Prior investigations have attempted to characterize the longitudinal variability of the column number density ratio of atomic oxygen to molecular nitrogen (ΣO/N2) in the context of non-migrating tides. The retrieval of thermospheric ΣO/N2 from far ultra-violet (FUV) emissions assumes production is due to photoelectron impact excitation on O and N2. Consequently, efforts to characterize the tidal variability in O/N2 have been limited by ionospheric contamination from O+ radiative recombination at afternoon local times (LT) around the equatorial ionization anomaly. The retrieval of ΣO/N2 from FUV observations by the Ionospheric Connection Explorer (ICON) provides an opportunity to address this limitation. In this work, we derive modified ΣO/N2 datasets to delineate the response of thermospheric composition to non-migrating tides as a function of LT in the absence of ionospheric contamination. We assess estimates of the ionospheric contribution to 135.6 nm emission intensities based on either Global Ionospheric Specification (GIS) electron density, International Reference Ionosphere (IRI) model output, or observations from the Extreme Ultra-Violet imager (EUV) onboard ICON during March and September equinox conditions in 2020. Our approach accounts for any biases between the ionospheric and airglow datasets. We found that the ICON-FUV dataset, corrected for ionospheric contamination based on GIS, uncovered a previously obscured diurnal eastward wavenumber 2 tide in a longitudinal wavenumber 3 pattern at March equinox in 2020. This finding demonstrates not only the necessity of correcting for ionospheric contamination of the FUV signals but also the utility of using GIS for the correction.

Insight and other observations of the Martian surface at different locations have recorded the diurnal variation in surface pressure (Ps) with two rapid fluctuations that occur at dawn and dusk (around LT0800 and LT2000). These short-period surface pressure perturbations at specific local times are typically observed near Martian equinox. Similar phase-locked surface pressure fluctuations over most areas of the middle and low latitudes are simulated by the Martian General Circulation Model at the Dynamic Meteorology Laboratory (LMD). This phenomenon is thus likely to be global rather than local. By reconstructing the surface pressure variation from the horizontal mass flux, the pressure fluctuations in a sol can be attributed to the diurnal variation in the horizontal wind divergence and convergence in the Martain tropical troposphere in the GCM simulations. The background diurnal variation in Ps is related to the diurnal migrating tidal wind, while the enhanced convergence due to the overlap of the 4-hour and 6-hour tides before LT0800 and LT2000 is responsible for the Ps peaks occurring at dawn and twilgith. Although the amplitudes of the 4-hour and 6-hour tides are smaller than those of diurnal tides, the phases of these tides remain similar in the Martain troposphere, which suggests that the convergences and divergences due to 4 h/6 h tidal winds at different altitudes are in phase and together create a mass flux comparable to that induced by diurnal/semidiurnal components and lead to rapid pressure fluctuations.

We examine the distribution of aerosol optical depth (AOD) across 27,707 northern hemisphere (NH) midlatitude cyclones for 2005-2018 using retrievals from the Moderate Resolution Spectroradiometer (MODIS) sensor on the Aqua satellite. Cyclone-centered composites show AOD enhancements of 20-45% relative to background conditions in the warm conveyor belt (WCB) airstream. Fine mode AOD (fAOD) accounts for 68% of this enhancement annually. Relative to background conditions, coarse mode AOD (cAOD) is enhanced by more than a factor of two near the center of the composite cyclone, co-located with high surface wind speeds. Within the WCB, MODIS AOD maximizes in spring, with a secondary maximum in summer. Cyclone-centered composites of AOD from the Modern Era Retrospective analysis for Research and Applications, version 2 Global Modeling Initiative (M2GMI) simulation reproduce the magnitude and seasonality of the MODIS AOD composites and enhancements. M2GMI simulations show that the AOD enhancement in the WCB is dominated by sulfate (37%) and organic aerosol (25%), with dust and sea salt each accounting for 15%. MODIS and M2GMI AOD are 60% larger in North Pacific WCBs compared to North Atlantic WCBs and show a strong relationship with anthropogenic pollution. We infer that NH midlatitude cyclones account for 355 Tg yr-1 of sea salt aerosol emissions annually, or 60% of the 30-80oN total. We find that deposition within WCBs is responsible for up to 35% of the total aerosol deposition over the NH ocean basins. Furthermore, the cloudy environment of WCBs leads to efficient secondary sulfate production.

