A new technique for estimating the global-scale pattern of magnetospheric convection in the ionosphere is presented. The technique uses the SuperDARN line-of-sight velocity observations combined with an empirical convection model and the assumption that the resulting velocity field is divergence free. In contrast to other techniques for convection estimation, it does not express the velocity field in terms of known basis vectors and it does not assume that the velocity can be determined from a static potential. The velocity is estimated by applying Bayesian inverse theory to the input data, model, and constraints. Linear equations for the plasma velocity at every point in the domain are solved simultaneously in a least-squares sense. Application of the technique results in convection patterns with spatial resolution equal to the calculation grid. The resulting patterns conform with expectations based on the observed IMF conditions and display features that show close correspondence to simultaneously observed features in the auroral luminosity.

Reservoir Computing (RC), a simplified form of recurrent neural network , is composed with the Fourier Transform to produce data-driven prediction of multi-scale coupled quasi-geostrophic flows. Experiments are conducted using the Modular Arbitrary-Order Ocean-Atmosphere Model (MAOOAM) [10], a coupled quasi-geostrophic model that includes a 2-layer atmosphere (fast dynamics) and 1-layer ocean (slow dynamics). The Fourier Reservoir Computing (FRC) approach produces forecasts that extend the skillful forecast horizon beyond a comparable RC model trained on data in physical grid space. The FRC approach can be enhanced by applying localization in physical space, which extends the skillful forecast horizon further and facilitates practical application to high-dimensional geophysical problems. Plain Language Summary The science of predicting changes in weather and climate is typically enabled using a combination of idealized physical models and instrument measurements. Here, we offer a contribution as part of a growing community that is attempting to advance the use of machine learning for weather and climate prediction. We use a simplified coupled atmosphere-ocean model to generate synthetic ‘observa-tions’, and show that reliable forecast models can be generating using machine learning applied to these observation data alone. We also provide an approach for scaling this method for use in more realistic operational weather prediction scenarios.

Global studies of climate change impacts that use future climate model projections also require projections of land surface changes. Simulated land surface performance in Earth System models is often affected by climate biases of the atmospheric models, leading to errors in land surface projections. Here we run the JULES-ES land surface model with ISIMIP2b bias-corrected climate model data from 4 global climate models (GCMs). The bias correction reduces the impact of the climate biases present in individual models. We evaluate JULES-ES performance against present-day observations to demonstrate its usefulness for providing required information for impacts such as fire and river flow. We simulate a historical and two future scenarios; a mitigation scenario RCP2.6 and RCP6.0, which has very little mitigation. We include a standard JULES-ES configuration without fire as a contribution to ISIMIP2b and JULES-ES with fire as a potential future development. Simulations for gross primary productivity, evapotranspiration (ET) and albedo compare well against observations. Including fire improves the simulations, especially for ET and albedo and vegetation distribution, with some degradation in shrub cover and river flow. This configuration represents some of the most current earth system science for land surface modelling. The suite associated with this configuration provides a basis for past and future phases of ISIMIP, providing a simulation setup, postprocessing and initial evaluation using ILAMB. This suite ensures that it is as straightforward, reproducible and transparent as possible to follow the protocols and participate fully in ISIMIP using JULES.

Reservoir sedimentation management has become an important topic to large dams in the United States due to their historical design, current age, and increased environmental regulation. Less attention has been paid to small dams in remote mountains even though they are facing a more urgent sedimentation problem due to their relatively small storage. This study aimed to explore the relation between reservoir operations and sediment erosion in a small dam’s backwater zone to seek potential alternative solutions to flushing and excavation. Mindful timing and magnitude adjustment of water transfer, water diverted by tunnels, through a reservoir was hypothesized to strategically redistribute sediment erosion for sites with water transfer/diversion facilities in the main channel. It was found that sediment erosion was increased by over 100% by turning the water transfer to the maximum level which is 12 times higher than mean annual discharge. With stage drawdown, the increment of sediment erosion was further increased by over 50% compared with water transfer only scenarios. Upstream inflow with occurrence from 5% to 25% was found to be the optimal hydrologic condition for water transfer. These results indicated that water transfer is a promising strategy to redistribute deposited sediment downstream of it with appropriate stage drawdown.

