Optical televiewer borehole logging within a crevassed region of fast-moving Store Glacier, Greenland, revealed the presence of 35 high-angle planes that cut across the background primary stratification. These planes were composed of a bubble-free layer of refrozen ice, most of which hosted thin laminae of bubble-rich ‘last frozen’ ice, consistent with the planes being the traces of former open crevasses. Several such last-frozen laminae were observed in four traces, suggesting multiple episodes of crevasse reactivation. The frequency of crevasse traces generally decreased with depth, with the deepest detectable trace being 265 m below the surface. This is consistent with the extent of the warmer-than-modelled englacial ice layer in the area, which extends from the surface to a depth of ~400 m. Crevasse trace orientation was strongly clustered around a dip of 63° and a strike that was offset by 71° from orthogonal to the local direction of principal extending strain. The traces’ antecedent crevasses were therefore interpreted to have originated upglacier, probably ~8 km distant involving mixed-mode (I and III) formation. We conclude that deep crevassing is pervasive across Store Glacier, and therefore also at all dynamically similar outlet glaciers. Once healed, their traces represent planes of weakness subject to reactivation during their subsequent advection through the glacier. Given their depth, it is highly likely that such traces - particularly those formed downglacier - survive surface ablation to reach the glacier terminus, where they may represent foci for fracture and iceberg calving.

The Geology teaching and learning processes should be carried out through the interaction of different learning environments, inside the classroom and outdoors. Practical activities are a methodological strategy widely suggested by the Portuguese Ministry of Education for the Geology teaching whose aim is to promote the development of skills (knowledge, ability and attitudes) denying the idea that the subjects of this science are complex and far from the students’ academic interests and needs. This work presents some formal and non-formal education practical activities (e.g. laboratory and fieldwork, mineral fairs, national contests), developed throughout the last year in Sé School, a small school in the north of Portugal, with students of the different grades (Kindergarten to high school).

Wind-driven redistribution of snow is one of the key factors leading to heterogeneous accumulation of snow at small scales. Understanding these processes is, therefore, of great importance to many glaciological and hydrological questions. High-quality information on the wind field is necessary to realistically represent drifting snow. Here, we introduce a novel, intermediate-complexity drifting and blowing snow module for the Weather Research and Forecasting (WRF) model that integrates seamlessly into the standard WRF infrastructure. The module also accounts for snow particle sublimation and considers the thermodynamic feedback on the atmospheric fields. The module was tested in an idealized model environment. The simple and computationally efficient implementation allows this module to be used for small-scale and large-scale simulations in polar and glaciated regions.

Antarctica has been proposed as the dominant source of the meltwater that entered the oceans during Meltwater Pulse 1b (MWP1b) that occurred approximately 11,500 years ago. The deglaciation of heavily glaciated fjords off the coast of Antarctica at approximately this time has provided support for this hypothesis. Further support for this scenario was provided by the fact that the highly non-monotonic relative sea level histories recorded at sites on the coast of Scotland, which had been heavily glaciated at last glacial maximum, could be explained by the inter-hemispheric sea level teleconnection associated with a significant deglaciation of Antarctic ice sheets at this time. That the magnitude of grounded ice loss from Antarctica at MWP1b time was adequate to provide the necessary RSL rise along the coast of Scotland has not been demonstrated. Furthermore, there exist implicit suggestions to the effect that a significant contribution to MWP1b must have also been delivered to the oceans by the abrupt northern hemisphere warming that occurred at the end of the Younger Dryas (YD) cold reversal, which also occurred approximately 11,500 years ago. This warming event occurred due to the rapid intensification of the Atlantic Meridional Overturning Circulation (AMOC) when it recovered after the YD. We present a fingerprinting analysis of the contribution of all major ice sheets to MWP1b using the ICE7G_NA (VM7) model of ice loading history and find that the best agreement between calculated sea level curves and observations is obtained with a minimal Antarctic contribution.

