The projection of extreme convective precipitation by global climate models (GCM) exhibits significant uncertainty due to coarse resolutions. Direct dynamical downscaling (DDD) of regional climate at kilometer-scale resolutions provides valuable insight into extreme precipitation changes, but its computational expense is formidable. Here we document the effectiveness of machine learning to enable smart dynamical downscaling (SDD), which selects a small subset of GCM data to conduct downscaling. Trained with data for three subtropical/tropical regions, convolutional neural networks (CNNs) retained 92% to 98% of extreme precipitation events (rain intensity higher than the 99th percentile) while filtering out 88% to 95% of circulation data. When applied to reanalysis data sets differing from training data, the CNNs’ skill in retaining extremes decreases modestly in subtropical regions but sharply in the deep tropics. Nonetheless, one of the CNNs can still retain 62% of all extreme events in the deep tropical region in the worst case.

The extreme variability of the cold point tropopause temperature (TCPT) and height (HCPT) are examined over a tropical station, Gadanki (13.45N, 79.2E) using high-resolution radiosonde data during the period 2006-2014. The extreme variabilities such as the coldest (warmest) tropopause is defined if TCPT is lesser (greater) than the lower (upper) limit of its two-sigma level whereas the highest (lowest) tropopause is defined as the HCPTis greater (lesser) than the lower (upper) limit of its two-sigma level. In total 161 extreme cases such as the coldest (52) and warmest (30) TCPT and the highest (57) and lowest (22) HCPT are observed. The coldest (187.2±1.60 K, 17.3±0.52 km), warmest (194.2±1.78 K, 16.9±0.89 km), lowest (191.7±1.78 K, 18.2±0.55 km) and highest (191.8±2.11 K, 16.2±0.38 km) occurs without preference of season. These extreme tropopause cases occur independently. Thermal structure of the coldest tropopause cases reveals that they are often sharper whereas the warmest, highest and lowest tropopause is broader. Water vapor and ozone concentrations are found to be high for the warmest tropopause and low for the coldest tropopause. Under the shallow convection, extreme temperature profiles, in general, show prominent warming between 8-14 km while anomalous cooling (warming) just below (above) the CPT. The occurrence of the tropical cyclones, cirrus clouds and equatorial wave propagation are the possible candidates for the occurrence of the extreme tropopauses.

Modelling the spatial distribution of infrasound attenuation (or transmission loss, TL) is key to understanding and interpreting microbarometer data and observations. Such predictions enable the reliable assessment of infrasound source characteristics such as ground pressure levels associated with earthquakes, man-made or volcanic explosion properties, and ocean-generated microbarom wavefields. However, the computational cost inherent in full-waveform modelling tools, such as Parabolic Equation (PE) codes, often prevents the exploration of a large parameter space, i.e., variations in wind models, source frequency, and source location, when deriving reliable estimates of source or atmospheric properties – in particular for real-time and near-real-time applications. Therefore, many studies rely on analytical regression-based heuristic TL equations that neglect complex vertical wind variations and the range-dependent variation in the atmospheric properties. This introduces significant uncertainties in the predicted TL. In the current contribution, we propose a deep learning approach trained on a large set of simulated wavefields generated using PE simulations and realistic atmospheric winds to predict infrasound ground-level amplitudes up to 1000 km from a ground-based source. Realistic range dependent atmospheric winds are constructed by combining ERA5, NRLMSISE-00, and HWM-14 atmospheric models, and small-scale gravity-wave perturbations computed using the Gardner model. Given a set of wind profiles as input, our new modelling framework provides a fast (0.05 s runtime) and reliable (~5 dB error on average, compared to PE simulations) estimate of the infrasound TL.

