels in the atmosphere—and potential impacts on humans—have been misleading, with significant implications for current environmental, industrial, and public health policy, as well as for the history of human lead exposure. Trans‐disciplinary application of this new technology opens the door to new approaches to the study of the anthropogenic impact on past and present human health., Key Points Preindustrial, atmospheric lead (Pb) levels have been grossly underestimated, with significant implications for human health and developmentOverwhelming historical evidence shows that catastrophic demographic collapse caused atmospheric Pb to plummet to natural levels only once in the last ~2000 yearsNext‐generation ice core analysis by laser ablation inductively coupled plasma mass spectrometry allows for the first time an ultra‐high resolution (subannual) record of Pb deposition

Large uncertainty in the predicted intensity of tropical cyclones (TCs) persists compared to the steadily improving skill in the predicted TC tracks. This intensity uncertainty has its most significant implications in the coastal zone, where TC impacts to populated shorelines are greatest. Recent studies have demonstrated that rapid ahead‐of‐eye‐center cooling of a stratified coastal ocean can have a significant impact on hurricane intensity forecasts. Using observation‐validated, high‐resolution ocean modeling, the stratified coastal ocean cooling processes observed in two U.S. Mid‐Atlantic hurricanes were investigated: Hurricane Irene (2011)—with an inshore Mid‐Atlantic Bight (MAB) track during the late summer stratified coastal ocean season—and Tropical Storm Barry (2007)—with an offshore track during early summer. For both storms, the critical ahead‐of‐eye‐center depth‐averaged force balance across the entire MAB shelf included an onshore wind stress balanced by an offshore pressure gradient. This resulted in onshore surface currents opposing offshore bottom currents that enhanced surface to bottom current shear and turbulent mixing across the thermocline, resulting in the rapid cooling of the surface layer ahead‐of‐eye‐center. Because the same baroclinic and mixing processes occurred for two storms on opposite ends of the track and seasonal stratification envelope, the response appears robust. It will be critical to forecast these processes and their implications for a wide range of future storms using realistic 3‐D coupled atmosphere‐ocean models to lower the uncertainty in predictions of TC intensities and impacts and enable coastal populations to better respond to increasing rapid intensification threats in an era of rising sea levels., Key Points Observations, models reveal similar processes governing stratified coastal ocean cooling response to Hurricane Irene, Tropical Storm BarryRobust shear‐induced mixing produced rapid ahead‐of‐eye‐center cooling in TCs on opposite ends of track and seasonal stratification envelopeCoupled TC models capable of predicting processes leading to rapid cooling of stratified coastal oceans critical for populated coastlines

The tide‐gauge record from the North American East Coast reveals significant accelerations in sea level starting in the late twentieth century. The estimated post‐1990 accelerations range from near zero to ∼0.3 mm yr−2. We find that the observed sea level acceleration is well modeled using several processes: mass change in Greenland and Antarctica as measured by the Gravity Recovery and Climate Experiment satellites; ocean dynamic and steric variability provided by the GECCO2 ocean synthesis; and the inverted barometer effect. However, to achieve this fit requires estimation of an admittance for the dynamical and steric contribution, possibly due to the coarse resolution of this analysis or to simplifications associated with parameterization of bottom friction in the shallow coastal areas. The acceleration from ice loss alone is equivalent to a regional sea level rise in one century of 0.2 m in the north and 0.75 m in the south of this region., Key Points Understanding sea level acceleration requires consideration of multiple spatially varying physical processesRecent acceleration is well modeled using Greenland and Antarctic ice loss and ocean dynamicsAcceleration from ice loss alone translates to regional rise of 0.2–0.75 m in one century, depending on location

