On the relative roles of dynamics and chemistry governing the abundance and diurnal variation of low latitude thermospheric nitric oxide

We use data from two NASA satellites, the Thermosphere Ionosphere Energetics and Dynamics (TIMED) and the Aeronomy of Ice in the Mesosphere (AIM) satellites in conjunction with model simulations from the Thermosphere-Ionosphere-Mesosphere-Electrodynamics General Circulation Model (TIME-GCM) to elucidate the key dynamical and chemical factors governing the abundance and diurnal variation of nitric oxide (NO) at near solar minimum conditions and low latitudes. This analysis was enabled by the recent orbital precession of the AIM satellite which caused the solar occultation pattern measured by the Solar Occultation for Ice Experiment (SOFIE) to migrate down to low and mid latitudes for specific periods of time. We use a month of NO data collected in January 2017 to compare with two versions of the TIME-GCM, one driven solely by climatological tides and analysis-derived planetary waves at the lower boundary and free running at all other altitudes, while the other is constrained by a high-altitude analysis from the Navy Global Environmental Model (NAVGEM)up to the mesopause. We also compare SOFIE data with a NO climatology from the Nitric Oxide Empirical Model (NOEM). Both SOFIE and NOEM yield peak NO abundances of around 4×107cm−3; however, the SOFIE profile peaks about 6-8 km lower than NOEM. We show that this difference is likely a local time effect; SOFIE being a dawn measurement and NOEM representing late morning/near noon. The constrained version of TIME-GCM exhibits a low altitude dawn peak while the model that is forced solely at the lower boundary and free running above does not. We attribute this difference due to a phase change in the semi-diurnal tide in the NAVGEM-constrained model causing descent of high NO mixing ratio air near dawn. This phase difference between the two models arises due to differences in the mesospheric zonal mean zonal winds. Regarding the absolute NO abundance, all versions of the TIME-GCM overestimate this. Tuning the model to yield calculated atomic oxygen in agreement with TIMED data helps, but is insufficient. Further, the TIME-GCM underestimates the electron density [e-] as compared with the International Reference Ionosphere empirical model. This suggests a potential conflict with the requirements of NO modeling and [e-] modeling since one solution typically used to increase model [e-] is to increase the solar soft X ray flux which would, in this case, worsen the NO model/data discrepancy

Simulation of the 21 August 2017 Solar Eclipse Using the Whole Atmosphere Community Climate Model-eXtended

We performed simulations of the atmosphere‐ionosphere response to the solar eclipse of 21 August 2017 using the Whole Atmosphere Community Climate Model‐eXtended (WACCM‐X v. 2.0) with a fully interactive ionosphere and thermosphere. Eclipse simulations show temperature changes in the path of totality up to −3 K near the surface, −1 K at the stratopause, ±4 K in the mesosphere, and −40 K in the thermosphere. In the F region ionosphere, electron density is depleted by about 55%. Both the temperature and electron density exhibit global effects in the hours following the eclipse. There are also significant effects on stratosphere‐mesosphere chemistry, including an increase in ozone by nearly a factor of 2 at 65 km. Dynamical impacts of the eclipse in the lower atmosphere appear to propagate to the upper atmosphere. This study provides insight into coupled eclipse effects through the entire atmosphere from the surface through the ionosphere

HL‐TWiM Empirical Model of High‐Latitude Upper Thermospheric Winds

We present an empirical model of thermospheric winds (High‐latitude Thermospheric Wind Model [HL‐TWiM]) that specifies F region high‐latitude horizontal neutral winds as a function of day of year, latitude, longitude, local time, and geomagnetic activity. HL‐TWiM represents the large‐scale neutral wind circulation, in geomagnetic coordinates, for the given input conditions. The model synthesizes the most extensive collection to date of historical high‐latitude wind measurements; it is based on statistical analyses of several decades of F region thermospheric wind measurements from 21 ground‐based stations (Fabry‐Perot Interferometers and Scanning Doppler Imaging Fabry‐Perot Interferometers) located at various northern and southern high latitudes and two space‐based instruments (UARS WINDII and GOCE). The geomagnetic latitude and local time dependences in HL‐TWiM are represented using vector spherical harmonics, day of year and longitude variations are represented using simple harmonic functions, and the geomagnetic activity dependence is represented using quadratic B splines. In this paper, we describe the HL‐TWiM formulation and fitting procedures, and we verify the model against the neutral wind databases used in its formulation. HL‐TWiM provides a necessary benchmark for validating new wind observations and tuning our physical understanding of complex wind behaviors. Results show stronger Universal Time variation in winds at southern than northern high latitudes. Model‐data intra‐annual comparisons in this study show semiannual oscillation‐like behavior of GOCE winds, rarely observed before in wind data