We investigate sunward planetary ions in the Martian magnetotail that potentially reduce the amount of escaping ions. The global properties of sunward flows in the Martian magnetotail are characterized, based on over 13-years of ion data (May 2007–December 2020) collected by the ASPERA-3 instrument on Mars Express. We find that sunward flows mainly occur in the vicinity of the crustal fields, implying that crustal fields may play a key role in producing such flows. The occurrence rate and sunward flux are higher during solar maximum rather than solar minimum. However, we identify a relatively low occurrence rate of sunward flows and low sunward flux, suggesting that sunward flows have negligible influence on total ion escape at Mars. This is different from those at Venus, where sunward flows can significantly decrease the total escape rates of ions.

Wildfires expose populations to increased morbidity and mortality due to the increase of air pollutant emissions. This study assesses the impact of wildfire exposure in Portugal. In this work, we analyze the effects of the wildfire seasons (June-July-August-September-October) on monthly mortality by using data from atmospheric composition reanalysis, air quality stations, remote sensing, and mortality for exposure assessment, cluster analyses and regression models. Cluster analysis separated the months within fire seasons with extreme atmospheric conditions (months with more frequency of lower relative humidity and higher temperature, higher pollutant concentrations and higher wildfire activities), Cluster 1, from months with cleaner air and stable atmospheric conditions, Cluster 2. Linear regression showed statistically significant (p-value < 0.05) correlation (r) between Cluster 1 and cardiorespiratory mortalities due to Diseases of the Respiratory System (DRS), Pneumonia (PNEU), Chronic Obstructive Pulmonary Disease (COPD) and Diseases of the Circulatory System (DCS) (rDRS = 0.49; rPNEU = 0.42; rCOPD = 0.44; rDCS = 0.45). Cluster 2 presented no significant statistical correlation between atmospheric conditions and health outcomes. Results shows epidemiological evidence that heat stress combined with air pollution during wildfire season are contributing to increase disease burden. Besides that, we performed smoke forecasts over Portugal by using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model with satellite‐based fire emission. Forecasted PM10 and PM2.5 concentration reproduced the behavior of the observations (NRMSEPM10 = 3.70, rPM10 = 0.75; NRMSEPM2.5 = 1.51, rPM2.5 = 0.46) during October 15-16th, 2017, fire episode. BC results matches with satellite observations.

Because spatio-temporal variations in ocean heat content (OHC) are strongly predicted by ocean conductivity content (OCC) over most of the global ocean, we analyze the dynamical budget and behavior of the electrical conductivity of seawater. To perform these analyses, we use an ocean-model state estimate designed to accurately represent long-term variations in ocean properties in a dynamically and kinematically consistent way. We show that this model accurately reproduces the spatio-temporal variations in electrical conductivity seen in satellite-derived and in a seasonal climatology product derived from in-situ data, justifying use of the model data to perform further analyses. An empirical orthogonal function analysis suggests that the vast majority of the variance in OHC and OCC can be explained by similar mechanisms. The electrical conductivity budget’s most important term is the temperature forcing tendency term, suggesting that ocean heat uptake is the mechanism responsible for the strong relationship between OCC and OHC.

The Madden-Julian oscillation (MJO) significantly impacts North Atlantic hurricanes, with more hurricane activity occurring when the MJO favors enhanced convection over Africa and the tropical Indian Ocean and suppressed hurricane activity occurring when the MJO favors enhanced convection over the tropical Pacific. Using data from 1905-2015, we find more hurricanes make landfall in the continental US when the MJO enhances convection over the tropical Indian Ocean. In addition, when the MJO enhances convection over the Western Hemisphere, tropical cyclones tend to form in the Gulf of Mexico or the Caribbean, leading to more Gulf Coast landfalls. As the MJO moves to the Indian Ocean, more storms form in the tropical Atlantic, increasing the number of Florida and East Coast landfalls. The MJO’s modulation of tropical cyclone steering winds appears to be secondary to its effects on genesis locations.

Abstract: The presence of aliphatic hydrocarbons in sediments at the bottom of the platform and the continental margin of the northeastern tip of the Antarctic Peninsula and its natural escapes, leads us to infer about the likely effects they may have on the Antarctic environment, particularly on changes off average in surface temperatures and seawater. Methane leaks recorded in shallow waters of the region are compatible with the destabilization of gas hydrates in the marine substrate and with changes in average surface temperatures and seawater, suggesting a probable environmental impact such as As a consequence of this process, based on the hypothesis of the “clathrate rifle”, the scientific theory of Gerry Dickens and James Kennet, which argues that the rise in sea temperature can lead to a sudden release of methane from the clathrate deposits located in the ocean bottoms, as happened in the Eocene.