In this paper the meteorological drivers of North American Monsoon (NAM) extreme precipitation events (EPEs) are identified and analyzed. First, the NAM area and its subregions are distinguished using self-organizing maps (SOM) applied to the Climate Prediction Center (CPC) global precipitation dataset. This delineation emphasizes the distinct extreme precipitation character and drivers in each subregion, and we subsequently argue these subregions are more suitable for regional analysis given the inhomogeneous geographical features in the NAM area. For each EPE, defined as daily precipitation exceeding the 95th precipitation percentile, five synoptic features and one mesoscale feature are investigated and assigned as potential drivers. Essentially all EPEs can be associated with at least one selected driver, with only one event remaining as unclassified. The attribution result demonstrates the dominant role of Gulf of California moisture surges, followed by mesoscale convective systems. Finally, a frequency and probability analysis is conducted to contrast precipitation distributions conditioned on the associated meteorological drivers. Interactions and influences among candidate features are revealed by the precipitation probability density functions.

Wildfires expose populations to increased morbidity and mortality due to increased air pollutant concentrations. Data included burned area, particulate matter (PM10, PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), temperature, relative humidity, wind-speed, aerosol optical depth (AOD) and mortality rates due to Circulatory System Disease (CSD), Respiratory System Disease (RSD), Pneumonia (PNEU), Chronic Obstructive Pulmonary Disease (COPD), and Asthma (ASMA). Only the months of the 2011-2020 wildfire season (June-July-August-September-October) with burned area greater than 1000 ha were considered. Multivariate statistical methods were used to reduce the dimensionality of the data to create two fire-pollution-meteorology indices (PBI, API), which allow us to understand how the combination of these variables affect cardio-respiratory mortality. Cluster analysis applied to PBI-API-Mortality divided the data into two Clusters. Cluster 1 included the months with lower temperatures, higher relative humidity, and high PM10, PM2.5, and NO2 concentrations. Cluster 2 included the months with more extreme weather conditions such as higher temperatures, lower relative humidity, larger forest fires, high PM10, PM2.5, O3, and CO concentrations, and high AOD. The two clusters were subjected to linear regression analysis to better understand the relationship between mortality and the PBI and API indices. The results showed statistically significant (p-value < 0.05) correlation (r) in Cluster 1 between RSDxPBI (rRSD = 0.539), PNEUxPBI (rPNEU = 0.644). Cluster 2 showed statistically significant correlations between RSDxPBI (rRSD = 0.464), PNEUxPBI (rPNEU = 0.442), COPDxPBI (rCOPD = 0.456), CSDxAPI (rCSD = 0.705), RSDxAPI (rCSD = 0.716), PNEUxAPI (rPNEU = 0.493), COPDxAPI (rPNEU = 0.619).

The NASA Ames Mars Global Climate Model (MGCM) software has been in steady use at NASA for decades and was recently released to the public. This model simulates the complex interactions of various weather cycles that exist on Mars, namely the Dust Cycle, the CO2 Cycle, and the Water cycle. Utilized by NASA, the MGCM is used to help understand their empirically observed data through the use of sensitivity studies. However, these sensitivity studies are computationally taxing, requiring weeks to run. To address this issue, we have developed a surrogate model using Gaussian processes (GP) that can emulate the output of this model with relatively small amounts of data in a reduced amount of time (on the order of minutes). We demonstrate the effectiveness of our emulator using backward error analysis.

