Sudden Stratospheric Warmings (SSW) and Elevated Stratopause (ES) events are mid-to-high latitudinal, atmospheric wave-driven phenomena leading to significant changes in wind, temperatures, and vertical mass transport, especially in stratospheric and mesospheric altitudes. Presumably, SSW and ES-induced changes also cause modifications in the highly variable D-region ionization. This bottom side of the ionosphere behaves with the Earth’s surface as a reflection boundary for Very Low Frequency (VLF) radio signal transmission used for long-distance communication. Since perturbations of the D-region ionization are also notable in the VLF signal, it is a valuable tool for continuous investigations of the D-region. Here, we study the impact of four SSW/ES events on the VLF signal amplitude between the high latitude transmitter-receiver link Keflavik, Iceland, to NyÅlesund, Svalbard, to gain further knowledge about interactions between the D-region and the atmosphere during these atmospheric phenomena. For three of four SSW/ES events, a very similar VLF signal amplitude signature is observed, characterized by a significant increase during the SSW period in the signal amplitude followed by a decrease during the ES period. This study aims to reveal a possible mechanism driving these similar VLF signal amplitude variations, involving modified electron neutral collision frequencies and electron densities due to changed temperatures and minor constituent concentrations according to the SSW/ES events. However, the VLF signal amplitude for one event increased two weeks later than during the other three events and did not show a decrease during the ES period. Possible causes for the different VLF signal amplitude variations are discussed.

The important question of how the solar wind influences Jupiter's magnetosphere is difficult to answer due to the lack of consistent up-stream monitoring of the interplanetary medium (IPM) and the large-scale dynamics internal to the magnetosphere. To compensate for the relative lack of in-situ data, solar wind propagation models are often used to estimate the ambient IPM conditions near Jupiter for comparison to remote observations or in-situ measurements. A statistical analysis of the timescales over which Jupiter's magnetosphere reacts to changes in the IPM would allow the solar wind interaction to be better decoupled from internal dynamics; however, solar wind propagation from near-Earth measurements out to Jupiter introduces uncertainties in both the timing and magnitude of changes in the IPM which are themselves difficult to assess. Here, we present an ensemble modeling framework for the solar wind at Jupiter. A variety of existing solar wind models were compared to in-situ measurements from near-Jupiter spacecraft spanning diverse spacecraft-Sun-Earth alignments and phases of the solar cycle, amounting to more than 23,000 hours over four decades. Typical errors in prediction timing and magnitude, as well as conditions under which the different input models performed better than average, were then characterized as part of this framework. The resulting ensemble model produces the most-probable near-Jupiter IPM conditions for times within the tested epoch and provides the estimated variance in these conditions, allowing for a statistical analysis of the relationship between Jupiter’s magnetosphere and the solar wind. In addition to remote sensing studies, the robust modeling of solar wind conditions near Jupiter is crucial to ongoing and future in-situ studies using Galileo, Juno, JUICE, and Europa Clipper measurements; the compression or expansion of the magnetosphere is crucial to interpreting in-situ measurements of Jupiter’s middle and outer magnetosphere. Finally, we will discuss how the work presented here can be extended towards more robust characterization of solar wind parameters and time-dependent propagation of solar wind conditions at other planetary magnetospheres.

Wildfires have become a major ecological issue impacting humans and the environment alike. To prevent wildfires from starting in the first place, better spatial prediction tools are needed to assess wildfire risk between locations to allow better specialization of firefighting agencies in performing preventative measures. Current methods of predicting spatial wildfire risk rely on satellite imagery, which is significantly limited in terms of spatial and temporal resolution. The use of unmanned aerial vehicles (UAVs) for assessing wildfire risk has significant advantages over satellites due to their much higher spatial and temporal resolution. This project sought to develop a novel deep learning model to predict wildfire risk through aerial imaging specialized for deployment on UAVs. Convolutional neural network architectures were investigated due to their ability to extract implicit spatial features from data. Google Earth Engine was used to assemble a geospatial dataset containing patches of vegetation burnability indices each mapped to a wildfire risk index based on historical fire prevalence. The results for the model show extremely high performance across all metrics evaluated. From this proof of concept research, UAV-based prediction offers a promising prospective solution to the drawbacks of satellite imaging in remote sensing of wildfire risk.

