The historical record of rainfall observation rarely provides sufficient record length and resolution required in many applications. The work described here presents a stochastic framework for long-term simulations of non-tropical storms at high spatial and temporal resolution. The framework adopts optimal estimation for spatio-temporal modeling of rain fields. A non-parametric approach featuring K-Nearest Neighbor Resampling (KNNR) plus the Genetic Algorithm (GA) mixing process is utilized for generating parameters in the long-term simulation. A case study is conducted in Dallas-Fort-Worth metroplex as the simulation domain. Ensemble parameters are generated using the KNNR+GA method from adjacent homogeneous areas and 10 years of radar rainfall observation. One hundred most rainy days in the 10 years are simulated at the resolutions of 4 × 4 km and 1 hour for 50 ensemble members. The simulated rainfall is thoroughly evaluated against the observed radar rainfall with respects to statistical moments, spatio-temporal structure, and frequency distribution of rainfall at both near-point scale and domain scale. The results indicate that ensemble simulations successfully reproduce key statistical properties of the observed rainfall. In addition, the approach is also effective and flexible in capturing heavy rainfall values, which is important for many hydrologic/hydraulic practices. As essentially a downscaling tool, this stochastic rainfall generator can have many applications where rainfall needs to be represented at finer spatiotemporal resolution.

Machine learning has been widely applied in numerical weather prediction, but the incorporation of new observational sites into models trained on stations with long historical records remains a challenge. Here we propose a post-processing framework consisting of three machine learning methods: station clustering with K-means, temperature prediction based on decision trees, and transfer learning for newly-built stations. We apply this framework to post-processing forecasts of surface air temperature at 301 weather stations in China. The results show significant reductions (as much as 39.4%~20.0%) in the root-mean-square error of operational forecasts at lead times as long as 7 days. Moreover, the use of transfer learning to incorporate new stations improves forecasts at the new site by 36.4% after only one year of data collection. These results demonstrate the potential for clustering and transfer learning to boost existing applications of machine learning techniques in weather forecasting.

A Bayesian data assimilation scheme is formulated for advection-dominated or hyperbolic evolutionary problems, and observations. It uses the physics to dynamically update the likelihood in order to extend the impact of the likelihood on the posterior, a strategy that would be particularly useful when the the observation network is sparse in space and time and the associated measurement uncertainties are low. The filter is applied to a problem with linear dynamics and Gaussian statistics, and compared to the exact estimate, a model outcome, and the Kalman filter estimate. By comparing to the exact estimate the dynamic likelihood filter is shown to be superior to model outcomes and to the Kalman estimate, when the observation system is sparse. The added computational expense of the method is linear in the number of observations and thus computationally efficient, suggesting that the method is practical even if the space dimensions of the physical problem are large.

By solving Laplace’s tidal equations along the equatorial Pacific thermocline, assuming a delayed-differential effective gravity forcing due to a combined lunar+solar (lunisolar) stimulus, we are able to precisely match ENSO periodic variations over wide intervals. The underlying pattern is difficult to decode by conventional means such as spectral analysis, which is why it has remained hidden for so long, despite the excellent agreement in the time-domain. What occurs is that a non-linear seasonal modulation with monthly and fortnightly lunar impulses along with a biennially-aligned “see-saw” is enough to cause a physical aliasing and thus multiple folding in the frequency spectrum. So, instead of a conventional spectral tidal decomposition, we opted for a time-domain cross-validating approach to calibrate the amplitude and phasing of the lunisolar cycles. As the lunar forcing consists of three fundamental periods (draconic, anomalistic, synodic), we used the measured Earth’s length-of-day (LOD) decomposed and resolved at a monthly time-scale [1] to align the amplitude and phase precisely. Even slight variations from the known values of the long-period tides will degrade the fit, so a high-resolution calibration is possible. Moreover, a narrow training segment from 1880-1920 using NINO34/SOI data is adequate to extrapolate the cycles of the past 100 years (see attached figure). To further understand the biennial impact of a yearly differential-delay, we were able to also decompose using difference equations the historical sea-level-height readings at Sydney harbor to clearly expose the ENSO behavior. Finally, the ENSO lunisolar model was validated by back-extrapolating to Unified ENSO coral proxy (UEP) records dating to 1650. The quasi-biennial oscillation (QBO) behavior of equatorial stratospheric winds derives following a similar pattern to ENSO via the tidal equations, but with an emphasis on draconic forcing. This improvement in ENSO and QBO understanding has implications for vastly simplifying global climate models due to the straightforward application of a well-known and well-calibrated forcing. [1] Na, Sung-Ho, et al. “Characteristics of Perturbations in Recent Length of Day and Polar Motion.” Journal of Astronomy and Space Sciences 30 (2013): 33-41.