Climate models have consistently projected a drying trend in the southwestern United States, aiding speculation of increasing dust storms in this region. Long‐term climatology is essential to documenting the dust trend and its response to climate variability. We have reconstructed long‐term dust climatology in the western United States, based on a comprehensive dust identification method and continuous aerosol observations from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network. We report here direct evidence of rapid intensification of dust storm activity over American deserts in the past decades (1988–2011), in contrast to reported decreasing trends in Asia and Africa. The frequency of windblown dust storms has increased 240% from 1990s to 2000s. This dust trend is associated with large‐scale variations of sea surface temperature in the Pacific Ocean, with the strongest correlation with the Pacific Decadal Oscillation. We further investigate the relationship between dust and Valley fever, a fast‐rising infectious disease caused by inhaling soil‐dwelling fungus (Coccidioides immitis and C. posadasii) in the southwestern United States. The frequency of dust storms is found to be correlated with Valley fever incidences, with a coefficient (r) comparable to or stronger than that with other factors believed to control the disease in two endemic centers (Maricopa and Pima County, Arizona)., Key Points The frequency of locally originated windblown dust storms has increased 240% in 1990‐2011 in the Southwest United StatesThe dust trends are likely driven by large‐scale variations of sea surface temperature in the Pacific OceanThe dust trend is spatially and temporally correlated with incidences of valley fever, an infectious disease caused by soil‐dwelling fungus

Past observations and modeling of Jupiter's thermosphere have, due to their limited resolution, suggested that heat generated by the aurora near the poles results in a smooth thermal gradient away from these aurorae, indicating a quiescent and diffuse flow of energy within the subauroral thermosphere. Here we discuss Very Large Telescope‐Cryogenic High‐Resolution IR Echelle Spectrometer observations that reveal a small‐scale localized cooling of ~200 K within the nonauroral thermosphere. Using Infrared Telescope Facility NSFCam images, this feature is revealed to be quasi‐stable over at least a 15 year period, fixed in magnetic latitude and longitude. The size and shape of this “Great Cold Spot” vary significantly with time, strongly suggesting that it is produced by an aurorally generated weather system: the first direct evidence of a long‐term thermospheric vortex in the solar system. We discuss the implications of this spot, comparing it with short‐term temperature and density variations at Earth., Key Points The detection of a Great Cold Spot in Jupiter's upper atmosphere, which is quasi‐stable over a 15 year periodThe Great Cold Spot is approximately fixed in magnetic latitude and longitude but changes in shape and size on moderate timescalesWe interpret this as direct evidence of the first long‐term thermospheric vortex in the solar system: an aurorally generated weather system

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

The conventional sea level budget (SLB) equates changes in sea surface height with the sum of ocean mass and steric change, where solid‐Earth movements are included as corrections but limited to the impact of glacial isostatic adjustment. However, changes in ocean mass load also deform the ocean bottom elastically. Until the early 2000s, ocean mass change was relatively small, translating into negligible elastic ocean bottom deformation (OBD), hence neglected in the SLB equation. However, recently ocean mass has increased rapidly; hence, OBD is no longer negligible and likely of similar magnitude to the deep steric sea level contribution. Here, we use a mass‐volume framework, which allows the ocean bottom to respond to mass load, to derive a SLB equation that includes OBD. We discuss the theoretical appearance of OBD in the SLB equation and its implications for the global SLB., Key Points The conventional sea level budget equation does not include elastic ocean bottom deformation, implicitly assuming it is negligibleRecent increases in ocean mass yield global‐mean ocean bottom deformation of similar magnitude to the deep steric sea level contributionWe use a mass‐volume approach to derive and update the sea level budget equation to include ocean bottom deformation as a process