An intercomparison of four air quality models is performed in the tropical megacity of Sao Paulo with the perspective of developing an air quality forecasting system based on a regional model ensemble. During three contrasting periods marked by different types of pollution events, we analyze the concentrations of the main regulated pollutants (Ozone, CO, SO2, NOx, PM2.5 and PM10) compared to observations of a dense air quality monitoring network. The modeled concentrations of CO, PM and NOx are in good agreement with the observations for the temporal variability and the range of variation. However, the transport of pollutants due to biomass burning pollution events can strongly affect the air quality in the metropolitan area of Sao Paulo with increases of CO, PM2.5 and PM10, and is associated with an important inter-model variability. Our results show that each model has periods and pollutants for which it has the best agreement. The observed day-to-day variability of ozone concentration is well reproduced by the models, as well as the average diurnal cycle in terms of timing. Overall the performance for ozone of the median of the regional model ensemble is the best in terms of time and magnitude because it takes advantage of the capabilities of each model. Therefore, an ensemble prediction of regional models is promising for an operational air quality forecasting system for the megacity of Sao Paulo.

Because strong absorption of infrared radiation by greenhouse gases is more significant than the cloud longwave (LW) scattering effect, most climate models neglect cloud LW scattering to save computational costs. However, ignoring cloud LW scattering directly overestimates the outgoing longwave radiation (OLR). A recent study performed slab-ocean model simulations in the Community Earth System Model and showed that such radiative flux changes due to ice cloud LW scattering can affect the polar surface climate more than other climate zones. In this study, we included the same ice cloud LW scattering treatment in the Exascale Energy Earth System Model (E3SM) version 2 and ran fully-coupled simulations to assess the impact of ice cloud LW scattering on global climate simulation. Including ice cloud LW scattering leads to ~2 W m^-2 instantaneous OLR reduction in the tropics, more than the OLR reduction in other climate zones. Strong surface warming occurs in the Arctic, which is dominantly caused by the polar amplification resulting from the radiative forcing caused by ice cloud LW scattering. In the tropics, when the ice cloud LW scattering effect is included, more liquid clouds form in the middle troposphere, high clouds in the convection zone are lifted, anvil clouds retreat, and stratiform low cloud fraction increases. Most of these effects are similar to the cloud response to the increase of well-mixed greenhouse gases. The present study suggests that the ice cloud LW scattering effect must be incorporated into climate simulations.

The eruption of Hunga Tonga in January 2022 injected an amount of water vapor into the stratosphere that is unprecedented in the satellite era. In the ensuing months Aura Microwave Limb Sounder (MLS) measurements showed that this plume of water vapor spread from its original injection site at 20.5⁰S to Mauna Loa, Hawaii at 19.5⁰N, where an increase was observed in April by the ground-based Water Vapor Millimeter-wave Spectrometer (WVMS) instruments. Interannual variations in water vapor occur over Mauna Loa due to both dynamical variations in the tropical stratosphere and variations in the amount of water vapor crossing the tropical tropopause, and we place the observed stratospheric water vapor increase from Hunga Tonga into context of these other variations that have been observed since 2013.

Land-atmosphere interactions are central to the evolution of the atmospheric boundary layer and the subsequent formation of clouds and precipitation. Existing global climate models represent these connections with bulk approximations on coarse spatial scales, but observations suggest that small-scale variations in surface characteristics and co-located turbulent and momentum fluxes can significantly impact the atmosphere. Recent model development efforts have attempted to capture this phenomenon by coupling existing representations of subgrid-scale (SGS) heterogeneity between land and atmosphere models. Such approaches are in their infancy and it is not yet clear if they can produce a realistic atmospheric response to surface heterogeneity. Here, we implement a parameterization to capture the effects of SGS heterogeneity in the Community Earth System Model (CESM2), and compare single-column simulations against high-resolution Weather Research and Forecasting (WRF) large-eddy simulations (LESs), which we use as a proxy for observations. The CESM2 experiments increase the temperature and humidity variances in the lowest atmospheric levels, but the response is weaker than in WRF-LES. In part, this is attributed to an underestimate of surface heterogeneity in the land model due to a lack of SGS meteorology, a separation between deep and shallow convection schemes in the atmosphere, and a lack of explicitly represented mesoscale secondary circulations. These results highlight the complex processes involved in capturing the effects of SGS heterogeneity and suggest the need for parameterizations that communicate their influence not only at the surface but also vertically.