This study identifies the role of mid-latitude teleconnection in determining the interannual variability of the Indian summer monsoon in the CFSv2 model. Since CFSv2 has been identified as a potential forecast model for the Indian summer monsoon, it is important to understand the factors that determine its prediction skills at seasonal timescales. ENSO is one of the most important factors driving Indian monsoon variability at seasonal timescales. It is represented realistically in CFSv2. The model, however, misses associated mid-latitude teleconnections. We show that the inadequate strength of mid-latitude teleconnections, especially from the North Atlantic and North-western Pacific can be the primary reasons for the weaker monsoon variability, despite strong ENSO-Monsoon relationship in the model.

It is now widely accepted that cumulus cloud size distributions follow power-laws, at least over part of the cloud size spectrum. Providing reliable fits to empirical size distributions is however not a simple task, and this is reflected by the large spread in power-law exponents reported in the literature. Two well-documented idealized high-resolution numerical simulations of convective situations are here performed and analyzed in order to gain a clearer understanding of cumulus size distributions. Advanced statistical methods, including maximum likelihood estimators and goodness-of-fit tests, are employed to produce the most accurate fits possible. Various candidate distributions are tested including exponentials, power-laws and other heavy-tail functions. Size distributions estimated from clouds identified just above cloud base are found to be best modeled by exponential distributions. If one considers instead clouds identified from an integrated condensed water path, robust power-law behaviors start to emerge, in particular when deep convection is involved. In general however, these empirical distributions are best represented by alternative heavy-tail distributions such as the Weibull or cutoff power-law distributions. In an attempt to explain these results, it is suggested that exponential size distributions characterize a population where clouds interact only weakly, whereas heavy-tail distributions are the manifestation of a cloud population that self-organizes towards a critical state.

Being in an arid zone that is frequently submitted to high winds, south-central Arizona regularly gets impacted by several blowing dust events or dust storms every year. Major consequences of these events are visibility impairment and ensuing road traffic accidents, and a variety of health issues induced by inhalation of polluted air loaded with fine particulate matter produced by wind erosion. Despite such problems, and thus a need for guidance on mitigation efforts, studies dealing with dust source attribution for the region are largely missing. Furthermore, existing dust models exhibit large uncertainties and deficiencies in simulating dust events, rendering them of limited use in attribution studies or early warning systems. Therefore, to address some of these model issues, we have developed a high-resolution (1 km) dust modeling system by building upon an existing modeling framework consisting of Weather Research and Forecasting (WRF), FENGSHA (a dust emission model), and Community Multiscale Air Quality (CMAQ) models. In addition to incorporating new representations in the dust emission scheme, including roughness correction factor, sandblasting efficiency, and dust source mask, we implemented, in the dust model, up-to-date and very high-resolution data on land use, soil texture, and vegetation index. We used the revised dust modeling system to simulate a springtime dust storm (08–09 April 2013) of relatively long duration that caused a regional traffic incident involving minor injuries. The model simulations compared reasonably well against observations of concentration of particulate matter with a diameter of 10 μm and smaller (PM₁₀) and satellite-derived dust optical depth and vertical profile of aerosol subtypes. Interestingly, simulation results revealed that the anthropogenic (cropland) dust sources contributed more than half (~53 % or 260 µg/m³) of total PM₁₀, during the dust storm, over the region including Phoenix and western Pinal County. Contrary to the conventional wisdom that desert is the main dust source, our findings for this region challenge such belief and suggest that the regional air quality modeling over dryland regions should emphasize an improved representation of dust from agricultural lands as well, especially during high wind episodes. Such representations have the potential to inform decision-making in order to reduce windblown dust-related hazards on public health and safety.