Recent work using CMIP5 models under RCP8.5 suggests that individual multimodel-mean changes in precipitation and wind variability associated with the Madden-Julian oscillation (MJO) are not detectable until the end of the 21st century. However, a decrease in the ratio of MJO circulation to precipitation anomaly amplitude is detectable as early as 2021-2040, consistent with an increase in dry static stability as predicted by weak-temperature-gradient balance. Here, we examine MJO activity in multiple reanalyses (ERA5,MERRA-2, and ERA-20C) and find that MJO wind and precipitation anomaly amplitudes have a complicated time evolution over the record. However, a decrease in the ratio of MJO circulation to precipitation anomaly amplitude is detected over the observational period, consistent with the change in dry static stability. These results suggest that weak-temperature-gradient theory may be able to help explain changes in MJO activity in recent decades.

Automated classification of remote sensing data is an integral tool for earth scientists, and deep learning has proven very successful at solving such problems. However, building deep learning models to process the data requires expert knowledge of machine learning. We introduce DELTA, a software toolkit to bridge this technical gap and make deep learning easily accessible to earth scientists. Visual feature engineering is a critical part of the machine learning lifecycle, and hence is a key area that will be automated by DELTA. Hand-engineered features can perform well, but require a cross functional team with expertise in both machine learning and the specific problem domain, which is costly in both researcher time and labor. The problem is more acute with multispectral satellite imagery, which requires considerable computational resources to process. In order to automate the feature learning process, a neural architecture search samples the space of asymmetric and symmetric autoencoders using evolutionary algorithms. Since denoising autoencoders have been shown to perform well for feature learning, the autoencoders are trained on various levels of noise and the features generated by the best performing autoencoders evaluated according to their performance on image classification tasks. The resulting features are demonstrated to be effective for Landsat-8 flood mapping, as well as benchmark datasets CIFAR10 and SVHN.

In clouds containing both liquid and ice that have temperatures between -3°C and -8°C, liquid droplets collide with large ice crystals, freeze, and shatter, producing a plethora of small ice splinters. This process, known as Hallett-Mossop rime splintering, can cause clouds to reflect less sunlight and to have shorter lifetimes. Here, we use a novel suite of five global cloud-resolving models, which break up the Earth’s atmosphere into columns with 2-4 km horizontal edges, to show that this microscale process has global implications. Simulations that include Hallett-Mossop rime splintering have reduced cumulus cloud cover over the Southern Ocean and reflect 12 Wm^(-2) less sunlight back to space over the same region, better matching satellite observed radiative fluxes. We evaluate simulated clouds using high-resolution visible images from the Himawari satellite, and radar reflectivities and two-dimensional images of cloud particles from the SOCRATES aircraft campaign. Cumulus clouds from simulations with Hallett-Mossop rime splintering included have more realistic cloud morphology, cloud vertical structure and ice crystal properties. We show that Hallett-Mossop rime splintering is an important control on cumulus cloud cover and cloud radiative effects over the Southern Ocean, and that including it in simulations improves model performance. We also demonstrate the key role that global cloud-resolving models can play in detangling the effects of clouds on Earth’s climate across scales, making it possible to translate the behavior of tiny cloud particles (10^(-8) m^2) to their impact on the radiative budget of the massive Southern Ocean basin (10^(14) m^2).

Evaluating the influence of anthropogenic emissions changes on air quality requires accounting for the influence of meteorological variability. Statistical methods such as multiple linear regression (MLR) models with basic meteorological variables are often used to remove meteorological variability and estimate trends in measured pollutant concentrations attributable to emissions changes. However, the ability of these widely-used statistical approaches to correct for meteorological variability remains unknown, limiting their usefulness in the real-world policy evaluations. Here, we quantify the performance of MLR and other quantitative methods using two scenarios simulated by a chemical transport model, GEOS-Chem, as a synthetic dataset. Focusing on the impacts of anthropogenic emissions changes in the US (2011 to 2017) and China (2013 to 2017) on PM2.5 and O3, we show that widely-used regression methods do not perform well in correcting for meteorological variability and identifying long-term trends in ambient pollution related to changes in emissions. The estimation errors, characterized as the differences between meteorology-corrected trends and emission-driven trends under constant meteorology scenarios, can be reduced by 30%-42% using a random forest model that incorporates both local and regional scale meteorological features. We further design a correction method based on GEOS-Chem simulations with constant emission input and quantify the degree to which emissions and meteorological influences are inseparable, due to their process-based interactions. We conclude by providing recommendations for evaluating the effectiveness of emissions reduction policies using statistical approaches.