A tracking algorithm based upon a multiple object tracking method is developed to identify, track, and classify Tropical Intraseasonal Oscillations (TISO) on the basis of their direction of propagation. Daily National Oceanic and Atmospheric Administration Outgoing Longwave Radiation anomalies from 1979–2017 are Lanczos band‐pass filtered for the intraseasonal time scale (20–100 days) and spatially averaged with nine neighboring points to get spatially smoothed anomalies over large spatial scales (~105 km2). TISO events are tracked by using a two‐stage Kalman filter predictor‐corrector method. Two dominant components of the TISO (Eastward propagating and Northward propagating) are classified, and it is found that TISO remains active throughout the year. Eastward‐propagating TISO events occur from November to April with a phase speed of ~4 m/s and northward‐propagating TISO events occur from May to October with a phase speed of ~2.5 m/s in both the Indian and Pacific Ocean basins. Composites of the mean background states (wind; sea surface temperature, SST; and moisture) reveal that the co‐occurrence of warm SST and mean westerly zonal wind plays an important role in the direction of propagation and the geographical location of TISO events. In mean state sensitivity experiments with Sp‐CAM4, we have found that the seasonality of TISO in terms of the geographical location of occurrences and direction of propagation is primarily associated with the annual march of the maximum SST and low level zonal wind which tends to follow the SST., Key Points Multiple Object Tracking Method successfully detects and tracks TISO regardless of season and direction of propagationCo‐occurrence of warm SST and mean background westerly are important for the seasonality of TISOThe seasonality of TISO primarily associated with the annual march of the maximum SST and low level zonal wind which tends to follow the SST

Dong et al. (2020, https://doi.org/10.1029/2019JD030691) employed a new compressible model to examine gravity wave (GW) self‐acceleration dynamics, instabilities, secondary gravity wave (SGW) generation, and mean forcing for GW packets localized in two dimensions (2D). This paper extends the exploration of self‐acceleration dynamics to a GW packet localized in three dimensions (3D) propagating into tidal winds in the mesosphere and thermosphere. As in the 2D packet responses, 3D GW self‐acceleration dynamics are found to be significant and include 3D GW phase distortions, stalled GW vertical propagation, local instabilities, and SGW and acoustic wave generation. Additional 3D responses described here include refraction by tidal winds, localized 3D instabilities, asymmetric SGW propagation, reduced SGW and acoustic wave responses at higher altitudes relative to 2D responses, and forcing of transient, large‐scale, 3D mean responses that may have implications for chemical and microphysical processes operating on longer time scales., Key Points 3D gravity wave packets exhibit strong self‐acceleration, mean‐flow interactions, and instability dynamics3D gravity wave packets yield strong local mean‐flow forcing and secondary gravity wave and acoustic wave generationSecondary gravity waves are modulated by tidal winds and have large scales and large influences extending into the thermosphere

Numerical weather prediction models require ever‐growing computing time and resources but, still, have sometimes difficulties with predicting weather extremes. We introduce a data‐driven framework that is based on analog forecasting (prediction using past similar patterns) and employs a novel deep learning pattern‐recognition technique (capsule neural networks, CapsNets) and an impact‐based autolabeling strategy. Using data from a large‐ensemble fully coupled Earth system model, CapsNets are trained on midtropospheric large‐scale circulation patterns (Z500) labeled 0–4 depending on the existence and geographical region of surface temperature extremes over North America several days ahead. The trained networks predict the occurrence/region of cold or heat waves, only using Z500, with accuracies (recalls) of 69–45% (77–48%) or 62–41% (73–47%) 1–5 days ahead. Using both surface temperature and Z500, accuracies (recalls) with CapsNets increase to ∼80% (88%). In both cases, CapsNets outperform simpler techniques such as convolutional neural networks and logistic regression, and their accuracy is least affected as the size of the training set is reduced. The results show the promises of multivariate data‐driven frameworks for accurate and fast extreme weather predictions, which can potentially augment numerical weather prediction efforts in providing early warnings., Key Points A data‐driven extreme weather prediction framework based on analog forecasting and deep learning pattern‐recognition methods is proposedExtreme surface temperature events over North America are skillfully predicted using only midtropospheric large‐scale circulation patternsMore advanced deep learning methods are found to yield better forecasts, encouraging novel methods tailored for climate/weather data

