Earthquake stress drop is an important source parameter that directly links to strong ground motion. However, estimating stress drops is often challenging due to many factors, such as data limitations, methodology, and attenuation. Different studies may yield highly inconsistent stress drop values for the same earthquakes, leading to different interpretations of stress drop scaling and spatial patterns. Stress drop is also important for interpretation of earthquake triggering processes. In particular, the roles of foreshocks have continuous debate, and some recent studies show that detailed source parameter analysis is the key. In this study, we combine analyses of three sequences in Southern California using different methods to investigate the resolution of stress drops and the roles of foreshocks. The three sequences include the M7 El-Mayor Cucapah, the 2012 M5 Brawley swarm, and the 2019 M7 Ridgecrest sequence. The Ridgecrest sequence will participate in the Community Stress Drop Validation Study. For each sequence, we apply an improved stacking method and a spectral ratio method. We will use different types of waves: P-wave, S-wave, and Coda-wave. Stress drop results from this study will be compared with available previous studies. We will first discuss the influence of wave type and methodology on stress drop estimations, then we will investigate detailed stress interactions between foreshocks and the mainshock for different types of earthquake sequences (i.e., mainshock-aftershock and swarms that involve aseismic slip or fluid).

GOSAT (Greenhouse gases Observing SATellite) observations ameliorate inversion analysis of greenhouse gas emissions. Meso-scale atmospheric transport model (AIST-MM, National Institute of Advanced Industrial Science and Technology-Mesoscale Model) and global-scale transport model (NICAM-TM, Nonhydrostatic ICosahedral Atmospheric Model-Transport Model) have been coupled for GOSAT data assimilation in order to estimate CO2 emission from mega-city Tokyo. However, forests in the north and west of Kanto region, of which Tokyo Metropolis is the center, generate significant biogenic CO2 fluxes and such atmosphere-biosphere gas exchange remains to be properly calculated during the modeling processes. In this study, we use MODIS products to simulate regional GPP (gross primary production), vegetational and soil respirations based on BEAMS (Biosphere model integrating Eco-physiological And Mechanistic approaches using Satellite data) algorithms. By integrating this atmosphere-terrestrial ecosystem carbon balance module to our regional inversion analysis, we aim at more precise estimation of CO2 emission from Tokyo. Reference https://doi.org/10.1175/1520-0450(1995)034<1439:TTILWA>2.0.CO;2 https://doi.org/10.2151/jmsj.79.11 https://doi.org/10.2151/jmsj.2011-306 https://doi.org/10.1029/2005JG000045 https://doi.org/10.1016/j.rse.2011.03.007

Regional numerical modeling provides a valuable means to assess important aspects of natural systems when the cost and effort of direct observation is impractical; the Arctic Ocean and its marginal seas and archipelagos represent a domain where such applications abound. We describe the configuration of and results from a set of coupled sea-ice ocean circulation models that are based on the Regional Ocean Modeling System (ROMS). Challenging features include large fresh water fluxes from the major Arctic rivers, seasonal land-fast ice, and ice-covered open boundary conditions in nested models. A broad-scale domain, dubbed the Pan-Arctic ROMS (PAROMS) model, extends from the Aleutian Islands to southern Greenland using a telescoping horizontal grid spacing that varies from 4 km in the Pacific to 8 km in the Atlantic. Higher-resolution domains include nested grids at 3 km and 500 m grid spacing. Coastal discharges are prescribed as lateral inflows distributed over the depth of the ocean-land interface. The model includes tides, sea ice, updated bathymetry, and atmospheric forcing from the MERRA reanalysis. We assess the model’s performance with respect to tides, storm surges, wind-driven circulation, and thermohaline fields. A hindcast integrated over 1983-2015 provides a means to assess synoptic, seasonal and inter-annual variability. Applications include investigations of shelf flow field pathways, residence times and advective timescales, and energetics balances.

The study and investigation of temperature trends is quite essential for a country like India, whose economy is largely dependent on agriculture as temperature greatly influences rainfall and evapotranspiration processes. Temperature along with other meteorological parameters largely affect the water availability over a region. Almost 83% water is used in agriculture, 12% is consumed by industries and merely 5% is used for domestic purposes. In the present study, long term trend with temperature data of 64 years (1951-2014) was analyzed for maximum, minimum and mean temperature values. After removal of all significant correlation in the data, Mann-Kendall test (MK) was applied. To identify the trends Sen’s slope estimator was used and the overall percent change for the temperature over the study area was determined. To interpolate the spatial pattern an inverse distance weighted interpolation method (IDW) was used using ArcGIS 10.2.2. The analysis showed a significant increase in the trend of temperature for annual and seasonal series during the entire period over the study area i.e. Kharun watershed, Chhattisgarh, India. These results will be quite helpful and play an important role in managing water resources in the basin and will be useful for future planning of irrigation and agricultural practices.