Tropical Cyclone Precipitation (TCP) is one of the major triggers of flash flooding and landslide in eastern Mexico. We apply different statistical and machine learning techniques to study a 99 year TCP climatology in high resolution. Strong correlations exist between location variables and annual mean TCP, as well as between dynamic variables and event TCP. Topographic variables observe mixed signals with the elevation variances positively correlated with TCP. The Random Forest (RF) model is a powerful tool with excellent fitting and predicting skills for TCP variations. It has a very small out of sample cross-validation error and well captures the spatial variations of historical TCP events. Only three location variables are needed to construct the best model for the annual mean TCP while the best model needs 18 variables to explain the complex variations in the event TCP. The distance to the track is the most important variable for the event TCP model and many other factors contribute to the TCP collectively and nonlinearly, which can’t be captured fully by the previous correlation analysis. They include translation characteristics of the storms, locations of the precipitation grid, and topography. Event TCP is generally larger in storms with slower translation speed and more variance in their tracks. While the lower coastal area generally has a higher probability of TCP, the higher inland has elevation variances that enhance less frequent but extreme TCP events. The RF algorithm is an efficient machine learning approach showing potentials for future Quantitative Precipitation Forecasting (QPF).

Predictable internal climate variability on decadal timescales (2-10 years) is associated with large-scale oceanic processes, however these predictable signals may be masked by the noisy climate system. One approach to overcoming this problem is investigating state-dependent predictability - how differences in prediction skill depend on the initial state of the system. We present a machine learning approach to identify state-dependent predictability on decadal timescales in the Community Earth System Model version 2 by incorporating uncertainty estimates into a regression neural network. We leverage the network’s prediction of uncertainty to examine state dependent predictability in sea surface temperatures by focusing on predictions with the lowest uncertainty outputs. In particular, we study two regions of the global ocean - the North Atlantic and North Pacific - and find that skillful initial states identified by the neural network correspond to particular phases of Atlantic multi-decadal variability and the interdecadal Pacific oscillation.

We report the first Terrestrial Electron Beam detected by the Atmosphere‐Space Interactions Monitor. It happened on 16 September 2018. The Atmosphere‐Space Interactions Monitor Modular X and Gamma ray Sensor recorded a 2 ms long event, with a softer spectrum than typically recorded for Terrestrial Gamma ray Flashes (TGFs). The lightning discharge associated to this event was found in the World Wide Lightning Location Network data, close to the northern footpoint of the magnetic field line that intercepts the International Space Station location. Imaging from a GOES‐R geostationary satellite shows that the source TGF was produced close to an overshooting top of a thunderstorm. Monte‐Carlo simulations were performed to reproduce the observed light curve and energy spectrum. The event can be explained by the secondary electrons and positrons produced by the TGF (i.e., the Terrestrial Electron Beam), even if about 3.5% to 10% of the detected counts may be due to direct TGF photons. A source TGF with a Gaussian angular distribution with standard deviation between 20.6° and 29.8° was found to reproduce the measurement. Assuming an isotropic angular distribution within a cone, compatible half angles are between 30.6° and 41.9°, in agreement with previous studies. The number of required photons for the source TGF could be estimated for various assumption of the source (altitude of production and angular distribution) and is estimated between 1017.2 and 1018.9 photons, that is, compatible with the current consensus.