A coordinated set of large ensemble atmosphere‐only simulations is used to investigate the impacts of observed Arctic sea ice‐driven variability (SIDV) on the atmospheric circulation during 1979–2014. The experimental protocol permits separating Arctic SIDV from internal variability and variability driven by other forcings including sea surface temperature and greenhouse gases. The geographic pattern of SIDV is consistent across seven participating models, but its magnitude strongly depends on ensemble size. Based on 130 members, winter SIDV is ~0.18 hPa2 for Arctic‐averaged sea level pressure (~1.5% of the total variance), and ~0.35 K2 for surface air temperature (~21%) at interannual and longer timescales. The results suggest that more than 100 (40) members are needed to separate Arctic SIDV from other components for dynamical (thermodynamical) variables, and insufficient ensemble size always leads to overestimation of SIDV. Nevertheless, SIDV is 0.75–1.5 times as large as the variability driven by other forcings over northern Eurasia and Arctic., Key Points Arctic sea ice variations drive ~1.5% of boreal winter Arctic SLP variance during 1979–2014 in coordinated 130‐member AGCM experimentsAn accurate quantification of the impacts of Arctic sea ice‐driven SLP variability requires an ensemble size greater than 100 membersInsufficient ensemble size always leads to an overestimation of the Arctic sea ice‐driven variance within and outside Arctic

Black carbon (BC) aerosols from incomplete combustion generally warm the climate, but the magnitudes of their various interactions with climate are still uncertain. A key knowledge gap is their role as ice nucleating particles (INPs), enabling ice formation in clouds. Here we assess the global radiative impacts of BC acting as INPs, using simulations with the Community Earth System Model 2 climate model updated to include new laboratory‐based ice nucleation parameterizations. Overall, we find a moderate cooling through changes to stratiform cirrus clouds, counteracting the well‐known net warming from BC's direct scattering and absorption of radiation. Our best estimates indicate that BC INPs generally thin cirrus by indirectly inhibiting the freezing of solution aerosol, with a global net radiative impact of −0.13 ± 0.07 W/m2. Sensitivity tests of BC amounts and ice nucleating efficiencies, and uncertainties in the environment where ice crystals form, show a potential range of impacts from −0.30 to +0.02 W/m2., Key Points Anthropogenic black carbon (BC) thins cirrus clouds by inhibiting homogeneous nucleation, inducing moderate global coolingThe cooling impact saturates at high BC number and nucleating efficiency, due to cirrus thickening via enhanced heterogeneous ice formationBC cooling by cirrus thinning requires colocation of the aerosol with abundant, medium‐thickness homogeneously formed cirrus

A climate model's ability to reproduce observed historical warming is sometimes viewed as a measure of quality. Yet, for practical reasons it cannot be considered a purely empirical result of the modeling efforts because the desired result is known in advance and so is a potential target of tuning. Here we report how the latest edition of the Max Planck Institute for Meteorology Earth System Models (MPI‐ESM1.2) atmospheric component (ECHAM6.3) had its sensitivity systematically tuned in order to improve the modeled match with the instrumental record. In practice, this was done by targeting an equilibrium climate sensitivity of about 3 K, slightly lower than in the previous model generation (MPI‐ESM), which warmed more than observed, and in particular by addressing a climate sensitivity of about 7 K in an intermediate version of the model. In the process we identified several controls on cloud feedback, some of which confirm recently proposed hypotheses. We find the model exhibits excellent fidelity with the observed centennial global warming. We further find that an alternative approach with high climate sensitivity compensated by strong aerosol cooling instead would yield colder than observed results in the second half of the twentieth century., Key Points We document how the MPI‐ESM1.2 was tuned to the instrumental record warmingThe tuning involved lowering the models climate sensitivity