Toward an improved representation of middle atmospheric dynamics thanks to the ARISE project

This paper reviews recent progress toward understanding the dynamics of the middle atmosphere in the framework of the Atmospheric Dynamics Research InfraStructure in Europe (ARISE) initiative. The middle atmosphere, integrating the stratosphere and mesosphere, is a crucial region which influences tropospheric weather and climate. Enhancing the understanding of middle atmosphere dynamics requires improved measurement of the propagation and breaking of planetary and gravity waves originating in the lowest levels of the atmosphere. Inter-comparison studies have shown large discrepancies between observations and models, especially during unresolved disturbances such as sudden stratospheric warmings for which model accuracy is poorer due to a lack of observational constraints. Correctly predicting the variability of the middle atmosphere can lead to improvements in tropospheric weather forecasts on timescales of weeks to season. The ARISE project integrates different station networks providing observations from ground to the lower thermosphere, including the infrasound system developed for the Comprehensive Nuclear-Test-Ban Treaty verification, the Lidar Network for the Detection of Atmospheric Composition Change, complementary meteor radars, wind radiometers, ionospheric sounders and satellites. This paper presents several examples which show how multi-instrument observations can provide a better description of the vertical dynamics structure of the middle atmosphere, especially during large disturbances such as gravity waves activity and stratospheric warming events. The paper then demonstrates the interest of ARISE data in data assimilation for weather forecasting and re-analyzes the determination of dynamics evolution with climate change and the monitoring of atmospheric extreme events which have an atmospheric signature, such as thunderstorms or volcanic eruptions

Brain age predicts mortality

Age-associated disease and disability are placing a growing burden on society. However, ageing does not affect people uniformly. Hence, markers of the underlying biological ageing process are needed to help identify people at increased risk of age-associated physical and cognitive impairments and ultimately, death. Here, we present such a biomarker, ‘brain-predicted age’, derived using structural neuroimaging. Brain-predicted age was calculated using machine-learning analysis, trained on neuroimaging data from a large healthy reference sample (N = 2001), then tested in the Lothian Birth Cohort 1936 (N = 669), to determine relationships with age-associated functional measures and mortality. Having a brain-predicted age indicative of an older-appearing brain was associated with: weaker grip strength, poorer lung function, slower walking speed, lower fluid intelligence, higher allostatic load and increased mortality risk. Furthermore, while combining brain-predicted age with grey matter and cerebrospinal fluid volumes (themselves strong predictors) not did improve mortality risk prediction, the combination of brain-predicted age and DNA-methylation-predicted age did. This indicates that neuroimaging and epigenetics measures of ageing can provide complementary data regarding health outcomes. Our study introduces a clinically-relevant neuroimaging ageing biomarker and demonstrates that combining distinct measurements of biological ageing further helps to determine risk of age-related deterioration and death

Genetic basis of a cognitive complexity metric

Relational complexity (RC) is a metric reflecting capacity limitation in relational processing. It plays a crucial role in higher cognitive processes and is an endophenotype for several disorders. However, the genetic underpinnings of complex relational processing have not been investigated. Using the classical twin model, we estimated the heritability of RC and genetic overlap with intelligence (IQ), reasoning, and working memory in a twin and sibling sample aged 15-29 years (N = 787). Further, in an exploratory search for genetic loci contributing to RC, we examined associated genetic markers and genes in our Discovery sample and selected loci for replication in four independent samples (ALSPAC, LBC1936, NTR, NCNG), followed by meta-analysis (N>6500) at the single marker level. Twin modelling showed RC is highly heritable (67%), has considerable genetic overlap with IQ (59%), and is a major component of genetic covariation between reasoning and working memory (72%). At the molecular level, we found preliminary support for four single-marker loci (one in the gene DGKB), and at a gene-based level for the NPS gene, having influence on cognition. These results indicate that genetic sources influencing relational processing are a key component of the genetic architecture of broader cognitive abilities. Further, they suggest a genetic cascade, whereby genetic factors influencing capacity limitation in relational processing have a flow-on effect to more complex cognitive traits, including reasoning and working memory, and ultimately, IQ