El Niño Southern Oscillation (ENSO) is the leading mode of interannual climate variability, with large socioeconomical and environmental impacts. The main conceptual model for ENSO, the Recharge Oscillator (RO), considers two independent modes: the fast zonal tilt mode in phase with central-eastern Pacific Temperature (Te), and the slow recharge mode in phase quadrature. However, usual indices (western or equatorial sea level/thermocline depth h) do not orthogonally isolate the slow recharge mode, leaving it correlated with Te. Furthermore the optimal index is currently debated. Here, by objectively optimizing the RO equations fit to observations, we develop an improved recharge index. (1) Te-variability is regressed out, building h_ind statistically-independent from Te. Capturing the pure recharge, h_ind reconciles usual indices. (2) The optimum is equatorial plus southwestern Pacific h_ind_eq+sw (because of ENSO Ekman pumping meridional asymmetry). Using h_ind_eq+sw, the RO becomes more consistent with observations. h_ind_eq+sw is more relevant for ENSO operational diagnostics.

Synoptic weather systems are a major driver of spatial gradients in atmospheric CO2 mole fractions. During frontal passages, air masses from different regions meet at the frontal boundary creating significant gradients in CO2 mole fractions. We quantitatively describe the atmospheric transport of CO2 mole fractions during a mid-latitude cold front passage and explore the impact of various sources of CO2. We focus here on a cold front passage over Lincoln, Nebraska on August 4th, 2016 observed by aircraft during the Atmospheric Carbon and Transport (ACT)-America campaign. A band of air with elevated CO2was located along the frontal boundary. Observed and simulated differences in CO2 across the front were as high as 25 ppm. Numerical simulations using WRF-Chem at cloud resolving resolutions (3km), coupled with CO2 surface fluxes and boundary conditions from CarbonTracker (CT-NRTv2017x), were performed to explore atmospheric transport at the front. Model results demonstrate that the frontal CO2 difference in the upper troposphere can be explained largely by inflow from outside of North America. This difference is modified in the atmospheric boundary layer and lower troposphere by continental surface fluxes, dominated in this case by biogenic and fossil fuel fluxes. Horizontal and vertical advection are found to be responsible for the transport of CO2 mole fractions along the frontal boundary. We show that cold front passages lead to large CO2 transport events including a significant contribution from vertical advection, and that mid-continent frontal boundaries are formed from a complex mixture of CO2 sources.

This paper describes the first implementation of the d x=3.25 km version of the Energy Exascale Earth System Model (E3SM) global atmosphere model and its behavior in a 40 day prescribed-sea-surface-temperature simulation (Jan 20-Feb 28, 2020). This simulation was performed as part of the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) phase 2 model intercomparison. Effective resolution is found to be $\sim 6x the horizontal grid resolution despite using a coarser grid for physical parameterizations. Despite this new model being in an immature and untuned state, moving to 3.25 km grid spacing solves several long-standing problems with the E3SM model. In particular, Amazon precipitation is much more realistic, the frequency of light and heavy precipitation is improved, agreement between the simulated and observed diurnal cycle of tropical precipitation is excellent, and the vertical structure of tropical convection and coastal stratocumulus look good. In addition, the new model is able to capture the frequency and structure of important weather events (e.g. hurricanes, midlatitude storms including atmospheric rivers, and cold air outbreaks). Interestingly, this model does not get rid of the erroneous southern branch of the intertropical convergence zone nor the tendency for strongest convection to occur over the Maritime Continent rather than the West Pacific, both of which are classic climate model biases. Several other problems with the simulation are identified, underscoring the fact that this model is a work in progress.