In the atmosphere, microphysics refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the various components of Earth's atmospheric water and energy cycles. The representation of microphysical processes in models continues to pose a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models is divided into two parts: (i) how to represent the population of cloud and precipitation particles, given the impossibility of simulating all particles individually within a cloud, and (ii) uncertainties in the microphysical process rates owing to fundamental gaps in knowledge of cloud physics. The recently developed Lagrangian particle‐based method is advocated as a way to address several conceptual and practical challenges of representing particle populations using traditional bulk and bin microphysics parameterization schemes. For addressing critical gaps in cloud physics knowledge, sustained investment for observational advances from laboratory experiments, new probe development, and next‐generation instruments in space is needed. Greater emphasis on laboratory work, which has apparently declined over the past several decades relative to other areas of cloud physics research, is argued to be an essential ingredient for improving process‐level understanding. More systematic use of natural cloud and precipitation observations to constrain microphysics schemes is also advocated. Because it is generally difficult to quantify individual microphysical process rates from these observations directly, this presents an inverse problem that can be viewed from the standpoint of Bayesian statistics. Following this idea, a probabilistic framework is proposed that combines elements from statistical and physical modeling. Besides providing rigorous constraint of schemes, there is an added benefit of quantifying uncertainty systematically. Finally, a broader hierarchical approach is proposed to accelerate improvements in microphysics schemes, leveraging the advances described in this paper related to process modeling (using Lagrangian particle‐based schemes), laboratory experimentation, cloud and precipitation observations, and statistical methods., Key Points Microphysics is an important component of weather and climate models, but its representation in current models is highly uncertainTwo critical challenges are identified: representing cloud and precipitation particle populations and knowledge gaps in cloud physicsA possible blueprint for addressing these challenges is proposed to accelerate progress in improving microphysics schemes

The Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative‐convective equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climate sensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique among intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud‐resolving models (CRMs), large eddy simulations (LES), and global cloud‐resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self‐aggregation in large domains and agree that self‐aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self‐aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than unaggregated simulations., Key Points Temperature, humidity, and clouds in radiative‐convective equilibrium vary substantially across modelsModels agree that self‐aggregation dries the atmosphere and reduces high cloudinessThere is no consistency in how self‐aggregation depends on warming

Moderate volcanic eruptions are a recurrent source of sulfur dioxide (SO2) in the lower stratosphere. The oxidation of SO2 to sulfuric acid aerosol particles enhance the stratospheric aerosol optical depth with likely impact on the climate. Modelling properly the volcanic aerosol content and its evolution in this region is important for radiative impact issues. In this work, we explore the variability of the tropical Upper Troposphere Lower Stratosphere(UTLS) aerosol content between 2013 and 2019, a period which was particularly impacted by volcanic eruptions. For that purpose, space-borne observations from OMPS (datasets from GES DISC and from University of Saskatchewan) and OSIRIS, together with simulations by the Whole Atmosphere Community Climate Model (WACCM) coupled with the Community Aerosol and Radiation Model for Atmospheres (CARMA), are used. Different model sensitive experiments, particularly for the injection altitude and timing, have been conducted to evaluate how the model captures the aerosol plume in terms of content, optical and microphysical properties, transport and residence time. As an example, the results show that the decay of the Calbuco plume observed by OMPS is well reproduced by the model. This study also benefits from unique datasets in the tropical southern hemisphere from the NDACC Maïdo observatory (Reunion Island, France, 20.5°S, 55.5°E) and providing valuable ground-based observations and balloon sounding opportunities. Together with satellite data, we use ground-based aerosol lidar and in situ optical particle counter observations to assess the model performance. A particular focus is made on recent eruptions. The plume of the Ambae volcano (15°S, 167°E) which erupted in July 2018 is shown to propagate to the northern hemisphere with some influence until summer 2019 in the Asian monsoon region. For the year 2019, we investigate how the Ulawun (5°S, 151°E; ~0.14 Tg of SO2) tropical eruption and the Raikoke mid-latitude eruption (48°N, 153°E; ~1.5Tg of SO2), have influenced the aerosol burden in the tropics.

