A document by Martin Velez Pardo. Click on the document to view its contents.

Quantifying and characterizing suspended sediment is essential to successful monitoring and management of estuaries and coastal environments. To quantify suspended sediment, optical and acoustic backscatter instruments are often used. Optical backscatter systems are more sensitive to fine particles ($<63 \mu m$) and flocs, whereas acoustic backscatter systems are more responsive to larger sand grains ($>63 \mu m$). It is thus challenging to estimate the relative proportion of sand or mud in environments where both types of sediment are present. The suspended sediment concentration measured by these devices depends on the composition of that sediment, so it is also difficult to measure concentration with a single instrument when the composition varies. The objective of this paper is to develop a methodology for characterizing the relative proportions of sand and mud in mixed sediment suspensions by comparing the response of simultaneous optical and acoustic measurements. We derive a sediment composition index (SCI) that can be used to directly predict the relative fraction of sand in suspension. Here we verify the theoretical response of these optical and acoustic instruments in laboratory experiments, and successfully apply this approach to field measurements on the ebb-tidal delta of Ameland Inlet in the Netherlands. Increasing sand content decreases SCI, which was verified in laboratory experiments. A reduction in SCI is seen under more energetic conditions when sand resuspension is expected. Conversely, the SCI increases in calmer conditions when sand settles out, leaving behind finer sediment. This approach provides crucial knowledge of suspended sediment composition in mixed sediment environments.

The early stage of the postseismic phase is characterized by a large deformation rate. Its analysis is thus key to decipher the role played by different mechanisms (afterslip and viscoelasticity) at various time scales. Here, we process GPS data to obtain 30-seconds kinematic position time series recording the surface deformation following the Mw 9.0 Tohoku-Oki megathrust earthquake (2011), and combine them with static solutions over 9 years. We analyze the temporal evolution of the time series and use these observations to image the postseismic slip. We find that the first month of deformation following Tohoku-Oki can be explained by an afterslip mechanism, that exhibits an “Omori-like” decay, with a p-value around 0.75 almost everywhere with the exception of a small region around Ibaraki prefecture where p~1 is observed. This p<1 indicates that the postseismic displacements do not increase logarithmically with time as predicted by rate-and-state rheology. Instead, we argue that early afterslip is associated to a transient brittle creep mechanism. We use numerical simulations to show that an exponent of p<1 can be explained by a combination of thermal activation of local slips and elastic interactions. Over longer time scales, an additional mechanism is required to explain the observed deformation signal, and the transient brittle creep mechanism is combined with viscoelastic relaxation modeled by a Newtonian flow. The spatial analysis reveals two distinct afterslip regions, a major one on the North, associated with a p-value around 0.75, and a smaller one close to the Ibaraki aftershock, associated to p~1.

A document by Andy Baker. Click on the document to view its contents.

A reanalysis dataset produced by the Copernicus Atmosphere Monitoring service (CAMS reanalysis, 2003 - present day) augmented by ERA5 data for the years before 2003 is used to describe the evolution of the 2020 Arctic ozone season and to compare it with years back to 1979. Ozone columns over large parts of the Arctic reached record low values in March and April 2020 because of an exceptionally cold and persistent Arctic polar vortex. Minimum ozone columns were below 250 DU for most of March and the first half of April, with the lowest values of 211 DU in the CAMS reanalysis found on 18 March. Such low values are extremely unusual for the Arctic. The previous years with similarly strong Arctic ozone depletion were 2011 and 1997 with minimum values of 232 DU and 217 DU, respectively. The performance of the CAMS ozone analysis is assessed by comparison with ozone sonde data. We find a clear sign of chemical ozone destruction with ozone severely depleted in a layer between 80-50 hPa in late March and early April when partial pressure values below 2 mPa were observed. Profiles from the limb sounders ACE-FTS and MLS show clear signs of chlorine activation and the presence of polar stratospheric clouds. Monthly mean ozone columns in March 2020 were up to 180 DU or 40% lower than the CAMS climatology (2003-2019) while values for 2011 and 1997 were lower by 31% and 35% respectively.