We develop and demonstrate a new interpretable deep learning model specifically designed for image analysis in earth system science applications. The neural network is designed to be inherently interpretable, rather than explained via post hoc methods. This is achieved by training the network to identify parts of training images that act as prototypes for correctly classifying unseen images. The new network architecture extends the interpretable prototype architecture of a previous study in computer science to incorporate absolute location. This is useful for earth system science where images are typically the result of physics-based processes, and the information is often geo-located. Although the network is constrained to only learn via similarities to a small number of learned prototypes, it can be trained to exhibit only a minimal reduction in accuracy compared to non-interpretable architectures. We apply the new model to two earth science use cases: a synthetic data set that loosely represents atmospheric high- and low-pressure systems, and atmospheric reanalysis fields to identify the state of tropical convective activity associated with the Madden-Julian oscillation. In both cases, we demonstrate that considering absolute location greatly improves testing accuracies. Furthermore, the network architecture identifies specific historical dates that capture multivariate, prototypical behaviour of tropical climate variability.

Drylands occupy ~40% of the land surface and are thought to dominate global carbon (C) cycle inter-annual variability (IAV). Therefore, it is imperative that global terrestrial biosphere models (TBMs), which form the land component of IPCC earth system models, are able to accurately simulate dryland vegetation and biogeochemical processes. However, compared to more mesic ecosystems, TBMs have not been widely tested or optimized using in situ dryland CO2 fluxes. Here, we address this gap using a Bayesian data assimilation system and 89 site-years of daily net ecosystem exchange (NEE) data from 12 southwest US Ameriflux sites to optimize the C cycle parameters of the ORCHIDEE TBM. The sites span high elevation forest ecosystems, which are a mean sink of C, and low elevation shrub and grass ecosystems that are either a mean C sink or “pivot” between an annual C sink and source. We find that using the default (prior) model parameters drastically underestimates both the mean annual NEE at the forested mean C sink sites and the NEE IAV across all sites. Our analysis demonstrated that optimizing phenology parameters are particularly useful in improving the model’s ability to capture both the magnitude and sign of the NEE IAV. At the forest sites, optimizing C allocation, respiration, and biomass and soil C turnover parameters reduces the underestimate in simulated mean annual NEE. Our study demonstrates that all TBMs need to be calibrated for dryland ecosystems before they are used to determine dryland contributions to global C cycle variability and long-term carbon-climate feedbacks.

Marine heatwaves (MHWs) are extreme oceanic warm water events (above 90th percentile threshold) that significantly impact the marine environment. Several studies have recently explored the genesis and impacts of MHWs though they are least understood in the tropical Indian Ocean. Here we investigate the genesis and trend of MHWs in the Indian Ocean during 1982–2018 and their role in modulating the Indian monsoon. We find that the rapid warming in the Indian Ocean plays a critical role in increasing the number of MHWs. Meanwhile, the El Nino has a prominent influence on the occurrence of MHWs during the summer monsoon. The Indian Ocean warming and the El Nino variability have synergistically resulted in some of the strongest and long-lasting MHWs in the Indian Ocean. The western Indian Ocean (WIO) region experienced the largest increase in MHWs at a rate of 1.2–1.5 events per decade, followed by the north Bay of Bengal at a rate of 0.4–0.5 events per decade. Locally, the MHWs are induced by increased solar radiation, relaxation of winds, and reduced evaporative cooling. In the western Indian Ocean, the decreased winds further restrict the heat transport by ocean currents from the near-equatorial regions towards the north. Our analysis indicates that the MHWs in the western Indian Ocean and the north Bay of Bengal lead to a reduction in monsoon rainfall over the central Indian subcontinent. On the other hand, there is an enhancement of monsoon rainfall over southwest India due to the MHWs in the Bay of Bengal.