This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.https://creativecommons.org/licenses/by-nc-nd/4.0/, We investigate the launch of negative upward streamers from sprite glows. This phenomenon is readily observed in high-speed observations of sprites and underlies the classification of sprites into carrot or column types. First, we describe how an attachment instability leads to a sharply defined region in the upper part of the streamer channel. This region has an enhanced electric field, low conductivity and strongly emits in the first positive system of molecular nitrogen. We identify it as the sprite glow. We then show how, in the most common configuration of a carrot sprite, several upward streamers emerge close to the lower boundary of the glow, where negative charge gets trapped and the lateral electric field is high enough. These streamers cut off the current flowing toward the glow and lead to the optical deactivation of the glow above. Finally, we discuss how our results naturally explain angel sprites. ©2019. The Authors., Information on how to access the code used to run the simulations as well as the output data analyzed in this study is available in the supporting information. This work was supported by the European Research Council (ERC) under the European Union H2020 program/ERC Grant Agreement 681257. A. Malagon Romero and A. Luque acknowledge financial support from the State Agency for Research of the Spanish MCIU through the "Center of Excellence Severo Ochoa" Award for the Instituto de Astrofisica de Andalucia (SEV-2017-0709). J. Teunissen was supported by postdoctoral fellowship 12Q6117N from Research Foundation-Flanders (FWO). The aircraft observations were sponsored by the Japanese National Broadcasting System, NHK. H. C. Stenbaek-Nielsen and M. G. McHarg were supported by Grants 1104441 and 1201683 from the U.S. National Science Foundation to the University of Alaska Fairbanks and the U.S. Air Force Academy, respectively. The simulation code used for this work is available at this site (https://gitlab.com/MD-CWI-NL/afivo-streamer).The version used for the simulations corresponds to the commit a3e2241fba 15f177b26e79106cef7354cbe09d68. Configuration files as well as the output data are available at this site (https://doi.org/10.5281/zenodo.3478177).

Anthropogenically forced changes in ocean biogeochemistry are underway and critical for the ocean carbon sink and marine habitat. Detecting such changes in ocean biogeochemistry will require quantification of the magnitude of the change (anthropogenic signal) and the natural variability inherent to the climate system (noise). Here we use Large Ensemble (LE) experiments from four Earth system models (ESMs) with multiple emissions scenarios to estimate Time of Emergence (ToE) and partition projection uncertainty for anthropogenic signals in five biogeochemically important upper‐ocean variables. We find ToEs are robust across ESMs for sea surface temperature and the invasion of anthropogenic carbon; emergence time scales are 20–30 yr. For the biological carbon pump, and sea surface chlorophyll and salinity, emergence time scales are longer (50+ yr), less robust across the ESMs, and more sensitive to the forcing scenario considered. We find internal variability uncertainty, and model differences in the internal variability uncertainty, can be consequential sources of uncertainty for projecting regional changes in ocean biogeochemistry over the coming decades. In combining structural, scenario, and internal variability uncertainty, this study represents the most comprehensive characterization of biogeochemical emergence time scales and uncertainty to date. Our findings delineate critical spatial and duration requirements for marine observing systems to robustly detect anthropogenic change., Key Points Anthropogenic changes in sea surface temperature and air‐sea CO2 fluxes emerge decades prior to changes in the biological carbon pump, ocean color, and sea surface salinityDetecting anthropogenic changes in ocean biogeochemistry requires sustained monitoring from observing systems with large spatial footprintsInternal variability, model uncertainty, and scenario uncertainty are all important sources of uncertainty for projecting future changes in ocean biogeochemistry