It has been widely recognized that tropical cyclone (TC) genesis requires favorable large-scale environmental conditions. Based on these linkages, numerous efforts have been made to establish an empirical relationship between seasonal TC activities and large-scale environmental favorabilities in a quantitative way, which lead to conceptual functions such as the TC genesis index. However, due to the limited amount of reliable TC observations and complexity of the climate system, a simple analytic function may not be an accurate portrait of the empirical relation between TCs and their ambiences. In this research, we use convolution neural networks (CNNs) to disentangle this complex relationship. To circumvent the limited amount of seasonal TC observation records, we implement transfer-learning technique to train ensembles of CNNs first on suites of high-resolution climate simulations with realistic seasonal TC activities and large-scale environmental conditions, and then subsequently on the state-of-the-art reanalysis from 1950 to 2019. Our CNNs can remarkably reproduce the historical TC records, and yields significant seasonal prediction skills when the large-scale environmental inputs are provided by operational climate forecasts. Furthermore, by forcing the ensemble CNNs with 20th century reanalysis products and phase 6 of the Coupled Model Intercomparison Project (CMIP6) experiments, we attempted to investigate TC variabilities and their changes in the past and future climates. Specifically, our ensemble CNNs project a decreasing trend of global mean TC activity in the future warming scenario, which is consistent with our dynamic projections using TC-permitting high-resolution coupled climate model.

Atmospheric aerosols influence the radiation budget, cloud amount, cloud properties, and surface albedos of sea ice and snow over the Arctic. In spite of their climatic importance, Arctic aerosol contains large uncertainties due to limited observations. This study evaluates the Arctic aerosol variability in three reanalyses, JRAero, CAMSRA, and MERRA2, in terms of the aerosol optical depth (AOD), and its relationship to the atmospheric disturbances on synoptic timescales. The AOD becomes highest in July–August over most of the Arctic regions, except for the North Atlantic and Greenland, where monthly variability is rather small. The three reanalyses show a general consistency in the horizontal distribution and temporal variability of the total AOD in summer. In contrast, the contributions of individual aerosol species to the total AOD are quite different among the reanalyses. Compared with observations, the AOD variability is represented well in all reanalyses in summer with high correlation coefficients, albeit exhibiting errors as large as the average AOD. The composite analysis shows that large aerosol emissions in Northern Eurasia and Alaska and transport by a typical atmospheric circulation pattern contribute to the high aerosol loading events in each area of the Arctic. Meanwhile, the empirical orthogonal function analysis depicts that the first- and second-largest AOD variabilities on the synoptic timescales appear over Northern Eurasia. Our results indicate that these summertime AOD variabilities mainly result from aerosol transportation and deposition due to the atmospheric disturbances on synoptic scales, suggesting an essential role played by Arctic cyclones.