Various interdisciplinary studies have shown substantial discrepancies between modeled and remotely sensed glacier surface elevation change. It is therefore crucial to better understand and quantify uncertainties associated to both methods. We design a probabilistic framework with the aim to filter outliers, fill data voids and estimate uncertainties in glacier surface elevation changes computed from Digital Elevation Model (DEM) differentiation. The technique is based on a Bayesian formulation of the DEM difference problem and specifically targets surging and debris-covered glaciers, both at glacier and regional scales. We first define a set of physically admissible surface elevation changes as an elevation-dependent probability density function. In a second step, terrain roughness is defined as the main descriptor for DEM uncertainty. Each surface elevation change pixel is a probability distribution. We present validation experiments in High Mountain Asia and show that the model produces results consistent with conventional DEM differencing, while avoiding the caveats of already existing methods. We further demonstrate that accounting for unstable glacier dynamics is crucial for accurate outlier filtering and robust uncertainty estimation. The technique can be applied to other types of remotely sensed glacier quantities (surface velocity etc.) and so would help to improve the characterization of uncertainty associated with changes in glacier mass and dynamics.

Improved observations of ice hydrometeors can lead to better weather predictions and understanding of the hydrological cycle. Global coverage is best achieved by satellites, using active and passive microwave remote sensing due to the inherent penetration capability and sensitivity to the snow and ice cloud particles which scatter and absorb the radiation. Snow and cirrus cloud particles are to a large degree composed of aggregates. While it is known that the shape of these aggregates influence microwave measurements to various degrees, it is currently not fully clear to what extent certain particle features are of importance. For example, of what importance is the shape of individual crystals in the aggregates? This work is an attempt at improving our knowledge on the impact of ice aggregate microphysics on microwave and sub-millimetre scattering properties from a modelling point of view. A large amount of aggregates (roughly 4000) where modelled through several semi-physical stochastic simulations. The aggregates are composed of hexagonal ice crystals of varying axis ratio, ranging from 1/15 (plates) to 15 (columns), and assumed to be oriented in the horizontal plane. Single scattering properties of over 1000 aggregates were then assessed for zenith/nadir observations, using the discrete dipole approximation (DDA) at three typical radar bands (13.4, 35.6, 94.1 GHz) and three passive microwave frequencies (183.3, 325.15 and 664 GHz). An analysis on the sensitivity of these scattering data to various aggregate parameters is presented. In general, extinction was found to be less sensitive to shape than back-scattering at investigated at frequencies. Extinction at 664 GHz in particular was found to be shape insensitive; promising for the sake of the Ice Cloud Imager (ICI) on the upcoming Metop-SG satellite. In contrast, evaluation of triple frequency signatures showed relatively high shape sensitivity; of relevance to future multi-frequency radars.

Glacier surface elevation responds to a variety of localized processes occurring beneath the ice. Subglacial-lake volume change in particular is inherently time-dependent, producing time-varying perturbations in ice-surface elevation. Here, we introduce inverse methods for quantifying time-varying subglacial perturbations from altimetry data and, when available, horizontal surface velocity data. The forward model is based on a small-perturbation approximation of the Stokes equations that is solved efficiently with Fourier transform methods. The inverse methods are derived from variational least-squares optimization problems and the associated normal equations are solved with the conjugate gradient method. We conduct synthetic tests for reconstructing time-varying basal vertical velocity and drag perturbations that are motivated by subglacial-lake activity and slippery spots beneath Antarctic ice streams. We show that incorporation of horizontal surface velocity data as additional constraints can refine altimetry-based inversions or facilitate reconstruction of multiple fields, depending on whether the data are spatially discrete or continuous. We further validate the method by showing that it can reconstruct basal perturbations from synthetic elevation data that are produced by a nonlinear subglacial lake model. With the advent of high spatial and temporal resolution altimetry data from NASA's ICESat-2 mission, these inverse methods will facilitate further assessment of the relation between ice-sheet flow and subglacial processes.

On Venus, the coexistence of large volcanic highlands interpreted as the surface expression of long-lived mantle plumes, alongside coronae, smaller features thought to be caused by transient thermal diapirs, remains enigmatic. Using numerical models of mantle convection with sharp and broad mineral phase transitions, we show that both scales of upwellings are generated in a stagnant lid planet with an interior temperature 400 K warmer than Earth's. The smaller plumes originate from a ~600 km deep internal layer that exists as a consequence of the different sequence of mineral phase transitions that occurs in warmer mantles. These models further produce essential features of Venus' geodynamics, including apparent large scale downwellings, variable heat flow and plains dynamic topography.

Inspired by Comrie (2021), we discuss a few issues related to dust storms and Coccidioidomycosis (Valley fever). There is inconsistency in the term “dust storm” as used by science communities, and the dust data from NOAA Storm Events Database are from diverse sources, unsuitable for assessing dust-Coccidioidomycosis relationships. Population exposure to dust or Coccidioides needs to consider the frequency, magnitude, duration, and spatial coverage of dust events. Given abundant evidence that dust storms are a viable driver to transport Coccidioides, it is in best public interest to advocate that dust storms may put people at risk for contracting Valley fever.