Small stress changes such as those from tidal loading can be enough to trigger earthquakes. If small and large earthquakes initiate similarly, high resolution catalogs with low detection thresholds are best suited to illuminate such processes. Below the Sea of Marmara section of the North Anatolian Fault, a segment of 150 km is late in its seismic cycle. We generated high-resolution seismicity catalogs for a hydrothermal region in the eastern Sea of Marmara employing both AI-based and template matching techniques to investigate a complex long-lasting sequence including seismicity up to MW 4.5. We document a strong effect of the Sea of Marmara level changes on the local seismicity. Both high resolution catalogs show that local seismicity rates are significantly larger during time periods shortly after local minima on sea level. Local strainmeters indicate that the associated strain changes, on the order of 30-300 nstrain, are sufficient to promote seismicity.

We developed a machine learning based meta-model to identify sustainability pathways through rapid scenario generation and defined the safe operating space for achieving them via scenario discovery. We trained a meta-model to replicate the Land-Use Trade-Offs integrated model of the Australian land system. Latin hypercube sampling was used to create many scenarios exploring the impact of uncertainties in key drivers including future socio-economic development, climate change mitigation, and agricultural productivity at a granular level. Economic and environmental impacts were evaluated against nationally downscaled SDG targets. Scenario discovery revealed new pathways to achieving five SDG targets for 2050 which required crop yield increases above 1.78 times, a carbon price above 100 AU$ tCO2-1, a > 9% biodiversity levy on carbon plantings, and carefully regulated land-use policy. Machine learning based meta-modelling teamed with scenario discovery revealed the policy and scenario settings required for a sustainable future for the Australian land sector.

Accurate models of water withdrawal are crucial in anticipating the potential water use impacts of drought and climate change. Machine-learning methods are increasingly used in water withdrawal prediction due to their ability to model the complex, nonlinear relationship between water use and potential explanatory factors. However, most machine learning methods do not explicitly address the hierarchical nature of water use data, where multiple observations are typically available for multiple facilities, and these facilities can be grouped an organized in a variety of different ways. This work presents a novel approach for prediction of water withdrawals across multiple usage sectors using an ensemble of models fit at different hierarchical levels. A dataset of over 300,000 records of water withdrawal was used to fit models at the facility and sectoral grouping levels, as well as across facility clusters defined by temporal water use characteristics. Using repeated holdout cross validation, it demonstrates that ensemble predictions based on models learned from different data groupings improve withdrawal predictions for 63% of facilities relative to facility-level models. The relative improvement gained by ensemble modeling was greatest for facilities with fewer observations and higher variance, indicating its potential value in predicting withdrawal for facilities with relatively short data records or data quality issues. Inspection of the ensemble weights indicated that cluster level weights were often higher than sector level weights, pointing towards the value of learning from the behavior of facilities with similar water use patterns, even if they are in a different sector.

We re-examine data from a deep earthquake beneath NE Taiwan as recorded by the Formosa Array to explain mechanisms of a significant later P phase. For 2D simulations, we set up an initial velocity structure with (A) a high Vp anomaly in the mantle wedge above Ryukyu subduction, and (B) a slightly high Vp Eurasian lithosphere to the west with a sub-vertical boundary – based on results of tomographic studies. We conclude that (1) the small-amplitude first P phase is attributed to the energies radiated near the nodal point of the focal mechanism and propagated through (A), (2) those of the significant later P phase are analogous to a head wave that propagates and generates spherical waves along (B) that are received at the surface. Accordingly, stations of zero delay time between the first and second P provide a first-ever portrayal of the Eurasian lithosphere boundary by waveform constraints.

Continental flood basalts intruded and erupted millions of km$^3$ of magma over $\sim$ 1-5 Ma. Previous work proposed the presence of large ($>$ 10$^5$-10$^6$ km$^3$) crustal magma reservoirs to feed these eruptions. However, in Paper I, we illustrated that this model is inconsistent with observations, by combining eruptive rate constraints with geochemical and geophysical observations from the Deccan Traps and other CFBs. Here, we use a new mechanical magma reservoir model to calculate the variation of eruptive fluxes (km$^3$/year) and volumes for different magmatic architectures. We find that a single magma reservoir cannot explain the eruptive rate and duration constraints for CFBs. Using a 1D thermal model and characteristic timescales for magma reservoirs, we conclude that CFB eruptions were likely fed by a number of interconnected small-medium ($\sim$ 10$^2$ - 10$^{3.5}$ km$^3$) magma reservoirs. It is unlikely that each individual magma reservoir participated in every eruption, thus permitting the occasional formation of large xenocrysts (e.g., megacrystic plagioclase). This magmatic architecture permits (a) large volume eruptive episodes with 10s to 100s of years duration, and (b) relatively short time-periods separating eruptive episodes (1000s of years) since multiple mechanisms can trigger eruptions (via magma recharge or volatile exsolution, as opposed to long term (10$^5$ - 10$^6$ year) accumulation of buoyancy overpressure); (c) lack of large upper-crustal intrusive bodies in various geophysical datasets. Our new proposed magmatic architecture has significant implications for the tempo of CFB volatile release (CO$_2$ and SO$_2$), potentially helping explain the pre-K-Pg warming associated with Deccan Traps.