The composite structure of the Madden-Julian Oscillation (MJO) has long been known to feature pronounced Rossby gyres in the subtropical upper troposphere, whose existence can be interpreted as the forced response to convective heating anomalies in the presence of a subtropical westerly jet. Here we inquire as to whether these forced gyre circulations have any subsequent effects on divergence patterns in the tropics. A nonlinear spherical shallow water model is used to investigate how the introduction of different background jet profiles affects the model’s steady-state response to an imposed MJO-like thermal forcing. Results show that a stronger jet leads to a stronger Kelvin-mode response in the tropics up to a critical jet speed, along with stronger divergence anomalies in the vicinity of the forcing. To understand this behavior, additional calculations are performed in which a localized vorticity forcing is imposed in the extratropics, without any thermal forcing in the tropics. The response is once again seen to include pronounced equatorial Kelvin waves, provided the jet is of sufficient amplitude. A detailed analysis of the vorticity budget reveals that the zonal-mean zonal wind shear plays a key role in amplifying the Kelvin-mode divergent winds near the equator. These results help to explain why the MJO tends to be strongest during boreal winter when the Indo-Pacific jet is typically at its strongest.

Solar-induced fluorescence (SIF) shows enormous promise as a proxy for photosynthesis and as a tool for modeling variability in gross primary productivity (GPP) and net biosphere exchange (NBE). In this study, we explore the skill of SIF and other vegetation indicators in predicting variability in global atmospheric CO2 observations, and thus global variability in NBE. We do so using a four-year record of global CO2 observations from NASA’s Orbiting Carbon Observatory 2 (OCO-2) satellite and using a geostatistical inverse model. We find that existing SIF products closely correlate with space-time variability in atmospheric CO2 observations in the extra-tropics but show weaker explanatory power across the tropics. In the extra-tropics, all SIF products exhibit greater skill in explaining variability in atmospheric CO2 observations compared to an ensemble of process-based CO2 flux models and other vegetation indicators. Furthermore, we find that using SIF as a predictor variable in the geosatistical inverse model shifts the seasonal cycle of estimated NBE and yields an earlier end to the growing season relative to other vegetation indicators. In tropical biomes, by contrast, the seasonal cycles of SIF products and estimated NBE are out of phase, and existing respiration and biomass burning estimates do not reconcile this discrepancy. Overall, our results highlight several advantages and challenges of using SIF products to help predict global variability in GPP and NBE.

Upper thermosphere mass density over the declining phase of solar cycle 23 are investigated using a day-to-night ratio (DNR) of thermosphere properties as a metric to evaluate how much relative change occurs climatologically between day and night. CHAMP observations from 2002-2009, MSIS 2.0 output, and TIEGCM V2.0 simulations are analyzed to assess their relative response in DNR. The CHAMP observations demonstrate nightside densities decrease more significantly than dayside densities as solar flux decreases. This causes a steadily increasing CHAMP mass density DNR from two to four with decreasing solar flux. The MSIS 2.0 nightside densities decrease more significantly than the dayside, resulting in the same trend as CHAMP. TIEGCM V2.0 displays an opposing trend in density DNR with decreasing solar flux due to dayside densities decreasing more significantly than nightside densities. A sensitivity analysis of the two models reveals the TIEGCM V2.0 to have greater sensitivity in temperature to levels of solar flux, while MSIS 2.0 displayed a greater sensitivity in mean molecular weight. The pressure DNR from both models contributed the most to the density DNR value at 400 km. As solar flux decreases, the two models’ estimate of pressure DNR deviate appreciably and trend in opposite directions. The TIEGCM V2.0 dayside temperatures during middle-to-low solar flux are too cold relative to MSIS 2.0. Increasing the dayside temperature values by about 50 – 100 K and decreasing the nightside temperature slightly would bring the TIEGCM V2.0 into better agreement with MSIS 2.0 and CHAMP observations.