Carbonate melts have been proposed to exist in the lower mantle, but their interaction with other lower mantle melt compositions is poorly understood. To understand miscibility in the carbonate-silicate-metal melt system, we simulate endmember, binary, and ternary melt mixtures and study how their Gibbs free energies of mixing evolve with pressure. We find that carbonate-metal and carbonate-silicate melts have miscibility gaps that close with increasing pressure, while silicate-metal melts are immiscible at all lower-mantle pressures. Extending this analysis to the core-mantle boundary, we suggest three miscible melt fields near the endmember carbonate, silicate, and iron melt compositions. Analysis of the densities of these miscible melt compositions indicates that some carbonate-rich and some silicate-rich melt compositions are gravitationally stable at the core-mantle boundary and could be candidate compositions to explain ultra-low velocity zones. Additionally, we evaluate the speciation of an example immiscible melt composition at various pressures throughout the mantle and identify reduced carbon species that would be expected to form in the melt. Our analysis reveals that a majority of Earth's carbon could have been transported to the core during core-mantle differentiation and that much of Earth's carbon may be stored in the deep interior today.

Underground monitoring of root morphology and their interactions with the environment is critical to understand the overall performance of a plant. Such understanding allows plant breeders to develop plants that are resilient to the adverse effects of climate change and potentially even improve yields for food, fuel, and fiber. We propose and experimentally demonstrate the use of fiber Bragg grating-based fiber optic sensors as a non-destructive technology to measure width and depth of a root-like object and monitor the change in groundwater level as an indicator for soil-water content. Low-cost and continuous remote monitoring analyzed the spectral shift induced optical power change in the fiber optic sensors.

Hydrological variables of a catchment and their corresponding extreme characteristics have a possibility of switching regimes, particularly when a catchment undergoes protracted dry periods. This can result in a catchment experiencing a flow anomaly that is even more extreme than what was historically considered an extreme low flow event for the catchment. Catchments in southeast Australia have been shown to exhibit multiple states of mean annual flows. Given this and studies that suggest that extreme events may be changing with time, it is important to understand whether extremes in flows also have the potential to exist in multiple states. To investigate this, we studied intensity, duration, and frequency (IDF) of low flows for 161 unregulated catchments in southeast Australia. A Hidden Markov Model-based approach was used to examine shifts in the low flow characteristics. We found very strong evidence of low flow intensity exhibiting two distinct states for at least 34 (21%) catchments in the region, providing convincing reasons to believe that extremes in low flows can and have undergone regime changes. The second state of these catchments is often associated with higher values of low flow intensities. Simulation of the duration and frequency of these events, however, needs improvement with the current approach and may be better studied by accounting for climate indicators that may more suitably explain them. Impacts from a changing climate may enhance the triggering of low flows into alternate states, which calls for water managers to plan for changing regimes of extremes.

Coastal wetlands provide a wealth of ecosystem services, including improved water quality, protection from storm surges, and wildlife habitat. Louisiana’s wetlands, however, are threatened by development, pollution, and relative sea level rise (RSLR)—the combination of sea level rise and subsidence rates. Despite widespread wetland loss, areas such as the Wax Lake and Atchafalaya river deltas are in fact growing due to their sediment loads, resulting in a complex of both degradation and aggradation along the Louisiana coast. In order to understand and model how coastal wetlands are responding to RSLR, there is a need for improved vegetation mapping, biomass estimation, and landscape-scale study of accretionary processes. AVIRIS-NG offers high spatial and spectral resolution data that can be integrated with external datasets—including from in situ measurements, monitoring stations, and other remote sensing platforms—to study these distributions and processes. Spectra derived from AVIRIS-NG imagery were used to parameterize Multiple Endmember Spectral Mixture Analysis (MESMA) for mapping vegetation functional types in addition to partial least squares regression (PLSR) models for plant aboveground biomass (AGB). The historical Landsat record complemented this analysis by deriving maps of change in wetland health and sediment availability through time. Each of these remotely sensed parameters were investigated to determine their combined relationship to Louisiana’s coastal accretion rates. In quantifying landscape-scale processes that impact wetland accretion, this research aids the assessment of coastal resiliency in the face of sea level rise. Further, the investigated imaging spectroscopy methods pertaining to vegetation mapping, biomass estimation, and accretionary modeling will inform future studies under the global Surface Biology and Geology mission.