Monsoons emerge over a range of land surface conditions and exhibit varying physical characteristics over the seasonal cycle, from onset to withdrawal. Systematically varying the moisture and albedo parameters over land in an idealized modeling framework allows one to analyze the physics underlying the successive stages of monsoon development. To this end we implement an isolated South American continent with reduced heat capacity but no topography in an idealized moist general circulation model. Irrespective of the local moisture availability, the seasonal cycles of precipitation and circulation over the South American monsoon sector are distinctly monsoonal with the default surface albedo. The dry land case (zero evaporation) is characterized by a shallow overturning circulation with vigorous lower-tropospheric ascent, transporting water vapor from the ocean. By contrast, with bucket hydrology or unlimited land moisture the monsoon features deep moist convection that penetrates the upper troposphere. A series of land albedo perturbation experiments indicates that the monsoon strengthens with the net column energy flux and the near-surface moist static energy with all land moisture conditions. When the land-ocean thermal contrast is strong enough, inertial instability alone is sufficient for producing a shallow but vigorous circulation and converging a large amount of moisture from the ocean even in the absence of land moisture. Once the land is sufficiently moist, convective instability takes hold and the shallow circulation deepens. These results have implications for monsoon onset and intensification, and may elucidate the seasonal variations in how surface warming impacts tropical precipitation over land.

The global mean surface temperature (GMST) record exhibits both interannual to multidecadal variability and a long-term warming trend due to external climate forcing. To explore the predictability of temporary slowdowns in decadal warming, we apply an artificial neural network (ANN) to climate model data from the Community Earth System Model Version 2 Large Ensemble. Here, an ANN is tasked with whether or not there will be a slowdown in the rate of the GMST trend by using maps of ocean heat content at the onset. Through a machine learning explainability method, we find the ANN is learning off-equatorial patterns of anomalous ocean heat content that resemble transitions in the phase of the Interdecadal Pacific Oscillation in order to make slowdown predictions. Finally, we test our ANN on observed historical data, which further reveals how explainable neural networks are useful tools for understanding decadal variability in both climate models and observations.

Incoherent scatter (IS) radars are invaluable instruments for ionospheric physics, since they observe altitude profiles of electron density (Ne), electron temperature (Te), ion temperature (Ti) and line-of-sight plasma velocity (Vi) from ground. However, the temperatures can be fitted to the observed IS spectra only when the ion composition is known, and resolutions of the fitted plasma parameters are often insufficient for auroral electron precipitation, which requires high resolutions in both range and time. The problem of unknown ion composition has been addressed by means of the full-profile analysis, which assumes that the plasma parameter profiles are smooth in altitude, or follow some predefined shape. In a similar manner, one could assume smooth time variations, but this option has not been used in IS analysis. We propose a plasma parameter fit technique based on Bayesian filtering, which we have implemented as an additional Bayesian Filtering Module (BAFIM) in the GUISDAP analysis package. BAFIM allows us to control gradients in both time and range directions for each plasma parameter separately. With BAFIM we can fit F1 region ion composition together with Ne, Te, Ti, and Vi, and we have reached 4 s/900 m time/range steps in four-parameter fits of Ne, Te, Ti and Vi in E region observations of auroral electron precipitation.

The COVID-19 pandemic led to widespread reductions in mobility and induced observable changes in the atmosphere. Recent work has employed novel mobility datasets as a proxy for trace gas emissions from traffic, yet there has been little work evaluating these emission numbers. Here, we systematically compare these mobility datasets to traffic data from local governments in seven diverse urban and rural regions to characterize the magnitude of errors in emissions that result from using the mobility data. We observe differences in excess of 60% between these mobility datasets and local traffic data, which result in large errors in emission estimates. We could not find a general functional relationship between mobility data and traffic flow over all regions. Future work should be cautious when using these mobility metrics for emission estimates. Further, we use the local government data to identify emission reductions from traffic in the range of 7-22% in 2020 compared to 2019.

