Human activities constantly produce air pollutants, which may greatly impact climate change. Elucidating the relationship between air quality and temperature change is essential to gain a better understanding of climate change. Up until now, machine learning algorithms have been deployed to big data analysis in various fields. Here, we use the machine learning algorithms to analyze temperature and air quality data of different cities across China. Multiple linear regression and tree-based methods, including bagging, boosting and random forest, are used to train the model. With the tree-based methods, the factors highly associated with temperature change will be elucidated, which indicate their significant impact on temperature change. The results in this study demonstrate the possibility of using machine learning in atmospheric science field to predict air quality and temperature change, and how different algorithms perform regarding temperature and air quality predictions, which is informative for future air quality prediction research. The relationship between air quality and temperature change can also provide guidance to policymakers.

The short-term disturbance of galactic cosmic rays(GCRs) caused by coronal cass ejections (CMEs) can be quantified to indicators of geomagnetic storms. We proposed a model takes GCRs time series slice as input and generates two geomagnetic storm prediction indexes as output. This new model is based on Hilbert-Huang Transform (HHT), utilizing Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) instead of Empirical Mode Decomposition (EMD), and extends 2D spectrum result of HHT to 3D form.Two prediction indexes is constructed from the sensitive components in GCRs. Finally, this model is tested on 11 great geomagnetic storms(Kp>=8) during solar cycle 23 and 24. Accuracy of this model turned out to be 82%, and prediction lead time ranges from 9 to 24 hours.

The COVID-19 pandemic resulted in reduced carbon dioxide (CO2) emissions in 2020 in large parts of the world. We have analysed an ensemble of satellite retrievals of column-averaged dry-air mole fractions of CO2, i.e. XCO2, to find out if the COVID-19 related regional-scale reduction of anthropogenic CO2 emissions can be detected from space. We focus on East China and analysed a set of latest version XCO2 data products from the satellites Orbiting Carbon Observatory-2 (OCO-2) and Greenhouse gases Observing SATellite (GOSAT). We use a data-driven approach, which is based on the computation of XCO2 anomalies using a method called DAM. Via DAM, trends and seasonal variations are largely filtered out and resulting positive values of the XCO2 anomalies correlate with the location of major CO2 source regions such as East China after spatio-temporal averaging. We analysed satellite data between January 2015 to May 2020 and compared monthly XCO2 anomalies in the time period January to May 2020 with corresponding monthly XCO2 anomalies from previous years. In order to link the satellite-derived XCO2 anomalies to East China fossil fuel (FF) emissions, we used target region XCO2 and corresponding FF emissions from a model.

Declining Arctic sea ice thickness and extent over the past several decades has resulted in extensive regions of thin ice vulnerable to melting. We use the ERA-5 reanalysis along with a fully coupled atmosphere-ocean-ice model (Navy ESPC) to explore the role of Arctic cyclones in summertime sea-ice change. The ERA-5 reanalysis is used to statistically analyze how surface energy fluxes and wind forcing from Arctic cyclones in the marginal ice zone between May and August (1999-2018) influence sea-ice extent on 1-10 day timescales. In May and June, cyclones decelerate the local seasonal loss of sea-ice extent due to a reduction in the incoming solar radiation. In late summer, cyclones no longer decelerate the seasonal loss of sea-ice extent, despite still reducing the net surface energy flux. Surface wind forcing across the ice edge explains only a small fraction of the short-term changes in local sea-ice extent, which suggests other processes not accounted for in this analysis, such as bottom melt, become important later in the melt season. In order to gain a more detailed understanding of how cyclones affect sea ice we then utilize the coupled Navy ESPC to examine the intense “Great Arctic Cyclone” of August 2012. Two mechanisms of cyclone-induced melting are identified: turbulent mixing of a warm layer located at 15- to 35-m depth increases bottom melting and warm air advection by the strong surface winds increases surface melting. The sea ice melt rate is substantially enhanced by the cyclone, however this effect is confined to a relatively small region and only lasts for a few days. Lastly, we note that despite the marked trend in the reduction of sea ice extent over the past several decades, reanalysis indicates little trend in the Arctic storminess as diagnosed by kinetic energy as a proxy for Arctic cyclones.