Spinning up a highly complex, coupled Earth system model (ESM) is a time consuming and computationally demanding exercise. For models with interactive ice sheet components, this becomes a major challenge, as ice sheets are sensitive to bidirectional feedback processes and equilibrate over glacial timescales of up to many millennia. This work describes and demonstrates a computationally tractable, iterative procedure for spinning up a contemporary, highly complex ESM that includes an interactive ice sheet component. The procedure alternates between a computationally expensive coupled configuration and a computationally cheaper configuration where the atmospheric component is replaced by a data model. By periodically regenerating atmospheric forcing consistent with the coupled system, the data atmosphere remains adequately constrained to ensure that the broader model state evolves realistically. The applicability of the method is demonstrated by spinning up the preindustrial climate in the Community Earth System Model Version 2 (CESM2), coupled to the Community Ice Sheet Model Version 2 (CISM2) over Greenland. The equilibrium climate state is similar to the control climate from a coupled simulation with a prescribed Greenland ice sheet, indicating that the iterative procedure is consistent with a traditional spin‐up approach without interactive ice sheets. These results suggest that the iterative method presented here provides a faster and computationally cheaper method for spinning up a highly complex ESM, with or without interactive ice sheet components. The method described here has been used to develop the climate/ice sheet initial conditions for transient, ice sheet‐enabled simulations with CESM2‐CISM2 in the Coupled Model Intercomparison Project Phase 6 (CMIP6)., Key Points We describe a computationally tractable, iterative procedure for spinning up a coupled Earth system‐ice sheet modelEquilibrium state from the iterative procedure is similar to a more expensive traditional model spin‐up with prescribed ice sheetsThe procedure is used for developing initial conditions for transient, fully coupled simulations in the Coupled Model Intercomparison Project phase 6

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

Quantifying the efficacy of different climate forcings is important for understanding the real‐world climate sensitivity. This study presents a systematic multimodel analysis of different climate driver efficacies using simulations from the Precipitation Driver and Response Model Intercomparison Project (PDRMIP). Efficacies calculated from instantaneous radiative forcing deviate considerably from unity across forcing agents and models. Effective radiative forcing (ERF) is a better predictor of global mean near‐surface air temperature (GSAT) change. Efficacies are closest to one when ERF is computed using fixed sea surface temperature experiments and adjusted for land surface temperature changes using radiative kernels. Multimodel mean efficacies based on ERF are close to one for global perturbations of methane, sulfate, black carbon, and insolation, but there is notable intermodel spread. We do not find robust evidence that the geographic location of sulfate aerosol affects its efficacy. GSAT is found to respond more slowly to aerosol forcing than CO2 in the early stages of simulations. Despite these differences, we find that there is no evidence for an efficacy effect on historical GSAT trend estimates based on simulations with an impulse response model, nor on the resulting estimates of climate sensitivity derived from the historical period. However, the considerable intermodel spread in the computed efficacies means that we cannot rule out an efficacy‐induced bias of ±0.4 K in equilibrium climate sensitivity to CO2 doubling when estimated using the historical GSAT trend., Key Points Multimodel mean efficacies computed using effective radiative forcing are close to one for major anthropogenic drivers, but there is notable intermodel spreadSurface temperature‐driven radiative feedbacks are generally not constant through time across forcing experimentsPDRMIP results suggest that the efficacy impact on equilibrium climate sensitivity derived from the historical period is limited to ±0.4 °C or better

Stochastic schemes, designed to represent unresolved subgrid‐scale variability, are frequently used in short and medium‐range weather forecasts, where they are found to improve several aspects of the model. In recent years, the impact of stochastic physics has also been found to be beneficial for the model's long‐term climate. In this paper, we demonstrate for the first time that the inclusion of a stochastic physics scheme can notably affect a model's projection of global warming, as well as its historical climatological global temperature. Specifically, we find that when including the “stochastically perturbed parametrization tendencies” (SPPT) scheme in the fully coupled climate model EC‐Earth v3.1, the predicted level of global warming between 1850 and 2100 is reduced by 10% under an RCP8.5 forcing scenario. We link this reduction in climate sensitivity to a change in the cloud feedbacks with SPPT. In particular, the scheme appears to reduce the positive low cloud cover feedback and increase the negative cloud optical feedback. A key role is played by a robust, rapid increase in cloud liquid water with SPPT, which we speculate is due to the scheme's nonlinear interaction with condensation., Key Points The inclusion of a stochastic scheme reduces climate sensitivity in a general circulation modelThis reduction, of around 10%, is linked to changes in cloud cover and cloud optical depth feedbacksWell‐calibrated stochastic schemes may give more accurate global warming projections