A regional data-constrained coupled ocean-sea ice general circulation model and its adjoint are used to investigate mechanisms controlling the volume transport variability through Bering Strait during 2002 to 2013. Comprehensive time-resolved sensitivity maps of Bering Strait transport to atmospheric forcing can be accurately computed with the adjoint along the forward model trajectory to identify spatial and temporal scales most relevant to the strait's transport variability. The simulated Bering Strait transport anomaly is found to be controlled primarily by the wind stress on short time-scales of order 1 month. Spatial decomposition indicates that on monthly time-scales winds over the Bering and the combined Chukchi and East Siberian Seas are the most significant drivers. Continental shelf waves and coastally-trapped waves are suggested as the dominant mechanisms for propagating information from the far field to the strait. In years with transport extrema, eastward wind stress anomalies in the Arctic sector are found to be the dominant control, with correlation coefficient of 0.94. This implies that atmospheric variability over the Arctic plays a substantial role in determining Bering Strait flow variability. The near-linear response of the transport anomaly to wind stress allows for predictive skill at interannual time-scales, thus potentially enabling skillful prediction of changes at this important Pacific-Arctic gateway, provided that accurate measurements of surface winds in the Arctic can be obtained. The novelty of this work is the use of space and time-resolved adjoint-based sensitivity maps, which enable detailed dynamical, i.e. causal attribution of the impacts of different forcings.

Tropical anvil clouds are an important player in Earth’s climate and climate sensitivity, but simulations of anvil clouds are uncertain. Here we pinpoint one source of uncertainty by demonstrating a marked increase of anvil cloud fraction with resolution in cloud-resolving simulations of radiative-convective equilibrium. This increase in cloud fraction can be traced back to the resolution dependence of horizontal mixing between clear and cloudy air. A mixing timescale is diagnosed for each simulation using the cloud fraction theory of Seeley et al. (2019), and is found to scale linearly with grid spacing, as expected from a simple scaling law. Thus mixing becomes more efficient with increasing resolution, generating more evaporation, decreased precipitation efficiency, greater mass flux, and hence greater detrainment and cloud fraction.The decrease in precipitation efficiency also yields a marked increase in relative humidity with resolution.

Since the earliest space-based observations of Earth’s atmosphere, ultraviolet (UV) airglow has proven a useful resource for remote sensing of the ionosphere and thermosphere. The NASA Ionospheric Connection Explorer (ICON) spacecraft, whose mission is to explore the connections between ionosphere and thermosphere utilizes UV airglow in the typical way: an extreme-UV (EUV) spectrometer uses dayglow between 54 nm and 88 nm to measure the density of O+, and a far-UV spectrograph uses the O 135.6 nm doublet and N2 Lyman-Birge-Hopfield band dayglow to measure the column ratio of O to N2 in the upper thermosphere. Two EUV emission features, O+ 61.6 nm and 83.4 nm, are used for the O+ retrieval; however, many other features are captured along the EUV instrument’s spectral dimension. In this study, we examine the other dayglow features observed by ICON EUV and demonstrate that it measures a nitrogen feature around 87.8 nm which can be used to observe the neutral thermosphere.

Since 2004, following prolonged stratospheric sudden warming (SSW) events, it has been observed that the stratopause disappeared and reformed at a higher altitude, forming an elevated stratopause (ES). The relative roles of atmospheric waves in the mechanism of ES formation are still not fully understood. We performed a hindcast of the 2018/19 SSW event using a gravity-wave (GW) permitting general circulation model that resolves the mesosphere and lower thermosphere (MLT) and analyzed dynamical phenomena throughout the entire middle atmosphere. An ES formed after the major warming on 1 January 2019. There was a marked temperature maximum in the polar upper mesosphere around 28 December 2018 prior to the disappearance of the descending stratopause associated with the SSW. This temperature structure is referred to as a mesospheric inversion layer (MIL). We show that adiabatic heating from the residual circulation driven by GW forcing (GWF) causes barotropic and/or baroclinic instability before the MIL formation, causing in situ generation of planetary waves (PWs). These PWs propagate into the MLT and exert negative (westward) forcing, which contributes to the MIL formation. Both GWF and PW forcing (PWF) above the recovered eastward jet play crucial roles in ES formation. The altitude of the recovered eastward jet, which regulates GWF and PWF height, is likely affected by the MIL structure. Simple vertical propagation from the lower atmosphere is insufficient to explain the presence of the GWs observed in this event.