Parameters in climate models are usually calibrated manually, exploiting only small subsets of the available data. This precludes an optimal calibration and quantification of uncertainties. Traditional Bayesian calibration methods that allow uncertainty quantification are too expensive for climate models; they are also not robust in the presence of internal climate variability. For example, Markov chain Monte Carlo (MCMC) methods typically require $O(10^5)$ model runs, rendering them infeasible for climate models. Here we demonstrate an approach to model calibration and uncertainty quantification that requires only $O(10^2)$ model runs and can accommodate internal climate variability. The approach consists of three stages: (i) a calibration stage uses variants of ensemble Kalman inversion to calibrate a model by minimizing mismatches between model and data statistics; (ii) an emulation stage emulates the parameter-to-data map with Gaussian processes (GP), using the model runs in the calibration stage for training; (iii) a sampling stage approximates the Bayesian posterior distributions by using the GP emulator and then samples using MCMC. We demonstrate the feasibility and computational efficiency of this calibrate-emulate-sample (CES) approach in a perfect-model setting. Using an idealized general circulation model, we estimate parameters in a simple convection scheme from data surrogates generated with the model. The CES approach generates probability distributions of the parameters that are good approximations of the Bayesian posteriors, at a fraction of the computational cost usually required to obtain them. Sampling from this approximate posterior allows the generation of climate predictions with quantified parametric uncertainties.

The development of atmospheric parameterizations based on neural networks is often hampered by numerical instability issues. Previous attempts to replicate these issues in a toy model have proven ineffective. We introduce a new toy model for atmospheric dynamics, which consists in an extension of the Lorenz'63 model to a higher dimension. While neural networks trained on a single orbit can easily reproduce the dynamics of the Lorenz'63 model, they fail to reproduce the dynamics of the new toy model, leading to unstable trajectories. Instabilities become more frequent as the dimension of the new model increases, but are found to occur even in very low dimension. Training the neural network on a different learning sample, based on Latin Hypercube Sampling, solves the instability issue. Our results suggest that the design of the learning sample can significantly influence the stability of dynamical systems driven by neural networks.

Sudden Stratospheric Warmings (SSWs) during boreal winter are one of the main drivers of sub-seasonal climate variability in the Northern Hemisphere. Although the impact of SSW events on surface climate and climate extremes has been clearly demonstrated , the impact of the resulting climate anomalies on society has not been so widely considered. In the United Kingdom (UK), SSWs are associated with cold weather, which is linked to significant increases in mortality. This study demonstrates, for the first time, that SSWs are linked to increases in mortality in the UK. A distributed lag non-linear model and standard parameter settings from the literature is used to construct a daily time series of UK deaths attributable to cold weather between 1991 and 2018. Weekly mortality associated with SSWs is diagnosed using a superposed epoch analysis of attributed mortality for the 15 SSW events in this period. SSW associated mortality peaks between 3 and 5 weeks after SSW central date and leads to, on average, 620 additional deaths in the same period. Given that the impacts of SSWs can be skilfully predicted on sub-seasonal timescales, this suggests that health and social care systems could derive substantial benefit from sub-seasonal forecasts during SSWs.

High-speed data transfer and high-performance imaging can be realized by using radio-waves in high-frequency bands, such as millimeter-waves and THz-waves, where wide frequency bands are available. However, the cell size would be smaller than a few hundred meters, due to large free space propagation loss and large atmospheric attenuation. Thus, many base stations, which are connected by networks, are required to offer nation-wide or global network services by such high-frequency radio-bands. The networks would be constructed by various transmission media including optical fibers and fixed wireless links, where many media converters are required. This paper reviews various technologies for seamless bridges between radio and optical links. For the time being, congestion of radio spectrum in THz bands is not significant. However, if we look at the history of radio-wave technologies, spectral congestion has been high even in newly developed high frequency bands. Even in active radio services in millimeter-wave or THz-wave bands, interference mitigation with passive services such as radio astronomy and Earth observation satellites is an important issue, as of now. This paper describes research trends of THz-wave technologies from the point of view of a figure of merit defined by a product of the carrier frequency and spectral efficiency, to discuss the significance of spectral efficiency enhancement in the high-frequency region. Analysis of power consumption of short distance radio systems is also shown to discuss expected performance of THz-wave links.