NASA’s Atmospheric Tomography Mission (ATom) deployed in each of the four seasons during 2016-2018, the DC-8 aircraft in order to establish global-scale datasets intended to improve the representation of chemically reactive gases in global atmospheric chemistry models (ACMs). The Global Modeling Initiative (GMI) executed simulations for each ATom flight using the GMI Chemistry Transport Model (GMI-CTM) to provide species concentrations of chemical gases along the DC-8 flight transects. To solve the problem of translating the GMI-CTM simulation data to the unique spatial resolutions of each ATom flight, the GMI ICARTT Processing Software (GMI-IPS) was developed. The GMI-IPS is written in Python and provides data processing, flight extraction, and visualization support for aircraft research projects using ICARTT format, which is a standard format for airborne instrument data. Additionally, the GMI-IPS interpolates global gridded model data from Hierarchical Data Format (HDF) to ICARTT flight transects. Software classes for instruments and collections provided by the ATom DC-8 aircraft such as MER10, MMS, etc. are derived from a common base class. Other functionality provided by the GMI-IPS are: deriving missing flight entries along a transect, reading ICARTT entries from file, providing Python data structures for storing flight and model information, and more. The GMI-IPS is GIT source controlled, has approximately 30,000 lines of code, and supports parallelization across data collections. It delivered GMI-CTM data for more than forty distinct DC-8 aircraft flights that took place under ATom. The output ICARTT files adhere to format standard V1.1, and pass the scan utility provided by NASA LaRC Airborne Science Data for Atmospheric Composition. This presentation will include a software and methods overview, and results from ATom, including assessments using the GMI-CTM showing how well observations from ATom flight transects represent a broader region.

Operational ocean and wave models are used to produce forecasts for navigation but also required for a wide range of services, most of them relying on particle tracking methods. Improving the forecast capabilities and accuracy of a model is then a constant necessity in order to deliver more reliable services. The Marine Institute in Ireland is running several coastal operational models, one of them focuses on the area of Galway Bay in the west coast. At the moment it consists of a stand-alone ocean application, it is the purpose of the work presented here to set-up a coupled application with a wave model. Coupled models are a recent development in ocean modelling, developer teams have included ocean and wave coupling by combining existing models each dedicated to a specific physics. Two theoretical formulations are mostly used for the implementation, both giving the same equations of evolution and interaction terms known as the vortex-force formalism. One approach is using a Lagrangian framework defining an exact averaged operator following the fluid particles, the other approach is Eulerian making use of a multi-scale expansion. In both cases the larger current components are found to be forced by gravity and infra-gravity waves. The Coupled Ocean Atmosphere Wave Sediment Transport (COAWST) modelling system is a widely used code, the vortex-force formalism has been implemented in 2012 coupling the Regional Ocean Modeling System (ROMS) with the Simulation Wave Nearshore wave model (SWAN). The implementation has been validated with academic cases and used in several real case studies in the last decade. The work presented here is making use of COAWST, a coupled model is set-up for Galway Bay running a 1-year hind-cast application for 2017 and preliminary results are shown here. The performance of the coupled model is compared with each stand-alone model, using in-situ data as a reference. In the last releases of COAWST the wave model WAVEWATCHIII has been added and can be used in the coupled system. This new feature is tested and the results are compared against SWAN, both wave codes are solving the same equations but different technical choices have been made resulting in different capabilities.

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.

Recent research has demonstrated the existence of a new type of solar event, the “terminator”. Unlike the Sun’s signature events, flares and Coronal Mass Ejections, the terminator takes place in the solar interior. The terminator signals the end of a magnetic activity cycle at the Sun’s equator and the start of a sunspot cycle at mid latitudes. Observations indicate that the time difference between these events is very short, less than a solar rotation, in the context of the sunspot cycle. As the (definitive) start and end point of solar activity cycles the precise timing of terminators should permit new investigations into the meteorology of our star’s atmosphere. In this letter we use a standard method in signal processing, the Hilbert transform, to identify a mathematically robust signature of terminators in sunspot records and in radiative proxies. Using this technique we can achieve higher fidelity terminator timing than previous estimates have permitted. Further, this method presents a unique opportunity to project when the next terminator will occur, 2020.33(±0.16), and trigger the growth of sunspot cycle 25. We also will use this method to show why Cycle 23 was unusually long, why the Cycle 23-24 minimum was unusually quiet, and why neither of these occurrences will happen with the end of Cycle 24. Ignoring the wealth of observational evidence and viewing the solar activity cycle as merely the growth and decay of sunspot number is one “social constraint that hampers progress” to be overcome.