We estimate the global impact of storms on the global structure and dynamics of the nightside plasma sheet (PS) from observations by the NASA mission THEMIS. We focus on an intense storm occurring in December 2015 triggered by interplanetary coronal mass ejections (ICMEs). It starts with a storm sudden commencement (SSC) phase (SYM-H~+50nT) followed by a growth phase (SYM-H~-188nT at the minimum) and then a long recovery phase. We investigate THEMIS observations when the spacecraft were located in the midnight sector of the PS at distances typically between 8 and 13RE. It is found that the PS has been globally compressed up to a value of about~>4nPa during the SSC and main phases, i.e. 8 times larger than its value during the quiet phase before the event. This compression occurs during periods of high dynamic pressure in the ICME (20nPa) about one order of magnitude larger than its value in the pristine solar wind. We infer a global increase of the lobe magnetic field from 30nT to 100nT, confirmed by THEMIS data just outside the PS. During the SSC and main phases, the PS is found thinner by a factor of 2 relative to its thickness at quiet times, while the Tsyganenko T96 magnetic field model shows very stretched magnetic field lines from inner magnetospheric regions toward the nightside. During the recovery phase, whereas the interplanetary pressure has dropped off, the PS tends to gradually recover its quiet phase characteristics (pressure, thickness, magnetic configuration, etc) during a long recovery phase.

A document by Zeeshan Shariff. Click on the document to view its contents.

Long-term analysis of climate trends and patterns relies on continuous and high-frequency observation data sets. Still, due to limitations in historical meteorological observation techniques and national policies, most weather stations worldwide can only provide three, four, or eight observations per day, hindering climate change research progress. To solve the problem of low-frequency daily observation in part of global meteorological stations, we propose a time-downscaling model of observation series based on deep learning, Land Surface Observation Simulator-Time Series Version (LOS-T), taking 2m air temperature as an example. LOS-T, combined with multimodal technology and Transformer architecture, effectively merges multiple types of data, including low-frequency observations, ERA5-land, and geographic information, to convert low-frequency observations into hourly high-frequency observations. The model showed significant accuracy improvements by training on millions of meteorological observations worldwide, especially on downscaling the data, which only has three observations per day. The results showed that LOS-T substantially improved over baseline models such as Bilinear and vanilla Transformer on several metrics such as MAE, RMSE, COR, and R2. In addition, case studies have confirmed that LOS-T can effectively utilize ERA5-land’s high-frequency temperature change information to improve the accuracy and robustness of predictions, even when there is a significant deviation between ERA5-land data and Ground Truth. In short, LOS-T provides new ways to refine global meteorological observation data and helps advance climate science.

Kinematic conditions at the water table are commonly used in analytical models to interpret aquifer pumping test data. However, the existing kinematic conditions often lead to an underestimation of specific yield during the drainage process due to the assumption of instantaneous gravity drainage, which does not align with estimates from soil column experiments. This study derives a new water table condition on the basis of a full unsaturated-saturated flow model. This new condition not only integrates the hydraulic properties of the unsaturated zone that explicitly provides a comprehensive insight into the physical implications, but intriguingly retains mathematical tractability. This new condition is applied to a groundwater flow model for a pumping problem, and the drawdown solution is derived via integral transformations. The proposed solution is unitized to analyze the field data from a series of pumping test. The results demonstrate that the proposed model provides a better fit to measured data, particularly in scenarios where an unsaturated flow exerts a significant effect. Overall, the newly derived water table condition can be a handy tool to characterize the aquifer properties in an unconfined aquifer.

Woody plant encroachment is a global phenomenon, observed in many of the world's drylands. In those with shallow soils overlying karst geology, rock moisture can be an important source of water for the encroaching woody plants. This source can be particularly important for trees to maintain basic physiological functions during extended droughts, which are becoming more frequent and intense owing to climate change. However, our understanding of rock moisture dynamics in karst drylands undergoing woody plant encroachment is still limited because of the scarcity of direct measurements. In this study, we evaluated soil and rock moisture dynamics at a semiarid site in the Edwards Plateau region of Texas. Our measurements over the course of three years showed that in shallow upslope terrain, the dynamic water storage in bedrock was roughly twice that of soil, while in downslope terrain, the dynamic storage was largely restricted to the soil layer. Most of the bedrock storage gains occurred during the first year, after two major storm events of approximately 95 mm, and that storage was gradually depleted during the following two years, when precipitation was below average. Importantly, in upslope terrain we found substantially larger water storage under woody plants, which suggests that they not only can access and utilize rock moisture but also play a role in enhancing bedrock water storage capacity. These interconnected abilities can help woody plants survive extended droughts---a factor crucial for understanding their persistence and proliferation in the shallow soils of the Edwards Plateau and similar karst regions.