Subtropical anticyclones and midlatitude storm tracks are key components of the large-scale atmospheric circulation. Focusing on the southern hemisphere, the seasonality of the three dominant subtropical anticyclones, situated over the South Pacific, South Atlantic and South Indian Ocean basins, has a large influence on local weather and climate within South America, Southern Africa and Australasia, respectively. Generally speaking, sea level pressure within the southern hemisphere subtropics reaches its seasonal maximum during the winter season when the southern hemisphere Hadley Cell is at its strongest. One exception to this is the seasonal evolution of the South Pacific subtropical anticyclone. While winter maxima are seen in the South Atlantic and South Indian subtropical anticyclones, the South Pacific subtropical anticyclone reaches its seasonal maximum during local spring with elevated values extending into summer. In this study we investigate the hypothesis that strength of the austral summer South Pacific subtropical anticyclone is largely due to heating over the South Pacific Convergence Zone. Using reanalysis data, and AGCM added cooling and heating experiments to artificially change the strength of diabatic heating over the South Pacific Convergence Zone, our results show that increased heating triggers a Rossby wave train over the Southern Hemisphere mid-latitudes by increasing upper-level divergence. The propagating Rossby wave train creates a high-low sea level pressure pattern that projects onto the center of the South Pacific Subtropical Anticyclone to intensify its area and strength. The southern hemisphere storm tracks also shift poleward due to increased heating over the South Pacific Convergence Zone.

The projected response of the atmospheric circulation to changes driven by increasing greenhouse gas concentrations is highly uncertain. One of the primary reasons for this is that the state-of-the-art models we employ to investigate these responses struggle to represent basic features of the midlatitude circulation such as storm tracks, jets and blocking. Biases also have detrimental effects on predictive skill for dynamically driven fields at climate prediction time scales of seasons to decades. Despite this, physical understanding of the controls on these features and the drivers of their biases is still limited. Here we investigate a hierarchy of large ensemble climate reanalysis and hindcast simulations performed by the Norwegian Earth System Model (NorESM). Each ensemble is 30 members and was run from 1985-2010. For the reanalysis runs various data-assimilation strategies were employed. These are: SST only, SST plus hydrographic profiles, SST plus hydrographic profiles plus sea-ice concentration. The assimilation was performed monthly after which the model runs freely. These are compared to both free runs and AMIP-style simulations with ERA-Interim serving as ground truth. We evaluate the North Pacific and North Atlantic jets in winter and summer. We also identify where the observations lie within the predictive distribution of the ensemble. Results show that the North Atlantic jet is too zonal, extends too far into Europe and is shifted northwards. Virtually the entire North Atlantic sector lies outside the predictive distribution of the ensemble and performance actually degrades in the simulations with tighter constraints on the assimilation. By contrast the North Pacific jet is rather better represented in all aspects both with respect to pattern as well as magnitude of the biases. This is likely due to the better-represented teleconnections between the tropical and extratropical Pacific. Comparison of these ensembles with AMIP simulations suggests that the errors in the midlatitude circulation reside in the atmospheric component of the model. We also present results from hindcast simulations where NorCPM was initialized at different times of the year and then run forward 12 months. Implications and causes of the varying behavior among the ensembles are discussed as well as prospects for the future.

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.