Carbon fluxes from agroecosystems contribute to the variability in the carbon cycle and atmospheric [CO2]. In this study, we used the Integrated Science Assessment Model (ISAM) equipped with a spring wheat module to study carbon fluxes and their variability in spring wheat agroecosystems of India. First, ISAM was run in the site-scale mode to simulate the Gross Primary Production (GPP), Total Ecosystem Respiration (TER), and Net Ecosystem Production (NEP) over an experimental spring wheat site in the north India. Comparison with flux-tower observations showed that the spring wheat module in ISAM can match the observed flux patterns better than generic crop models. Next, regional-scale runs were conducted to simulate carbon fluxes across the country for the 1980-2016 period. Results showed that the fluxes vary widely, primarily due to variations in planting dates across regions. Fluxes peak earlier in the eastern and central parts of the country, where the crops are planted earlier. All fluxes show statistically significant increasing trends (p<.01) during the study period. The GPP, Net Primary Production (NPP), Autotrophic respiration (Ra), and Heterotrophic Respiration (Rh) increased at 1.272, 0.945, 0.579, 0.328, and 0.366 TgC/yr2, respectively. Numerical experiments were conducted to study how natural forcings like changing temperature and [CO2] and agricultural management practices like nitrogen fertilization and water availability could contribute to the increasing trends. The experiments revealed that increasing [CO2], nitrogen fertilization, and water added through irrigation contributed to the increase of carbon fluxes, with nitrogen fertilization having the strongest effect.

The shallow 1971 Mw 6.6 San Fernando, California earthquake involved a complex rupture process on an immature thrust fault with a non-planar geometry, and is notable for having a higher component of left-lateral surface slip than expected from seismic models. We extract its 3-D coseismic surface displacement field from aerial stereo photographs and document the amount and width of the vertical and strike-parallel components of distributed deformation along strike. The results confirm the significant left-lateral surface offsets, suggesting a slip vector rotation at shallow depths. Comparing our offsets against field measurements of fault slip, we observe that most of the offset was accommodated in the damage zone, with off-fault deformation averaging 68% in both the strike-parallel and vertical components. However, the magnitude and width of off-fault deformation behave differently between the vertical and strike-parallel components, which, along with the rotation in rake near the surface, can be explained by dynamic rupture effects.

A new augmented Ensemble of Data Assimilations (EDA) technique to estimate background error covariances (B-matrix) has been developed for the Copernicus European Regional Re-Analysis (CERRA). The B-matrix is modelled on a bi-Fourier limited area model. Background errors are assumed isotropic, homogeneous and non-separable. Linearised geostrophic and hydrostatic balances are incorporated as multivariate relationships, coupling vorticity and geopotential extended to mass-wind and specific humidity fields via the f-plane approximation. The B-matrix is estimated by a new 10-member CERRA-EDA system, temporally tethered to real-time meteorological situations. The EDA forecast differences comprise two main pools: seasonal and daily. The seasonal component is pre-prepared at reanalysis-resolution (5.5km). The new augmentation governs real-time mixture of winter and summer differences. The daily component is an 11km moving 2.5 day average. B-matrix re-estimation occurs every 2 days, with a fixed split of 80-20\% seasonal-daily. We consider a case study to illustrate the potential of CERRA-EDA to estimate weather regime change. The most influential factors are temporal evolution of spatial observation coverage, and varying the seasonal-daily split. Background error statistics, improvements in analysis and forecast skill scores and overall assimilation system performance are shown and discussed.