In the present study, using sixty-three and fifty-six years of continuous observations, we investigate the long-term oscillations and residual trends, respectively, in the E- and F-region ionosonde measured parameters over Juliusruh, Europe. Using the Lomb-Scargle periodogram (LSP) long-term variations are estimated before the trend estimation. We found that the amplitude of the annual oscillation is higher than the 11-year solar cycle variation in the critical frequencies of the daytime E (foE) and Es (foEs) layers. A weak semi-annual oscillation is also identified in the foE. In the F-region, except for daytime hmF2, and nighttime foF2, the amplitude of the 11-year solar cycle variation is higher than the annual oscillation. The LSP estimated periods and their corresponding amplitudes are used to construct a model E- and F-region ionospheric parameters that are in good agreement with the observation. The linear trend estimation is derived by applying a least-squares fit analysis to the residuals, subtracting the model from the observation. Except for the daytime foF2, all the other parameters like nighttime foF2, day and nighttime h’F, and hmF2 show a negative trend. Present results suggest that the greenhouse effect is a prime driver for the observed long-term trend in the F-region. Interestingly, weak negative trends in the foE and foEs are found which contradicts an earlier investigation. The present study suggests that the changes in the upper stratospheric ozone and mesosphere wind shear variability could be the main driver for the observed weak negative trends in the foE, and foEs, respectively.

This paper expands on work showing that the winter North Atlantic Oscillation (NAO) is predictable on decadal timescales to quantify the skill in capturing the North Atlantic eddy-driven jet’s location and speed. By focussing on decadal predictions made for years 2-9 from the 6th Coupled Model Intercomparison Project over 1960-2005 we find that there is significant skill in both jet latitude and speed associated with the skill in the NAO. However, the skill in all three metrics appears to be sensitive to the period over which it is assessed. In particular, the skill drops considerably when evaluating hindcasts up to the present day as models fail to capture the latest observed northern shift and strengthening of the winter eddy-driven jet and more positive NAO. We suggest the drop in atmospheric circulation skill is related to reduced skill in North Atlantic Sea surface temperature.

We performed a statistical study of electromagnetic ion cyclotron (EMIC) wave distributions and their coupling with energetic protons in the inner magnetosphere using the Arase satellite data from May 2017 to December 2020. We investigated the energetic proton pitch-angle distributions and partial thermal pressures associated with EMIC waves using inter-calibrated proton data in the energy range of 30 eV/q-187 keV/q. With a cold plasma approximation, we computed the proton minimum resonance energies using the observed EMIC wave frequency and plasma density values. We found that the EMIC waves had left-handed polarization near the magnetic equator close to the threshold of proton cyclotron instability, and propagated to higher latitudes along the field line with polarization reversal. H-EMIC waves showed two peak occurrence regions in the morning and noon sectors at L=7.5-9 outside the plasmasphere. The flux enhancements associated with morning side H-EMIC waves appeared at E<1 keV/q among all pitch angles, while H-EMIC waves in the noon sector exhibited flux enhancement in field-aligned directions at E=1-100 keV/q. He-EMIC waves showed a broad occurrence region from 12 to 20 magnetic local time at L=5.5-8.5 inside the plasmasphere with strong flux enhancements at all pitch-angle ranges at E=1-100 keV/q. The proton minimum resonance energy using the obtained central frequency was consistent with the observed flux enhancements at different peak occurrence regions. We conclude that the free energy sources of EMIC waves in different geomagnetic environments drive the two different types of EMIC waves, and they interact with energetic protons at different energy ranges.

The Antarctica Peninsula (AP) has experienced more frequent and intense surface melting in recent years, jeopardizing the stability of ice shelves and ultimately leading to ice loss. Among the key phenomena that can initiate surface melting are atmospheric rivers (ARs) and leeside foehn; the combined impact of ARs and foehn led to moderate surface warming over the AP in December 2018 and record-breaking surface melting in February 2022. This study uses high-resolution Polar WRF simulations with advanced model configurations, Reference Elevation Model of Antarctica topography information, and surface observed albedo to improve our understanding of the relationship between ARs and foehn and their impacts on surface warming. With an intense AR (AR3) intrusion during the 2022 event, weak low-level blocking and heavy orographic precipitation on the upwind side resulted in latent heat release, which led to a more deep-foehn like case. On the leeside, sensible heat flux associated with the foehn magnitude was the major driver during the night and the secondary contributor during the day due to a stationary orographic gravity wave. Downward shortwave radiation was enhanced via cloud clearance, especially after the peak of the AR/foehn events, and dominated surface warming over the northeastern AP during the daytime. However, due to the complex terrain of the AP, ARs can complicate the foehn event by transporting extra moisture to the leeside via gap flows. During the peak of the 2022 foehn warming, cloud formation on the leeside hampered the downward shortwave radiation and slightly increased the downward longwave radiation.