Global change is a change in the planetary energy balance. It is usually expressed as a change in near-surface (2 m) air temperature (Ta), but changes to Ta represent only part of the atmospheric energy balance, which includes specific humidity (q) and more. We analyzed MERRA-2 reanalysis data and 15 Atmospheric Model Intercomparison Project (AMIP) models over the 1980-2014 period. Some 41%, 37%, and 49% of the near-surface atmosphere showed significant increases in ET, ESH, and E, respectively. The average increase in ET (ESH) was 10.6 J kg−1 year−1 (11.5 J kg−1 year−1) but AMIP models estimated that ET (14.5 J kg−1 year−1) exceeded ESH (13.7 J kg−1 year−1). Global near-surface Ta would have increased at more than twice the observed rate if energy was not partitioned into latent heat. Results demonstrate the critical role that q plays in recent changes to near-surface atmospheric energy.

Satellites provide the primary dataset for monitoring the earth system and constraining analyses in numerical models. A challenge for utilizing satellite radiances is the estimation of their biases. High-accuracy non-radiance data are typically employed to anchor radiance bias corrections. This study provides the first assessment of impacts of dropsonde data collected during the Atmospheric River (AR) Reconnaissance program that samples ARs over the Northeast Pacific on the radiance assimilation using the Global Forecast System (GFS) and the Global Data Assimilation System. Including this dropsonde dataset has provided better anchoring for bias corrections and improved model background, leading to an increase of ~5-10% in the amount of assimilated microwave radiance in the lower/middle troposphere over the Northeast Pacific and North America. The impact on tropospheric infrared radiance is small but also beneficial. This result points to the usefulness of dropsondes, along with other conventional data, in the assimilation of satellite radiance.

It is increasingly recognized that light-absorbing impurities deposited on a surface can reduce its albedo and lead to increased absorption of solar radiation. Natural dust can travel substantial distances in the Earth’s atmosphere from its original source. It affects all climatic zones from the tropics to the poles and it may have a regional or global impact on air quality and human health. In the Arctic, a rapid increase in temperature compared to the global change, known as Arctic Amplification, is closely linked to snow albedo feedback. Furthermore, recent studies detail an extreme climate change scenario in the history of our planet that lead to catastrophic cascading events and global mass extinction triggered by atmospheric soot injections. Therefore, knowledge of optical properties of dust particles is important for improved climate models and dust effect studies. Here we report detailed results of multi-angular polarized measurements of light scattered by volcanic sand particles obtained with the FIGIFIGO goniospectrometer (Peltoniemi et al. 2014). The design concept of this custom made instrument has a well designed user friendly interface, a high level of automation, and an excellent adaptability to a wide range of weather conditions during field measurements. The foreoptics is connected to an ASD FieldSpec Pro FR 350-2500 nm spectroradiometer by an optical fiber. A calcite Glan-Thompson prism is used as a polarizer, covering the full spectral range with better than 1% accuracy. The samples studied in this work were collected from the Mýrdalssandur area in Iceland (in March 2016) and from the Villarica area in Chile (in July 2019). Following established FGI practices in laboratory conditions samples are further divided into the following categories: (1) natural volcanic sand, (2) sieved volcanic sand (dust) where the size of the particles is less than 250 μm, including dry and wet sample condition, and (3) a fine-grained powder of milled volcanic sand measurable also as aerosol. The potential use of the results from our measurements are diverse, including their use as a ground truth reference for Earth Observation and remote sensing studies, estimating climate change over time, as well as measuring other ecological effects caused by changes in atmospheric composition or land cover.