We construct a linear model of microseism power as a function of sea-ice concentration and ocean-wave activity based on seismic data for a station located on northern Ellesmere Island. We find the increase in microseism power over the last 32 years reflects the long-term loss of sea ice and increasing ocean-wave activity in the Arctic Ocean likely associated with climate change. We assess the model performance and its coefficients to determine a representative region over which sea-ice concentration and ocean-wave activity most directly influence the microseism power at the single station. We conclude that seismic data can potentially help refine in-situ observation and improve the spatiotemporal monitoring of multiyear sea ice as it changes due to climate forcing.

A document by Peter Chu. Click on the document to view its contents.

Understanding the interior structures of icy moons will be crucial in untangling narratives of formation and evolution, both within our solar system and beyond it. Seismology is a proven and unparalleled methodology for investigating the deep interiors of planetary bodies but has never been deployed on an icy moon. To improve future mission design, we conduct seismic simulations for Saturn’s icy moon Enceladus which account for the unique seismic responses of icy ocean worlds. We discover that even with high surface temperatures at the south pole and 3D ice thickness models, seismic amplitudes are two orders of magnitude higher than the self-noise of mission-candidate instrumentation. We compare the effects of a 2D and 3D ice shell to determine the detail of seismic inversion for ice shell properties and how this varies with source and receiver location. We also compare the travel time differences caused by ice shell variation with potential effects from the uncertain core structure and discover that these two sources of travel time perturbation have similar magnitudes but could be distinguished through careful inversion strategy. We investigate varied source types to represent focal mechanisms likely to be present at the south pole of Enceladus. We finally make recommendations supporting landing sites between 20 - 30° from the south pole that should enable observation of a wide range of seismic phases, including core-transmitted phases that could constrain core velocities.

While an early wet Mars is well established along many lines of scientific evidence, it remains vigorously debated in what forms water existed in the early Mars and how Mars transitioned from a wet planet into a dry planet. Here, we construct a detailed seismic stratigraphy from the source region of a marsquake in Cerberus Fossae to the InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) landing site to decipher the evolution of Mars. Seismic velocity structures are constrained with combined seismic constraints of waveforms recorded for the marsquake and receiver functions extracted from the seismic data of three marsquakes, while the crustal composition and pore property are inferred based on rock physics modeling of the inferred seismic structure and constraints of porosity-depth profiles beneath Mars. Seismic stratigraphy reveals a liquid-water saturated smectite-chlorite-enriched sedimentary layer at 2.65-3.85 km beneath Martian surface and a transition from the dry surface basaltic layer to liquid-water saturated overlain crustal layers, suggesting that early wet Mars experienced a paleo-ocean and a resurface event in the northern plain before transitioning into a dry planet and covered by the emplacement of the Elysium volcanic unit in Hesperian and Amazonian. Our results indicate that the major early records of planetary water history are buried deep in the Martian crust, and so is the possible evidence of early life on Mars. Magmatic perturbations of the deep water reservoir would also provide an explanation for possible subsequent oceans proposed at different stages of the Martian history.

Increasing numbers of confirmed continental sub-Moho earthquakes beneath the Himalaya and Tibetan plateau have been taken to imply a brittle, seismogenic, uppermost mantle. However, differences between 'Moho' (a wavespeed/density boundary) and 'crust-mantle boundary' (a petrological distinction) complicate interpretation of these earthquakes. Applying the S-minus-P method to the Hi-CLIMB-2D array in south Tibet, we identify nine new earthquakes below the Moho as previously interpreted from spatially-smoothed receiver-function images at ~60 km, but find most lie above an ~80-km converter on our new CCP images. An ~100-km converter is below all these nine earthquakes and possibly beneath all sub-Moho earthquakes here. If the previously interpreted Moho represents the crust-mantle boundary, these sub-Moho earthquakes imply a brittle upper mantle. We suggest the 80-km converter is more appropriately interpreted as Moho, and as marking the (petrological) crust-mantle boundary, so that Himalayan sub-Moho earthquakes may be restricted to eclogitized Indian lower crust. Reversing recent understanding, beneath the Himalaya the deep crust may be stronger (more seismogenic) than ultramafic upper mantle.