The nighttime ionospheric response to a geomagnetic storm occurred on 23-29 September 2020 is investigated over the South American, Atlantic, and West African longitude sectors using NASA’s Global-scale Observations of the Limb and Disk (GOLD) measurements. On 27 September the solar wind conditions were favorable for prompt penetration electric fields (PPEF) to influence the equatorial ionosphere over extended longitudes. The equatorial ionization anomaly (EIA) crests were shifted 8o-10o poleward compared to the quiet time monthly mean across ~65o- 35oW during the main phase. Ionosonde hmF2 (peak electron density height) measurements from Fortaleza (GG: -3.9oN and -38.4oW) indicated a stronger prereversal enhancement this evening than other nights. As a result, Equatorial Plasma Bubbles (EPB) occurred at these longitudes on this evening. This is the first simultaneous investigation of EIA morphology and EPB occurrence rate over an extended longitude range from geostationary orbit during a geomagnetic storm.

Rotational temperatures in the Mesosphere-Lower Thermosphere region are estimated by utilizing the OH(6,2) Meinel band nightglow emission observed with an Ebert-Fastie Spectrometer (EFS) operated at Arecibo Observatory (AO), Puerto Rico (18.35oN, 66.75oW) during February-April 2005. To validate the estimated rotational temperatures, a comparison with temperatures obtained from a co-located Potassium Temperature Lidar (K-Lidar) and overhead passes of the Sounding of the Atmosphere by Broadband Emission Radiometry (SABER) instrument onboard NASA’s TIMED satellite are performed. Two types of weighting functions are applied on the K-Lidar temperatures to compare them with EFS temperatures. The first type has a fixed peak altitude and a fixed full width at half maximum (FWHM) for the whole night. In the second type, the peak altitude and FWHM vary with the local time. Average temperature difference between the EFS and K-Lidar obtained with both types of weighting functions are comparable with the previously published results from different latitude-longitude sectors. Further, it is found that temperature comparison improves when the time varying weighting functions are considered. On the other hand, we have shown that comparison of temperatures obtained from these two instruments could provide a better estimate of the OH(6,2) peak altitudes if a reference temporal trend of the OH(6,2) peak altitudes is available. Also, it is noticed that there are significant differences between the seasonal mean OH(6,2) peak altitudes obtained from SABER observation and model calculation. Such a detailed study using the AO EFS data has not previously been carried out.

Climate models still need to be improved in their capability of reproducing the present climate at both global and regional scale. The assessment of their performance depends on the datasets used as comparators. Reanalysis and gridded (homogenized or not homogenized) observational datasets have been frequently used for this purpose. However, none of these can be considered a reference dataset. Here, for the first time, using in-situ measurements from NOAA U.S. Climate Reference Network (USCRN), a network of 139 stations with high-quality instruments deployed across the continental U.S, daily temperature, and precipitation from a suite of dynamically downscaled regional climate models (RCMs; driven by ERA-Interim) involved in NA-CORDEX are assessed. The assessment is extended also to the most recent and modern widely used reanalysis (ERA5, ERA-Interim, MERRA2, NARR) and gridded observational datasets (Daymet, PRISM, Livneh, CPC). Results show that biases for the different datasets are mainly seasonal and subregional dependent. On average, reanalysis and in-situ-based datasets are generally warmer than USCRN year-round, while models are colder (warmer) in winter (summer). In-situ-based datasets provide the best performance in most of the CONUS regions compared to reanalysis and models, but still have biases in regions such as the Midwest mountains and the Northwestern Pacific. Results also highlight that reanalysis does not outperform RCMs in most of the U.S. subregions. Likewise, for both reanalysis and models, temperature and precipitation biases are also significantly depending on the orography, with larger temperature biases for coarser model resolutions and precipitation biases for reanalysis.