Chemical zoning of minerals, which is commonly caused by incomplete chemical reaction, is often utilized to study magma cooling processes, and metamorphic rock reaction paths. Cr-Al chemical zoning of spinel has been reported as induced by deformation (lattice diffusion). However, there are no studies that address the correlates of chemical distribution (intensity) with deformation, and application methods of the Cr-Al chemical zoning. In this study, we observed differences for intensities of the Cr-Al chemical zoning with various geometrical properties of spinel grains within a dunite sample in the Transition Zone in the Horoman Peridotite Complex in Hokkaido, Japan. Using machine learning analysis, we present relationships between the intensities of the Cr-Al zoning and geometrical properties of spinel grains. We examine connections between the relationships and deformation mechanisms of spinel and estimate deformation temperature based on the results. As a consequence, the spinel grains are clustered into three groups based on the chemical zoning intensity. The intensity is more importantly affected by grain size than aspect ratio and is much greater with increasing grain size. These results suggest that lattice diffusion actively contributes more to total diffusion creep of spinel than grain boundary diffusion with increasing grain size. The deformation temperature of spinel is estimated as 1250°C–1100°C by comparing diffusion flux ratio (Rdiff) and the spinel grains.

In a complex reservoir with such a significant degree of heterogeneity, it’s a demand to characterize the reservoir using different seismic attributes based on the available data within certain time constraints. Pre-stack seismic inversion and Amplitude variation with offset (AVO) are among those techniques which give excellent results in particular for the gas bearing clastic reservoirs delineation because of the remarkable contrast between the latter and the surrounded rocks. However, challenges arise of data shortage in seismic and /or well data may obstacle applying these techniques. Moreover, if the prediction of water saturation (Sw) is needed using the seismic data, it represents a serious challenge because of the independent nonlinear relationship between water saturation and seismic attributes and inversion products. Prediction of water saturation is necessary not only for characterizing pay from non-pay reservoirs but for the economics also. Therefore, Extended Elastic Impedance has been performed to produce a 3D volume of water saturation over the reservoir interval then, 3D Sweetness volume was used in order to grasp the geometry of the sand bodies that have been charged with gas in addition to its internal architecture which could illustrate the different stages in the evolution of the Saffron channel system and the sand bodies distribution both vertically and spatially consequently increase the production and decrease the development risk.

Reliable climate projections are essential for adaptation and mitigation planning. We compare CMIP6 projections for temperature and precipitation to CMIP5 for northern Europe, central Europe and the Mediterranean. The CMIP6 ensemble shows an increased projected summer warming compared to CMIP5. Central Europe was also found to have a stronger drying trend in the summer months in CMIP6. We show that warmer projected summer temperatures are largely driven by CMIP6 sampling higher global climate sensitivities, with broadly similar regional responses to these. For those interested in central estimates of European changes, CMIP6 does not change the existing CMIP5 picture but better samples higher end change for more risk adverse users. However, regional sensitivity is important in central Europe where it accounts for roughly 40$\%$ of the differences between ensembles in projected regional temperature. This analysis raises an important question about whether CMIP6 can be considered to supersede CMIP5, or supplement it

Geometric modelling of Coronal Mass Ejections (CMEs) is a widely used tool for assessing their kinematic evolution. Furthermore, techniques based on geometric modelling, such as ELEvoHI, are being developed into forecast tools for space weather prediction. These models assume that solar wind structure does not affect the evolution of the CME, which is an unquantified source of uncertainty. We use a large number of Cone CME simulations with the HUXt solar wind model to quantify the scale of uncertainty introduced into geometric modelling and the ELEvoHI CME arrival times by solar wind structure. We produce a database of simulations, representing an average, a fast, and an extreme CME scenario, each independently propagating through 100 different ambient solar wind environments. Synthetic heliospheric imager observations of these simulations are then used with a range of geometric models to estimate the CME kinematics. The errors of geometric modelling depend on the location of the observer, but do not seem to depend on the CME scenario. In general, geometric models are biased towards predicting CME apex distances that are larger than the true value. For these CME scenarios, geometric modelling errors are minimised for an observer in the L5 region. Furthermore, geometric modelling errors increase with the level of solar wind structure in the path of the CME. The ELEvoHI arrival time errors are minimised for an observer in the L5 region, with mean absolute arrival time errors of 8.2±1.2h, 8.3±1.0h, and 5.8±0.9h for the average, fast, and extreme CME scenarios