The wave telescope technique is used to reconstruct spatial power distributions of space plasmas from multipoint spacecraft missions. This study aims to quantify the impact of uncalibrated uncertainties in the time synchronization and the spatial position on the accuracy of the wave telescope method for observatories with more than four spacecraft, e.g. HelioSwarm a nine-spacecraft NASA observatory currently in Phase B. We simulate synthetic data with systemic timing and spatial errors modeled using geometries drawn from HelioSwarm’s Design Reference Mission, applying the wave telescope technique to estimate wavevectors for two characteristic ion-scale waves. By carefully selecting optimal polyhedral configurations from the overall geometry, and combining signals from multiple polyhedra, the impact of systematic uncertainties and spatial aliasing can be significantly reduced, leading to more accurate wavevector identification for future multipoint missions.

Between 1985 and 2014 observations show an expansion of Southern Ocean sea ice. This phenomena is not simulated in CMIP6 Atmosphere-Ocean General Circulation Model (AOGCM) simulations, nor in a large ensemble of HadGEM3-GC3.1-LL simulations. Here we quantify the impact of this discrepancy on radiative feedback and global temperature trends. We find that both satellite reconstructions of the Earth’s energy budget and atmosphere-only GCM simulations forced with observed SST and sea-ice trends support the idea that the expansion of sea-ice led to a negative surface albedo feedback over the Southern Ocean. In contrast, the decline of sea-ice in AOGCMs gives rise to a positive feedback. We estimate that, had the AOGCMs warmed with the observed Southern Ocean sea-ice evolution, their global feedback parameter would be less positive, by 0.15 to 0.36 Wm−2K−1, and their global temperature trends reduced by 0.072 to 0.02 K per decade over this period.

Geocoronal Solar Wind Charge Exchange (SWCX) is the process by which heavy ions from the solar wind undergo charge exchange with neutral hydrogen atoms from the Earth’s exosphere, releasing photons at discrete energies characteristic of the solar wind ions. This paper investigates the solar wind types driving geocoronal SWCX. We find that during periods of time-variable SWCX, higher fractions of every ion species are recorded by ACE compared to the averages. Notably, a subset of the slow solar wind characterised by a systematic lower temperature and higher proton flux is surprisingly effective for producing SWCX. Given the degradation of the solar wind composition spectrometer on ACE in 2011, we explore the capabilities of XMM-Newton as an alternative sensor to monitor heavy ion composition in the solar wind. Unlike the distributions of other ion line fluxes analysed, only OVIII, extracted via spectral analysis of XMM-Newton observations, display patterns similar to the corresponding parent ion abundances from ACE (O8+/p). Finally, we employ a Random Forest Classifier model to predict solar wind types based on literature results. When combining proton data with XMM-Newton features, the model performance improves significantly, achieving a macro-averaged F1-score of 0.80 (with a standard deviation of 0.06).

Capsule summary. Extreme temperatures in the UK (July 2022) and India/Pakistan (Spring 2022) are confidently attributed to climate change using an automated system. Similarly attributable extremes occurred frequently worldwide in 2022.

During the summer of 2022, a record-breaking heatwave-drought (DH2022) hit southern China, especially in the middle and lower reaches of the Yangtze River basin (MLYR). However, its impact on regional carbon budget remains unclear. Here, we assessed the response of regional terrestrial carbon fluxes to DH2022 using Global Carbon Assimilation System (GCASv2) by assimilating OCO-2 XCO2 retrievals. Our results show that compared to 2015-2021, MLYR experienced a clear reduction in carbon uptake by 45.8 TgC (44.1%) during July-September in 2022. However, the total annual carbon sink in southern China increased by 42.8 TgC. This is due to a warm and wet spring, leading to an increase in carbon sink by 50.8 TgC and greatly compensating for the carbon losses. Our study provides a comprehensive assessment of land carbon dynamics in southern China under the influence of DH2022, enhancing understanding of the impacts of climate extremes on the regional carbon cycle.