The prevalence of mixing vs. precipitation processes in biomass burning aerosol (BBA) laden air over the southeast Atlantic is assessed during three intensive observation periods during the NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) campaign. Air in the lower free troposphere (FT) and marine boundary layer (MBL) are treated as separate analyses, although connections are made where relevant. The study, centering on aircraft in-situ measurements of total water heavy isotope ratios, has two main objectives. The first is to gauge whether the atmospheric hydrology, and in particular precipitation, can be constrained primarily through visual assessment of the aircraft isotope ratio data plotted against total water concentration, similarly to several previous studies. However, regression of the data onto a simple model of convective detrainment is also used and alludes to the possibility of a precipitation data product derived from isotope ratios. The second objective is to connect variations in aerosol concentrations to the hydrology as diagnosed by the isotope ratio measurements, and determine whether aerosol variations are attributable to wet scavenging. First, joint water concentration (q) and H2O/HDO isotope measurements (δD) in the lower FT are combined with satellite and MERRA-2 data into simple analytical models to constrain hydrologic histories of BBA-laden air originating over Africa and flowing over the southeast Atlantic. We find that even simple models are capable of detecting and constraining the primary processes at play. Further, a strong correlation between isotopic evidence of precipitation in lower FT air masses and an in-situ indicator of wet scavenging of black carbon – the ratio of black carbon to carbon monoxide (BC/CO) – is shown. In comparison, the correlation between BC/CO and the water concentration itself is low. Since wet scavenging is the primary removal mechanism of black carbon, these findings suggest that isotope measurements could support studies constraining the lifetime of black carbon in the FT. Next, the ability of measurements interpreted with simple analytical models in (q, δD) space to distinguish cloud-top entrainment vs. precipitation signals in the MBL is tested. This proves more difficult than the lower FT analysis since signals are smaller. We find that the largest obstacle to this goal is the (q, δD) values of the entrained airmass at cloud-top. We also compare cloud condensation nuclei (CCN) concentrations in the sub-cloud layer to the isotopic measurements. In 2016 and 2018 IOPs, lower CCN concentrations coincide with isotope ratio evidence of precipitation, indicating aerosol scavenging. However, a more complex model simulating water, isotope ratios, and aerosols would be necessary to achieve more definitive conclusions. For the 2017 IOP, with the highest sub-cloud CCN concentrations, there is no connection between precipitation signals and CCN concentrations.

We show the first achievement of inferring the electron temperature in ionospheric conditions from synthetic data using fixed-bias Langmuir probes operating in the electron saturation region. This was done by using machine learning and altering the probe geometry. The electron temperature is inferred at the same rate as the currents are sampled by the probes. For inferring the electron temperature along with the electron density and the floating potential, a minimum number of three probes is required. Furthermore does one probe geometry need to be distinct from the other two, since otherwise the probe setup may be insensitive to temperature. This can be achieved by having either one shorter probe or a probe of a different geometry, e.g. two longer and a shorter cylindrical probe or two cylindrical probes and a spherical probe. We use synthetic plasma parameter data and calculate the synthetic collected probe currents to train a neural network and verify the results with a test set. We additionally verify the validity of the inferred temperature in altitudes ranging from about 100 km-500 km, using data from the International Reference Ionosphere model. Even minor changes in the probe sizing enable the temperature inference and result in root mean square relative errors between inferred and ground truth data of under 3%. When limiting the temperature inference to 120-450 km altitude an RMSRE of under 0.7% is achieved for all probe setups. In future, the multi-needle Langmuir Probe instrument dimensions can be adapted for higher temperature inference accuracy.