This study aims to quantify the impacts of ocean coupling on simulated and projected tropical cyclone (TC) precipitationin the Northern Hemisphere. We used global climate model (GCM) simulations over 1950-2050 from the High Resolution Model Intercomparison Project (HighResMIP) and compared its fully coupled atmosphere-ocean GCMs (AOGCMs) with atmosphere-only GCMs (AGCMs). We find that ocean coupling generally leads to decreased TC precipitation over ocean and land. Large-scale sea surface temperature (SST) biases are critical drivers of the precipitation difference, with secondary contributions from local TC-ocean feedbacks via SST cold wakes. The two driving factors, attributed to ocean coupling in the AOGCMs, influence TC precipitation in association with decreased TC intensity and specific humidity. The AOGCMs and AGCMs consistently project TC precipitation increases in 2015-2050 relative to 1950-2014 over ocean for all basins, and for landfalling TCs in the North Atlantic and western North Pacific.

During the Norwegian young sea ICE (N-ICE2015) campaign, which took place in the first half of 2015 north of Svalbard, a deep winter snow pack (50 cm) on sea ice was observed, that was 50% thicker than earlier climatological studies suggested for this region. Moreover, a significant fraction of snow contributed to the total ice mass in second-year ice (SYI) (9% snow by mass), while very little snow was present in first-year ice (FYI) (3% snow by mass). We use a 1-D snow/ice thermodynamic model forced with reanalyses data in autumn and winter 2014/15. We show that snow-ice would form on SYI even with an initial ice thickness of 2 m in autumn. By the end of winter snow-ice can contribute up to 24-44% of the total thickness of SYI, if the ice is thin in autumn (0.6 m). This is important, especially in the absence of any bottom thermodynamic growth due to the thick insulating snow cover. We also show that growth of FYI north of Svalbard is controlled by the timing of growth onset relative to snow precipitation events and cold spells. These usually short-lived conditions are largely determined by the frequency of storms entering the Arctic from the Atlantic Ocean. In our case, a later freeze onset was favorable for FYI growth, due to less snow accumulation in early autumn. This limits snow accumulation on FYI but promotes bottom thermodynamic growth. We show our findings are related to regionally higher precipitation in the Atlantic sector of the Arctic, where frequent storms bring lot of precipitation in autumn and winter, and also affect the duration of cold temperatures required for ice growth in winter. We discuss the implications and the importance of snow-ice in the future Arctic, formerly believed to be non-existent in the central Arctic, due to thick perennial ice and little snow precipitation. The combination of sea ice thinning and high precipitation in the “Transpolar Drift region” highlights the need to understand the regionality of these processes across the Arctic.

In case of nuclear accidents like Fukushima disaster, the influence of 137Cs depositional forms (soluble and/or solid forms) on mineral soil of forest environment on its availability have not reported yet. Soluble (137Cs tagged ultra-pure water) and solid (137Cs contaminated litter-OL and fragmented litter-OF) input forms were mixed with the mineral soils collected under Fukushima coniferous and broadleaf forests. The mixtures then incubated under controlled laboratory condition to evaluate the extractability of 137Cs in soil over time in the presence of decomposition process through two extracting reagents- water and ammonium acetate. Results show that extracted 137Cs fraction with water was less than 1% for soluble input form and below detection limit for solid input form. On the same way with acetate reagent, the extracted 137Cs fraction ranged from 46 to 56% for soluble input and 2 to 15% for solid input, implying the nature of 137Cs contamination strongly influences the extractability and hence the mobility of 137Cs in soil. Although the degradation rate of the organic materials has been calculated in the range of 0.18 ± 0.1 to 0.24 ± 0.1 y-1, its impact on 137Cs extractability appeared very weak at least within the observation period, probably due to shorter observation period. Concerning the treatments of solid 137Cs input forms through acetate extraction, relatively more 137Cs has been extracted from broadleaf organic materials mixes (BL-OL & BL-OF) than the coniferous counterparts. This probably is due to the fact that the lignified coniferous organic materials (CED-OL & CED-OF) components tend to retain more 137Cs than that of the broadleaf. Generally, by extrapolating these observations in to a field context, one can expect more available 137Cs fraction in forest soil from wet depositional pathways such as throughfall and stemflow than those attached with organic materials like litter (OL) and its eco-processed forms (OF).