Recent insights into the spatial organization of atmospheric convection have emphasized the importance of its correct representation in Earth System Models (ESM). This study explores new opportunities created when combining a thermal population model on a horizontal microgrid with a decentralized vertical transport model. To this purpose the recently proposed BiOMi population model (Binomials on Microgrids) is used. BiOMi mimicks a population of independent but interacting convective thermals, with their birth, movement and life cycle described as Bernoulli processes. Simple rules of interaction are introduced to reflect observed physical behavior in single cumulus clouds, such as pulsating growth and environmental deformation. Under these rules, thermals can congregate and form longer-lived coherent clusters or chains that resemble cumulus clouds. The formation and evolution of these clusters is a form of self-organization that retains convective memory. Through an online clustering method the microgrid is coupled to a spectral EDMF convection scheme, providing the cluster size distribution it needs as input. This way, the inherently 3D structure of organized convection can in principle be captured in reduced but efficient form. The system is fully decentralized in that central top-down bulk closures are avoided. The main science objective of this study is to provide proof of concept of decentralized frameworks of this kind. To this purpose the BiOMi-EDMF scheme as implemented in the DALES circulation model is tested for various LASSO cases of shallow convection at the ARM SGP site. We find that the scheme achieves stable and realistic diurnal quasi-equilibria (as shown in the figure), and that the associated self-organizing patterns on the microgrid are realistic. Impacts of spatial organization and convective memory on the parameterized transport will be investigated.

Lower-band whistler-mode chorus waves are important to the dynamics of Earth’s radiation belts, playing a key role in accelerating seed population electrons (100’s of keV) to relativistic ($>$ 1 MeV) energies, and in scattering electrons such that they precipitate into the atmosphere. When constructing and using statistical models of lower-band whistler-mode chorus wave power, it is commonly assumed that wave power is spatially distributed with respect to magnetic L-shell. At the same time, these waves are known to drop in power at the plasmapause, a cold plasma boundary which is dynamic in time and space relative to L-shell. This study organizes wave power and propagation direction data with respect to distance from the plasmapause location to evaluate what role the location of the plasmapause may play in defining the spatial distribution of lower band whistler-mode chorus wave power. It is found that characteristics of the statistical spatial distribution of equatorial lower band whistler mode chorus are determined by L-shell, and are largely independent of plasmapause location. The primary physical importance of the plasmapause is to act as an Earthward boundary to lower band whistler mode chorus wave activity. This behavior is consistent with an equatorial lower band whistler mode chorus wave power spatial distribution that follows the L-shell organization of the particles driving wave growth.

As already presented at previous conferences, we have developed an innovative technique for ocean wave observations from space: A dedicated reprocessing of spotlight-mode synthetic aperture radar (SAR) data enables us to produce a series of images covering a total time interval of a few seconds, in which moving wave patterns become visible. In most cases, the duration and quality of the image series is sufficient for computing a wavenumber-frequency spectrum and applying a dispersion relation filter to separate signatures of actual ocean waves from contributions of other physical phenomena and noise. Then the extracted wave signatures can be inverted into surface slope and displacement fields (in the space-time domain) and / or spectra (in the wavenumber-frequency domain) by applying relatively simple linear modulation transfer functions. Altogether this process is much more straightforward than traditional wave retrieval techniques for SAR images, and we think it produces more accurate results, although a full quantitative validation is still to be completed. This presentation at Ocean Sciences 2020 will not focus on the waves themselves, but on secondary information that can be retrieved from the moving wave patterns: Since the dispersion relation of ocean waves depends on water depth and surface current, these two parameters need to be tuned to fit the shape of the “dispersion shell” to the observed spectral energy distribution in each subwindow of a SAR image for which the wave field is analyzed. Accordingly, depth and surface current are obtained as additional outputs of the dispersion relation filter. This methodology is well established in the marine radar community, but has not been applied to satellite data before, where it presents a range of new opportunities as well as specific challenges. We will discuss the potential of the proposed method for practical applications and show first examples of retrieved nearshore wave, depth, and surface current fields.

Magnetic reconnection is a fundamental process known to play a crucial role in electron acceleration and heating, however, the mechanism of electron energization during reconnection is still not fully understood. This study introduces a novel electron acceleration mechanism in which electrons can be accelerated by secondary reconnection in the separatrix region. The secondary reconnection occurs in a thin current sheet resulted from the shear of the out-of-plane Hall magnetic fields of the primary magnetopause reconnection. It results in intense electron energy fluxes towards the primary X-line. This result will likely be an important piece in the puzzle of particle acceleration by reconnection.