i) Within the framework of 3D resistive MHD, we simulate the formation of a plasma jet with the morphology, upward velocity and timescale formation similar to those expected for Type II spicules as described in González-Avilés et al. (2018a). ii) In this paper we analyze the transverse displacements and rotational type motion of the jet. iii) We calculate time series of the velocity components in different points near to the jet for various heights and find transverse oscillations in agreement with spicule observations. iv) By analyzing temperature isosurfaces, we find that the line-ofsight (LOS) is approximately perpendicular to the jet axis. v) The jet shows a red-blue shift pattern caused by rotational motion

To make timely decisions for weather -and climate-related disasters and vulnerabilities, decision makers need current information that can be readily shared and communicated to stakeholders. To date, geospatial data is distributed using monolithic storage architectures and formats best suited for traditional research applications. Thus, everyday decision-makers face significant “barriers to entry” when trying to access, explore, and modify vast historical archives and real time data feeds. To address this need, we are developing a server architecture for rapidly creating and publishing data products. Privileged users can rapidly create or change products by operating on another product or combining multiple disparate data sources together. Users can then consume these new products using OGC-compliant WMS/WCS clients such as ArcGIS, QGIS, or Leaflet. This enables decision makers to effectively communicate with stakeholders using customized maps. Moreover, this capability enables products to be rapidly updated in cases where timely information is important. Our server architecture is containerized, making it easy to deploy on various architectures including serverless cloud resources. It is implemented in Python, leverages the plug-and-play data wrangling capabilities of the PODPAC library, and uses a custom library for serving OGC-compliant data. The result is an easy-to-use architecture for rapidly publishing custom geospatial products that exploit vast earth science data resources. We will demonstrate our server capabilities by showing how privileged users can build a set of products that are computed on-demand starting from a fresh server. Using Jupyter Lab notebooks, we will create products that modify single data sources as well as products that combine multiple disparate sources. We will then show how users can consume these products using OGC-compliant clients. Next, we will detail our cloud-based, serverless deployment of this technology using Amazon Web Services. Finally, we will discuss the advantages of our approach along with any caveats. Enabling everyday decision makers to rapidly create and share geospatial data will revolutionize their productivity and effectiveness for assessing and remediating weather- and climate-related vulnerabilities and disasters.

Up to now, almost all of the ground motion modeling and hazard assessment for induced seismicity in Groningen, the Netherlands, has been focused on the horizontal components of earthquake waves. Including the vertical component in site response studies is hardly being done for low magnitude earthquakes. Since the top part of the soils in the Netherlands is practically always unconsolidated, the elastic waves generated by deeper (~3000m) seated earthquakes will be subject to transformation when arriving in these layers. Recordings over a range of depth levels in a borehole show most of the amplification in the upper 50 meters of the sedimentary cover. We observe not only a strong amplification from shear waves on the horizontal components, but also from longitudinal waves on the vertical component. Furthermore, the seismic velocities show a large lateral heterogeneity. A better understanding of the vertical component of low magnitude earthquakes (to date, max M=3.6 in Groningen) aims to support the design of re-enforcement measures for buildings in areas affected by induced seismicity. This study focuses on longitudinal wave amplification in a sedimentary basin setting.Generally, the vertical component of ground motion is less than the horizontal, but the longitudinal waves are concentrated in a high frequency band which can cause damage to buildings with vertical periods in this range. Furthermore, interference between the longitudinal and shear waves might contribute to extra damage on structures. In Groningen, a dense borehole network is continuously recording seismic activity. From 19 seismic events with a minimum of magnitude two, transfer functions are retrieved between geophones at 50m depth and accelerometers at the surface, for 70 borehole sites. Peak frequencies and amplitudes derived from the transfer functions, do show significant variability across the region. Highest longitudinal wave amplification is measured in the center of the field, which is also the epicenter of most seismic activity. We investigate if the variations in amplification can be linked to the local geology. Additionally, the possibility and consequences of interference between the shear and longitudinal waves are presented.