Global food security is negatively affected by drought. Climate projections show that drought frequency and intensity may increase in different parts of the globe. Early season forecasts on drought occurrence and severity could help to better mitigate the negative consequences of drought. The objective of this study was to assess if interannual variability in agricultural productivity in Chile can be accurately predicted from freely-available, near real-time data sources. As the response variable, we used the standard score of seasonal cumulative NDVI (zcNDVI), based on 2000-2017 data from Moderate Resolution Imaging Spectroradiometer (MODIS), as a proxy for anomalies of seasonal primary productivity. The predictions were performed with forecast lead-times from one- to six-month before the end of the growing season, which varied between census units in Chile. Predictor variables included the zcNDVI obtained by cumulating NDVI from season start up to prediction time; standardised precipitation indices, derived from satellite rainfall estimates, for time-scales of 1, 3, 6, 12 and 24 months; the Pacific Decadal Oscillation and the Multivariate ENSO oscillation indices; the length of the growing season, and latitude and longitude. We used two prediction approaches: (i) optimal linear regression (OLR) whereby for each census unit the single predictor was selected that best explained the interannual zcNDVI variability, and (ii) a multi-layer feedforward neural network architecture, often called deep learning (DL), where all predictors for all units were combined in a single spatio-temporal model. Both approaches were evaluated with a leave-one-year-out cross-validation procedure. Both methods showed good prediction accuracies for small lead times and similar values for all lead times. The mean R2cv values for OLR were 0.95, 0.83, 0.68, 0.56, 0.46 and 0.37, against 0.96, 0.84, 0.65, 0.54, 0.46 and 0.38 for DL, for one, two, three, four, five, and six months lead time, respectively. Given the wide range of climates and vegetation types covered within the study area, we expect that the presented models can contribute to an improved early warning system for agricultural drought in different geographical settings around the globe.

As phytoplankton form the base of the marine food web, understanding the controls on their abundance is fundamental to understanding marine ecology and how it might be altered by global climate change. While many Earth System Models (ESMs) predict phytoplankton biomass, it is unclear whether they properly capture the mechanistic relationships that control this quantity in the real ocean. In this paper, we used Random Forest (RF) analysis to analyze the output of ESMs and observational datasets. We gathered information from 13 ESMs and two observational datasets. The target variable was phytoplankton carbon and the predictors included environmental parameters known to influence phytoplankton, such as nutrients, light, mixed layer depth, salinity, temperature, and upwelling. We examined three questions: (1) What fractions of variability in ESMs and observations can be linked to the large-scale environmental variables simulated by ESMs? (2) What are the dominant predictors and relationships affecting phytoplankton biomass? (3) How well do ESMs simulate phytoplankton carbon and do they simulate the relationships we see in observations? We show that about 88% to 96% of the variability in observational datasets and greater than 98% in the ESMs was accounted for by variables known to influence phytoplankton biomass from large-scale environmental variables. The dominant predictors in the observational datasets were dissolved iron and shortwave radiation. The dominant predictors in the ESMs were dissolved iron, shortwave radiation, and mixed layer depth. While relationships in most of the ESMs matched the general trends seen in the observations, significant quantitative differences were seen. While the assumption made by ESMs that large-scale environmental conditions control phytoplankton biomass appears to hold in the real world, much work remains to be done to ensure that ESMs properly represent these controls.

A document by Marina Ruiz Sánchez-Oro. Click on the document to view its contents.

Blue corona discharges are bursts of streamers often observed at the top of thunderclouds, but the cloud conditions that facilitate them are not well known. Here we present observations by the Atmosphere-Space Interactions Monitor of 92 corona discharges as it passed over cyclone Fani in the Bay of Bengal. The discharges formed in convective cells of unstable air carried from land over the Indian Ocean, with CAPE reaching ~6000 J kg-1. The CALIPSO satellite passed over one of the cells ~12 min after ASIM, taking the first measurements of the microphysics at the top of a cloud generating corona discharges. We find the discharges occur in a region of strong convection, the cloud reaching into the stratosphere with ice/water content ~0.1 g m-3, photon mean free path ~ 3 m and ice crystal number density ~5e7 m-3. Measurements by a lightning detection network suggest the charge structures are folded.