One of the key measurements from the Clouds and the Earth’s Radiant Energy System (CERES) satellite is Earth emitted or longwave (LW) radiation. The CERES Ocean Validation Experiment (COVE), located at Chesapeake Light Station, approximately 25 kilometers east of Virginia Beach, Virginia (coordinates: 36.90N, 75.71W) had provided surface validation for the CERES satellite measurements for many years. Upwelling LW radiation was one of the measurements made at COVE but was complicated due to the Light Station tower being in the upwelling LW instruments field of view. According to our estimates, the Light Station tower alters 15% of the upwelling LW radiation. An unwanted consequence of the tower being in the field of view was the tower radiating effect, particularly noticeable on clear, sunny days. During these days, the tower would radiate extra heat energy by as much as 3% (15 W/m^2) that was measured by the upwelling LW instrument. COVE follows the Baseline Surface Radiation Network requirements and their target uncertainty is 2%. To resolve this issue, we obtain a different upwelling longwave value using data from an infrared radiation thermometer (IRT) and a pyrgeometer that retrieves sea surface temperature (SST) and downwelling longwave respectively. Using an IRT allows conversion from SST to a water emission value and the pyrgeometer provides the reflected flux of the downward longwave radiation. By determining the extent of the undesirable obstruction in the field of view of the upwelling longwave instrument and determining its emissivity could allow others with similar issues to obtain the proper values of upwelling longwave measurements.

Current approaches to estimate NOx emissions fail to account for new and small sources, biomass burning, and sources which change rapidly in time, generally don’t account for measurement error, and are either based on models, or do not consider wind, chemistry, and dynamical effects. This work introduces a new, model-free analytical environment that assimilates daily TROPOMI NO2 measurements in a mass-conserving manner, to invert daily NOx emissions. This is applied over a rapidly developing and energy-consuming region of Northwest China, specifically chosen due to substantial economic and population changes, new environmental policies, large use of coal, and access to independent emissions measurements for validation, making this region representative of many rapidly developing regions found across the Global South. This technique computes a net NOx emissions gain of 70% distributed in a seesaw manner: a more than doubling of emissions in cleaner regions, chemical plants, and regions thought to be emissions-free, combined with a more than halving of emissions in city centers and at well-regulated steel and powerplants. The results allow attribution of sources, with major contributing factors computed to be increased combustion temperature, atmospheric transport, and in-situ chemical processing. It is hoped that these findings will drive a new look at emissions estimation and how it is related to remotely sensed measurements and associated uncertainties, especially applied to rapidly developing regions. This is especially important for understanding the loadings and impacts of short-lived climate forcers, and provides a bridge between remotely sensed data, measurement error, and models, while allowing for further improvement of identification of new, small, and rapidly changing sources.

The 2021 Pacific Northwest heatwave featured record-smashing high temperatures, raising questions about whether extremes are changing faster than the mean, and challenging our ability to estimate the probability of the event. Here, we identify and draw on the strong relationship between the climatological higher-order statistics of temperature (skewness and kurtosis) and the magnitude of extreme events to quantify the likelihood of comparable events using a large climate model ensemble (CESM2-LE). In general, CESM2 can simulate temperature anomalies as extreme as those observed in 2021, but they are rare: temperature anomalies that exceed 4.5σ occur with an approximate frequency of one in a hundred thousand years. The historical data does not indicate that the upper tail of temperature is warming faster than the mean; however, future projections for locations with similar climatological moments to the Pacific Northwest do show significant positive trends in the probability of the most extreme events.