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.

Why would hundreds of scientists from around the world freeze a ship in Arctic sea ice for an entire year, braving subzero temperatures and months of polar darkness? This may sound like a fictional adventure movie plot, but from September 2019 through October 2020, the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) Arctic research expedition did just this. Currently, the Arctic is warming twice as fast as the global average (a phenomenon known as Arctic amplification) and due to a lack of observations, there is considerable uncertainty in climate models projecting the Arctic climate of the future. The MOSAiC expedition aims to better understand the changing Arctic climate system by gathering data from ground zero over a full seasonal cycle to augment satellite observation data. Using the expedition as an engagement hook, scientists and curriculum developers developed a high school earth science curriculum anchored by the phenomenon that climate scientists are actively trying to explain: Arctic amplification. The curriculum follows the model-based inquiry instructional framework where each lesson provides students with learning experiences (e.g., virtual reality tours of MOSAiC field sites, analyzing authentic Arctic satellite datasets) that relate back to the phenomena. Focusing on explaining natural phenomena provides an authentic context for students to learn and apply scientific understanding, which research shows can help engage students in NGSS scientific practices. Here we present an overview of the learning sequence using refinement of mental models throughout the unit and present preliminary results from pre-post assessments from two educator workshops (~100 teachers) that show that participants’ understanding of Earth’s climate system improved significantly after engaging with the curriculum. Based on these results, we expect this curriculum to be an important tool in engaging students in Earth’s systems thinking.

We use measurements from NASA’s Van Allen Probes to calculate the decay time constants for electrons over a wide range of energies (30 keV - 4 MeV) and values ( = 1.3 - 6.0) in the Earth’s radiation belts. Using an automated routine to identify flux decay events, we construct a large database of lifetimes for near-equatorially-mirroring electrons over a 5-year interval. We find long lifetimes (~100 days) in the inner zone that are largely independent of energy, contrasted with shorter, energy-dependent lifetimes (~1-20 days) in the slot region and outer zone. We compare our lifetime calculations with prior empirical estimates and find good quantitative agreement. The comparisons suggest that some prior estimates may overestimate electron lifetimes between ≈ 2.5-4.5 due to instrumental effects and/or background contamination. Previously reported two-stage decays are explicitly demonstrated to be a consequence of using integral fluxes.

Accurate photoionization rates are vital for the study and understanding of planetary ionospheres. Previous model calculations of terrestrial photoionization rates lack sufficient spectral resolution to account for highly structured photoionization cross sections as well as the solar spectral irradiance. We present new photoionization rate calculations from CPI’s Atmospheric Ultraviolet Radiance Integrated Code [AURIC; Strickland et al., 1999] using high-resolution (0.01 Å) solar spectra and high-resolution (0.01 Å) atomic oxygen (O) and molecular nitrogen (N2) photoionization cross sections. Theoretical photoionization cross sections of O are determined utilizing the R-matrix plus pseudo-states (RMPS) approximation whereas N2 cross sections are determined using the R-matrix approximation. We include 34 high-resolution partial O state photoionization cross sections and 3 high-resolution partial N2 state photoionization cross sections with supplemental Conway [1988] tabulations for molecular oxygen and the remaining N2 states. We find that photoionization rates computed at 0.01 Å resolution differ substantially from rates computed using low-resolution cross sections, especially in the lower thermosphere below 200 km. Specifically, we find that ionization production rate ratios exhibit variations in altitude of more than ±40% between the high- and low-resolution cases. Past low-resolution calculations at various low spectral resolutions do not sufficiently account for or preserve the highly structured auto-ionization lines in the photoionization cross sections [Meier et al., 2007]. These features, in combination with high-resolution solar spectra, allow photons to penetrate deeper into the Earth’s atmosphere producing larger total ionization rates. These higher ionization rates may finally resolve data-model discrepancies in altitude profiles of electron densities due to the use of low-resolution photoionization cross sections in current E-region models.