Water age and flow pathways should be related; however, it is still generally unclear how integrated catchment runoff generation mechanisms result in streamflow age distributions at the outlet. Here, we combine field observations of runoff generation at the Dry Creek catchment with StorAge Selection (SAS) age models to explore the relationship between streamwater age and runoff pathways. Dry Creek is a 3.5 km2 catchment in the Northern California Coast Ranges with a Mediterranean climate, and, despite an average rainfall of ~1,800 mm/yr, is an oak savannah due to the limited water storage capacity. Runoff lag to peak—after initial seasonal wet-up—is rapid (~1-2 hours), and total annual streamflow consists predominantly of saturation overland flow, based on field mapping of saturated extents and an inferred runoff threshold for the expansion of saturation extent beyond the geomorphic channel. SAS modeling based on daily isotope sampling reveals that streamflow is typically older than one day. Because streamflow is mostly overland flow, this means that a significant portion of overland flow must not be event-rain but instead derive from older, non-event groundwater returning to the surface, consistent with field observations of exfiltrating head gradients, return flow through macropores, and extensive saturation days after storm events. We conclude that even in a landscape with widespread overland flow, runoff pathways may be longer and slower than anticipated. Our findings have implications for the assumptions built into widely used hydrograph separation inferences, namely, the assumption that overland flow consists of new (event) water.

Science is undergoing very rapid changes due to the larger number of people having the opportunity to do science and also with the development of new and revolutionary techniques (e.g. machine learning). The new concepts discovered to end up not gaining the deserved prominence and are quickly discarded. We live in a world where great discoveries have already been made and small new ideas need to be cultivated and developed to rise to their deserved significance. An interesting image can be a start point to attract scientists to know more about some subject, but they are considered a time-consuming effort and difficult to be done. The worries with the creation of the images are not limited to beauty, but accurate drawing is fundamental to science. Illustrated scientific posters made by dedicated design are attractive but are often connected to companies and associated with the sale of a product. Although the idea presented in simple posters can lead to the imagination of new structures or relationships, one image of this idea could more easily explain the concept and attract people, encouraging a scientific debate. A list of commercial programs can be cited as Linkscape, Adobe packages (CorelDRAW, illustrator, etc.), Microsoft packages (Paint, PowerPoint, etc.) or even Matlab functions and this work will present images produced for a better understanding of ionospheric sciences. It is common knowledge that human memory is mostly visual. The digital images can not only be used in paper posters but also in digital posters, projections, websites, etc. The idea is to motivate ionospheric scientists to draw their discoveries. The images, and the scientific work itself, begin in a simple way and gain complexity with the advancement of the scientific discoveries and could be used for the author as a personal way to instigate a continuation of the study. Complex systems studies like the ionospheric sciences are often composed of multidisciplinary groups and are important to quickly explain the meaning of some concept. To investigate the perception of the scientist about the importance of images in explaining scientific concepts a questionnaire was done to collect information about the importance of scientific images in different fields of investigation.

Inverse methods form the basis of many investigations of the structure of the lithosphere-asthenosphere system as they provide the basis for physics-based subsurface imaging from surface and/or near-surface measurements. Steady increases in computational capabilities and methodological improvements have resulted in increasingly detailed three-dimensional models of the Earth based on inverse methods. While these models can show an impressive array of features, it may be difficult for non-specialists to assess which aspects can be considered reliable and which are tenuous, or are artefacts of the mathematical formulation or data collection. In this paper we address the fundamental issues of feature reliability due to limited resolution and model sensitivity to data noise for researchers who do not work with intimately with inverse methods. We include and introductory overview of the mathematical formulation of inversion methods and define commonly used terms and concepts. We then present two case studies based on data from USArray in the western United States. The first case study utilizes magnetotelluric array data to construct a three-dimensional model of electrical resistivity to a depth of approximately 300 km. We use this example to demonstrate fundamental issues regarding data fit, data coverage, and model parameterization. The second case study discusses how we can incorporate petrological and mineral physics information directly into the inversion approach to create models that are compatible with constraints on the temperature and composition of the lithosphere. We will discuss the implications for practical use of these models in interpretations and provide guidelines on how to evaluate such models.