Extratropical cyclones are weather phenomena with significant transfer of energy between the surface (over the ocean or on land) and the atmosphere. Recurrently, reanalysis data are used to understand the behavior of cyclonic tracks and to study extreme events, with constant updates and validations with the observational base in the Northern Hemisphere. However, studies using cyclone tracking in the Southwestern Atlantic, has proven more difficult. This disagreement seems to be in function of the structure and intensity of the forcing factors that influence both cyclogenesis and the displacement to the South Atlantic, when compared to the Northern Hemisphere. In this work, synoptic pressure charts at sea level, manually made and processed by the Brazilian Navy every 12 hours between the years 2010 and 2020, as a product resulting from a consensus among Navy meteorologists, were used to study the cyclonic pathways in the Southwestern Atlantic (METAREA V). Data obtained for all cyclones identified in the charts, based on their position and displacement, formed a database with 10737 cyclones, containing speed, dimensions, and pressure gradient. The cyclones identified have a higher radius frequency between 200/400 km and a faster-moving center shift. In addition, about 60% of cyclones associated with cold fronts have a life cycle ranging from 3 to 4 days. There is also a expressive cyclogenesis between latitudes 23ºS and 43ºS where, in austral autumn winter, increases its frequency over the ocean and close to the southern Brazilian coast. During spring, the greater cyclogenesis frequency occurs over the continent, close to Chaco area in Argentina and Uruguay. The impacts of these statistical figures on the south and southeastern Brazilian coast, mainly the continental insertion point of the cold fronts and cyclonic displacement that influence rough seas and storm surges, are discussed in this work. Keywords: EXTRATROPICAL CYCLONES, CYCLONE TRACK, SYNOPTIC CHARTS, SOUTHWESTERN ATLANTIC

Sparse observational data in developing regions leads to uncertainty about how hydro-climatic factors influence crop phases and productivity, knowledge of which is essential to mitigating food security threats induced by climate change. In this study, NASA Tropical Rainfall Measuring Mission (TRMM), Global Precipitation Measurement (GPM), and Global Land Data Assimilation System (GLDAS) data products bypass spatiotemporal limitations and drive machine learning algorithms developed to characterize hydro-climate-productivity interactions. Extensive feature engineering processes these products into nearly 4000 metrics, designed to decompose crop growing season hydro-climate conditions. Dimensionality reduction with bidirectional step-wise regression, Multi-Adaptive-Regression-Splines (MARS), and Random Forest algorithms are explored to determine key temporal hydro-climate drivers to agricultural productivity, with each method recognizing unique linear and non-linear predictors. Finally, multi-variate regression, MARS, and Random Forest models are trained on the drivers to predict seasonal crop yield. We apply this hydro-climate-productivity framework to investigate rabi wheat productivity on Pakistan’s Potohar Plateau. Here, we identify six of wheat’s ten phenological phases that display strong hydro-climate responses, with the shooting phase exhibiting sensitivity to precipitation intensity, minimum soil moisture, and sub-zero temperatures. In addition, the plateau’s heterogeneous climate-productivity connections are captured well by the calibrated models, strengthening their application for studying broader climate change impacts. The integration of remote sensing products and machine learning offers an effective framework to bypass in-situ data limitations and decompose climate-crop productivity relationships, thus improving drought onset recognition and food security forecasting.

Research on Atmospheric Rivers (ARs) has focused primarily on AR (thermo)dynamics and hydrological impacts over land. However, the evolution and potential role of nearshore air-sea fluxes during landfalling ARs are not well documented. Here, we examine synoptic evolutions of nearshore latent heat flux (LHF) during strong late-winter landfalling ARs (1979–2017) using 138 over-shelf buoys along the U. S. west coast. Composite evolutions show that ARs typically receive upward (absolute) LHF from the coastal ocean. LHF is small during landfall due to weak air-sea humidity gradients but is strongest (30–50 W/m^2 along the coast) 1–3 days before/after landfall. During El Niño winters, southern-coastal LHF strengthens, coincident with stronger ARs. A decomposition of LHF reveals that sea surface temperature (SST) anomalies modulated by the El Niño—Southern Oscillation dominate interannual LHF variations under ARs, suggesting a potential role for nearshore SST and LHF influencing the intensity of landfalling ARs.