This study examines the role of the mean state in the propagation of the Madden-Julian Oscillation (MJO) over the Maritime Continent (MC). We use an ensemble of simulations made with a single model - the Community Earth System Model version 2 (CESM2) – to assess the effect of the mean state that is unaffected by that of model components such as parameterization schemes. Results show that one ensemble member with an exceptionally stronger MJO propagation also exhibits a much steeper background meridional moisture gradient (MMG) over the southern MC region than the other ensemble members. The simulated mean state affects MJO via its impacts on moisture dynamics - a column water vapor budget reveals a greater advection of mean moisture by MJO wind in the southern MC is responsible for the anomalous MJO activity.

The air pollutant NO is derived largely from transportation sources, and is known to cause various respiratory diseases. Substantial reduction in transport and industrial processes around the globe stemming from the novel SARS-CoV-2 coronavirus and subsequent pandemic resulted in sharp declines in emissions, including for NO. Additionally, the COVID-19 disease that results from the coronavirus may present in its most severe form in those who have been exposed to high levels of air pollution and thus have various co-morbidities. To explore these links, we compared ground-based NOsensor data from 15 US cities from a one month window in 2019 versus the same window during shutdown in 2020. Levels of NO declined roughly 20-60% in 13 of the 15 cities in 2020, linked to similar declines in traffic volume in those cities. To broaden the spatial analysis beyond the individual ground-based monitors, satellite data for tropospheric NO was also analyzed, and was largely consistent with the ground measurements. Many of the cities studied had a substantial percentage of the population with various pre-existing conditions, and a relationship was found between NO levels, respiratory disease, and COVID-19 case counts. This finding indicates that substantial improvements in air pollution and health outcomes can be achieved quickly with local and state policy directives, perhaps leading to more population-level health resilience in the face of future pandemics.

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.

For decades the scientific community has been trying to reconcile abundant evidence for fluvial activity on Noachian and early Hesperian Mars with the faint young Sun and reasonable constraints on ancient atmospheric pressure and composition. Recently, the investigation of H2-CO2 collision induced absorption has opened up a new avenue to warm Noachian Mars. We use the ROCKE-3D global climate model to simulate plausible states of the ancient Martian climate with this absorptive warming and reasonable constraints on surface paleopressure. We find that 1.5-2 bar CO2-dominated atmospheres with 3% H2 can produce global mean surface temperatures above freezing, while also providing sufficient warming to avoid surface atmospheric CO2 condensation at 0°-45° obliquity. Simulations conducted with both modern topography and a paleotopography, before Tharsis formed, highlight the importance of Tharsis as a cold trap for water on the planet. Additionally, we find that low obliquity (modern and 0°) is more conducive to rainfall over valley network locations than high (45°) obliquity.

As the number of man-made Earth-orbiting objects increases, satellite operators need enhanced space traffic management capabilities to ensure safe space operations. For objects in Low-Earth orbit, orbit determination and prediction require accurate estimates of the local thermospheric density. In previous work, the estimation of thermospheric densities using two-line element data and a reduced-order model for the upper atmosphere was demonstrated. In this paper we demonstrate an approach for density estimation using radar and GPS tracking data. For this, we assimilate the tracking data in a dynamic reduced-order density model using a Kalman filter by simultaneously estimating the orbits and global density. We used the radar range and range-rate measurements of 20 objects and the GPS position measurements of 10 commercial satellites. The estimated density was validated against accurate SWARM density data and compared with NRLMSISE-00, JB2008, and TLE-estimated densities. We found that the estimated densities are significantly more accurate than NRLMSISE-00 and JB2008 densities. In particular, using the GPS data of 10 satellites, we obtain density estimates with a daily 1-σ error of only 5% compared to 14% and 22% for empirical models and 10% for TLE-estimated density. These accurate density estimates can be used to improve orbit determination and the use of real-time tracking data would enable real-time density estimation.

Abstract