The Australian NWC (North West Cape) signal transmitter is known to strongly interfere with the topside ionosphere. We analyze 456 conjunctions between Swarm A, B and NWC, in addition to 58 conjunctions between NorSat-1 and NWC. The in-situ measurements provided by these satellites include the 16 Hz Swarm Advanced Plasma density dataset, and the novel 1000 Hz plasma density measurements from the m-NLP system aboard NorSat-1. We subject the data to a detailed PSD analysis and subsequent superposed epoch analysis. This allows us to present comprehensive statistics of the NWC-induced plasma fluctuations, both their scale-dependency, and their climatology. The result should be seen in the context of VLF signal transmitter-induced plasma density fluctuations, where we find counter-evidence for the existence of turbulent structuring induced by the NWC transmitter.

The event of 8 September 2017 was characterized by the effects of the arrival of two interplanetary coronal mass ejections on September 6th and 7th and a resultant geomagnetic storm. This storm event has been widely studied due to its extreme geo-effectiveness in the global geospace. In the inner magnetosphere, the effects included a distinct intensification of the ring current and a severely eroded plasmasphere. However, little attention has been paid to the role that the observed substorm injections played on the storm-time ring current. Starting at 1209 UT on September 8th, multiple substorm onsets occurred spreading over a wide magnetic local time range on the dawn side. Multiple substorm injections were observed simultaneously at geosynchronous orbit by the Los Alamos National Laboratory satellites and the Geostationary Operational Environmental Satellites, and by both the Exploration of energization and Radiation in Geospace/Arase and the Van Allen Probes missions deep in the inner magnetosphere. Subsequent buildup of the ring current was observed. In this study, we will investigate the role of the substorm injections on the extreme ring current response by numerical simulations with the physics-based Comprehensive Inner Magnetosphere-Ionosphere model using the geosynchronous data as boundary conditions to the model. Since the ring current has a strong influence on the inner magnetospheric dynamics, we also consider its impacts on the dynamics of the electric field and the plasmasphere. Furthermore, this study addresses the critical need to include substorms in evaluating the geo-effectiveness of geomagnetic storms.

Seafloor spreading at slow rates can be accommodated on large-offset oceanic detachment faults (ODFs), that exhume lower crustal and mantle rocks in footwall domes termed oceanic core complexes (OCCs). Footwall rock experiences large rotation during exhumation, yet important aspects of the kinematics - particularly the relative roles of rigid block rotation and flexure - are not clearly understood. Using a high-resolution numerical model, we explore the exhumation kinematics in the footwall beneath an emergent ODF/OCC. A key feature of the models is that footwall motion is dominated by solid rotation, accommodated by the concave-down ODF. This is attributed to a system behaviour in which the accumulation of distributed plastic strain is minimized. A consequence of these kinematics is that curvature measured along the ODF is representative of a neutral stress configuration, rather than a ‘bent’ one. Instead, it is in the subsequent process of ‘apparent unbending’ that significant flexural stresses are developed in the model footwall. The brittle strain associated with apparent unbending is produced dominantly in extension, beneath the OCC, consistent with earthquake clustering observed in the Trans-Atlantic Geotraverse at the Mid-Atlantic Ridge.

Uncertainty in climate feedbacks is the primary source of spread in model projections of surface temperature response to anthropogenic forcing. Cloud feedback persistently appears as the main source of disagreement in future projections while the combined lapse-rate plus water vapor (LR+WV) feedback is a smaller (~30%), but non-trivial source of uncertainty in climate sensitivity. Here observation-based emergent constraints are adopted to evaluate the intermodel spread in these feedbacks. The observed interannual variation provides a useful constraint on the long-term cloud feedback as evidenced by the consistency between their global-mean values as well as their similar regional contributions to the intermodel spread. However, internal variability does not serve to constrain the long-term LR+WV feedback spread, which we find is mostly associated with the relative humidity response over the tropics. Model differences in hemispheric warming asymmetries, induced primarily by ocean heat uptake differences, also contribute to the spread in water vapor feedback.