Nudging is a ubiquitous capability of numerical weather and climate models that is widely used in a variety of applications (e.g. crude data assimilation, “intelligent’ interpolation between analysis times, constraining flow in tracer advection/diffusion simulations). Here, the focus is on the momentum nudging tendencies themselves, rather than the atmospheric state that results from application of the method. The initial intent was to interpret these tendencies as a quantitative estimation of model error (net parameterization error in particular). However, it was found that nudging tendencies depend strongly on the nudging time scale chosen, which is the primary result presented here. Reducing the nudging time scale reduces the difference between the model state and the target state, but much less so than the reduction in the nudging time scale, resulting in increased nudging tendencies. The dynamical core, in particular, appears to increasingly oppose nudging tendencies as the nudging time scale is reduced. These results suggest nudging tendencies cannot be quantitatively interpreted as model error. Still, nudging tendencies do contain some information on model errors and/or missing physical processes and still might be useful in model development and tuning, even if only qualitatively.

Intense snowfall sublimation was observed during a precipitation event over Davis in the Vestfold Hills, East Antarctica, from 08 to 10 January 2019. Radar observations and simulations from the Weather Research and Forecasting model revealed that orographic gravity waves (OGWs), generated by a north-easterly flow impinging on the ice ridge upstream of Davis, were responsible for snowfall sublimation through a Foehn effect. Despite the strong meridional moisture advection associated with an atmospheric river (AR) during this event, almost no precipitation reached the ground at Davis. We found that the direction of the synoptic flow with respect to the orography determined the intensity of OGWs over Davis, which in turn directly influenced the snowfall microphysics. Turbulence induced by the OGWs likely enhanced the aggregation process, as revealed by dual-polarization and dual-frequency radar observations. This study suggests that despite the intense AR, the precipitation distribution was determined by local processes tied to the orography. The mechanisms found in this case study could contribute to the extremely dry climate of the Vestfold Hills, one of the main Antarctic oasis.

US natural gas production increased by ~43% between 2005 and 2015, but there is disagreement in the scientific literature on whether this growth led to increased methane emissions. In this study, we evaluate the possible contributions of emissions versus meteorology to an upward trend in US atmospheric methane observations during 2007-2015. We find that interannual variability (IAV) in meteorology yields an apparent upward trend in atmospheric methane across much of the US. We further find that IAV in atmospheric methane at several observation sites is correlated with IAV in local wind speed. Overall, our results show that US trends in atmospheric methane largely reflect variability in meteorology, and are unlikely to be a direct reflection of trends in emissions. The results of this study therefore lend support for the conclusion that there was little upward trend in US methane emissions during this time.

Natural and non-natural factors have combined effects on the trajectory of COVID-19 pandemic, but it is difficult to make them separate. To address this problem, a two-stepped methodology is proposed. First, a compound natural factor (CNF) model is developed via assigning weight to each of seven investigated natural factors, i.e., temperature, humidity, visibility, wind speed, barometric pressure, aerosol and vegetation in order to show their coupling relationship with the COVID-19 trajectory. Onward, the empirical distribution based framework (EDBF) is employed to iteratively optimize the coupling relationship between trajectory and CNF to express the real interaction. In addition, the collected data is considered from the backdate, i.e., about 23 days—which contains 14-days incubation period and 9-days invalid human response time—due to the non-availability of prior information about the natural spreading of virus without any human intervention(s), and also lag effects of the weather change and social interventions on the observed trajectory due to the COVID-19 incubation period; Second, the optimized CNF-plus-polynomial model is used to predict the future trajectory of COVID-19.Results revealed that aerosol and visibility show the higher contribution to transmission, wind speed to death, and humidity followed by barometric pressure dominate the recovery rates, respectively. Consequently, the average effect of environmental change to COVID-19 trajectory in China is minor in all variables, i.e., about -0.3%, +0.3% and +0.1%, respectively. In this research, the response analysis of COVID-19 trajectory to the compound natural interactions presents a new prospect on the part of global pandemic trajectory to environmental changes.