Soil organic carbon (SOC) is a crucial component of the terrestrial carbon cycle and its turnover time in models is a key source of uncertainty. Studies have highlighted the utility of δ13C measurements for benchmarking SOC turnover in global models. We used 13C as a tracer within a vertically discretized soil module of a land‐surface model, Organising Carbon and Hydrology In Dynamic Ecosystems‐ Soil Organic Matter (ORCHIDEE‐SOM). Our new module represents some of the processes that have been hypothesized to lead to a 13C enrichment with soil depth as follows: 1) the Suess effect and CO2 fertilization, 2) the relative 13C enrichment of roots compared to leaves, and 3) 13C discrimination associated with microbial activity. We tested if the upgraded soil module was able to reproduce the vertical profile of δ13C within the soil column at two temperate sites and the short‐term change in the isotopic signal of soil after a shift in C3/C4 vegetation. We ran the model over Europe to test its performance at larger scale. The model was able to simulate a shift in the isotopic signal due to short‐term changes in vegetation cover from C3 to C4; however, it was not able to reproduce the overall vertical profile in soil δ13C, which arises as a combination of short and long‐term processes. At the European scale, the model ably reproduced soil CO2 fluxes and total SOC stock. These findings stress the importance of the long‐term history of land cover for simulating vertical profiles of δ13C. This new soil module is an emerging tool for the diagnosis and improvement of global SOC models., Key Points Soil 13C was incorporated as a tracer in the land surface model ORCHIDEE‐SOMThe model was able to reproduce short‐term changes such as those observed after a shift in C3/C4 vegetationLong‐term changes in δ13C were more difficult to represent, highlighting the importance of some missing mechanisms

The National Aeronautics and Space Administration/Centre national d’études spatiales Surface Water and Ocean Topography (SWOT) mission will estimate global river discharge using remote sensing. Synoptic remote sensing data extend in situ point measurements but, at any given point, are generally less accurate. We address two questions: (1)What are the scales at which river dynamics can be observed, given spatial sampling and measurement noise characteristics? (2) Is there an equation whose variables are the averaged hydraulic quantities obtained by remote sensing and which describes the dynamics of spatially averaged rivers? We use calibrated hydraulic models to examine the power spectra of the different terms in the momentum equation and conclude that the measurement of river slope sets the scale at which rivers can be observed. We introduce the reach‐averaged Saint Venant equations that involve only observable hydraulic variations and which parametrize within‐reach variability with a variability index that multiplies the friction coefficient and leads to an increased “effective” friction coefficient. An exact expression is derived for the increase in the effective friction coefficient, and we propose an approximation that requires only estimates of the hydraulic parameter variances. We validate the results using a large set of hydraulic models and find that the approximated variability index is most faithful when the river parameters obey lognormal statistics. The effective friction coefficient, which can vary from a few percent to more than 50% of the point friction coefficient, is proportional to the riverbed elevation variance and inversely proportional to the depth. This has significant implications for estimating discharge from SWOT data., Key Points Remote sensing measurements of rivers, such as those from the SWOT mission, estimate quantities averaged over a reach, not at a pointThe Saint Venant equation is reformulated in terms of reach‐averaged variables, enabling estimation of river discharge from remote sensingThe reach‐averaged equation contains an effective friction coefficient that depends on depth and width, which affects discharge estimation