The study was aimed to identify the relations between the severity of coronary artery disease and associated percutaneous coronary interventions with the changes in the local Earth magnetic field activity (LEMF). One-thousand-two-hundred-forty patients diagnosed with Acute coronary syndrome who underwent percutaneous coronary intervention within 2015-2016 were retrospectively included in this single centre study. The majority of acute coronary syndromes that occurred in females was associated with an increase in LEMF intensity in 3.5-32 Hz frequency ranges and were also associated with a higher number of diseased coronary arteries. Increased intensity in the same range was associated with a lower number of stented coronary arteries in males in 2015. Positive correlation coefficients were found between increased LEMF intensity in the 0-15 Hz range and the number of revascularized coronary arteries in females during the winter season in 2016. Stronger LEMF in low-medium frequency ranges is associated with acute coronary syndromes in males caused by less diffuse coronary artery disease resulting in lower number of coronary arteries segments needed for revascularisation, especially during winter. Stronger LEMF in high frequency range is associated with increased occurrence of ischaemic cardiovascular events, while stronger LEMF in low to moderate frequency ranges is associated with positive effect.

The calculations of atmospheric radiative transfer are among the most time-consuming components of the numerical weather prediction (NWP) models. Therefore, using deep learning to achieve fast radiative transfer has become a popular research direction. We propose a physics-incorporated framework for the radiative transfer model training, in which the thermal relationship between fluxes and heating rates is encoded as a layer of the network so that the energy conservation can be satisfied. Based on this framework, we compared various types of neural networks and found that the model structures with global receptive fields are more suitable for the radiative transfer problem, among which the Bi-LSTM model has the best performance.

Searching for life on other planets and planetary bodies poses a number of challenges, especially given that there is currently no clear evidence that lifeforms can only conform to characteristics observed on Earth. While current astrobiology missions operate under the assumption that any astrobiological entities of interest will have similar properties to organisms on Earth (‘canonical’ lifeforms), the current convention of searching for direct evidence of such lifeforms (e.g. organic compounds, genetic material, etc.) is largely exclusionary to any biologically valid lifeforms which are not currently a part of the canonical model of life that is used to drive exploratory efforts. It is proposed that the definition of life be broadened to include any entities capable of maintaining homeostasis relative to an entropic environment. Thus, instead of the traditional strategy of searching for direct evidence of life conforming to Earth-based standards, i.e., looking for specific organic compounds, a new strategy could be used to indirectly identify lifeforms through their utilization of environmental resources (e.g. as energy sources).

The polar vortices play a central role in vertically coupling the Sun-Earth system by facilitating the descent of reactive odd nitrogen (NOx = NO + NO2) produced in the atmosphere by energetic particle precipitation (EPP-NOx). Downward transport of EPP-NOx from the mesosphere-lower thermosphere (MLT) to the stratosphere inside the winter polar vortex is particularly impactful in the wake of prolonged sudden stratospheric warming events. This work is motivated by the fact that state-of-the-art global climate models severely underestimate this EPP-NOx transport in the Arctic. As a step toward understanding the transport pathways by which MLT air enters the top of the polar vortex, we explore the extent to which Lagrangian Coherent Structures (LCS) impact the geographic distribution of NO near the polar winter mesopause in the Whole Atmosphere Community Climate Model eXtended version with Data Assimilation Research Testbed (WACCMX+DART). We present planetary wave-driven enhanced NO descent near the polar winter mesopause during 14 case studies from the Arctic winters of 2005/2006 through 2018/2019. During all cases the model is in reasonable agreement with SABER temperatures and SOFIE and ACE-FTS NO. Results show consistent LCS formation at the top of the polar vortex during minor and major SSWs. LCSs act to confine air with elevated NO to high latitudes as it descends into the top of the polar vortex. Descent of NO tends to be enhanced in traveling planetary wave troughs. These results present a new conceptual model of transport in the polar winter mesosphere whereby regional-scale, long-lived LCSs, coincident with the troughs of planetary waves, act to sequester elevated NOx at high latitudes until the air descends to lower altitudes.