Mantle viscosity controls a variety of geodynamic processes, e.g. Glacial Isostatic Adjustment (GIA). Constraining GIA using better viscosity estimates would improve our estimates of recent ice mass loss from the Greenland and Antarctic ice sheets and the associated sea level rise. However, mantle viscosity is poorly constrained as it can rarely be measured directly, making geophysical observations that could place constraints on viscosity more essential. Empirically, viscosity is mainly controlled by temperature, water content of nominally anhydrous minerals, partial melt, grain size and stress. Of these, temperature, water content, and the presence of partial melt can be inferred from seismic and magnetotelluric (MT) measurements, which are important tools in imaging the subsurface of the Earth. In this study, we develop a method to estimate mantle viscosity in which we: (1) constrain temperature from MT, seismic, and surface heat flow observations; (2) constrain compositional structure (i.e., water content and partial melt) from MT and seismic data coupled with experimental mineral physics data; and finally, (3) convert the calculated thermal and compositional structures into a viscosity structure. In each step, we assess and quantify the involved uncertainties. In addition, we introduce a useful parameter – the viscosity ratio (a ratio between viscosities of a target region and a nearby reference region at the same stress and grain size), and quantify its amplitude and uncertainty for a range of temperatures and water contents. We find that the uncertainty in this ratio is relatively small when computed from both seismic and MT observations, compared to either constraint applied alone. We also explore how viscosity ratio uncertainties vary with grain size and stress. Information about grain size can potentially be obtained from seismic attenuation or tectonic history. Overall, we find that both seismic and MT observations can considerably improve estimates of mantle viscosity, and place useful constraints on its lateral variations in the upper mantle. Geophysically-derived mantle viscosity models can be calibrated in areas like Scandinavia, which has well-constrained GIA models, and applied to polar regions where the GIA response is poorly known.

Miocene-Pliocene strata in the Pacific Northwest preserve a rich record of landscape evolution and coincident faunal shifts. Considerable research efforts in the past century have been aimed at understanding major drainage reorganization and its relation to tectonics, volcanism, climate change, and aquatic biota. Many studies have focused on fish fossils, which show that Miocene fish diversity, particularly salmonoids, displays great adaptive plasticity. However, the details and mechanisms for river reorganization are still debated. Here we present new and recent detrital zircon provenance results from modern and ancestral river sands collected throughout Oregon, Washington, and Idaho. We synthesize our new results and interpretations with existing paleontological evidence for basin isolation and drainage capture. Detrital zircons from the Columbia Basin (CB) consistently show populations derived from the Snake River Plain (SRP) throughout late Miocene-Pliocene time. However, comparisons of Miocene-Pliocene detrital zircons from the CB to modern major rivers and tributaries in the CB and SPR show that the upstream eastern SRP is a major contributor. CB strata do not require zircons sourced from the western SRP, where Pliocene Lake Idaho existed in a large, deep, and occasionally internally drained basin. Based on the age and provenance results, we suggest that the transiting Yellowstone Hotspot divided the modern SRP into two basins: the western basin was isolated and possibly closed, while the eastern basin drained northward into the modern Clark Fork and Columbia Rivers. This scenario is consistent with fish, mollusk, and rodent fossil evidence from the SRP and CB. In addition, the detrital zircon data indicate a Miocene confluence of the Columbia and Clearwater rivers south of the Saddle Mountains anticline, but north of the current Columbia-Snake River confluence. We also find that the Salmon River may have been captured by the Clearwater River sometime between 4.6 and 8.5 Ma. Prior to this time, the Salmon River likely drained into the SRP. Lastly, we find that faunal localities in southern Oregon suggested to contain evidence for fluvial connection between the western SRP and California are ~2.5 Ma, younger than the incision of Hells Canyon.

Characterization of rocks from geothermal reservoirs is crucial to better understand their behavior. Los Humeros geothermal field contains one of the most important reservoirs in Mexico. This reservoir contains volcanic rocks product of various cooling processes. In the present study, we use four different samples, three distinct andesites and one highly altered volcanic rock from Los Humeros reservoir. We acquired 3D images using X-rays microtomography to visualize and estimate porosity in these rocks. In the three andesites, we used the maximum possible resolution of 0.5 micros/voxel. In the altered sample, we used four different resolutions of 0.5, 3, 14 and 53 microns/voxel and took physical and digital subsamples at various locations in the original core plug to compare and identify its heterogeneity. From the 3D images, image porosity after segmentation and fractal-scaling porosity were estimated. From the core plug, the experimental porosity was obtained by a collaborative lab (IPICYT). The fractal-scaling porosity approximates the fractal porosity under the assumption that all pores have the same size approximately equal to the box size, used in the box counting method. The calculations were done in all images at all resolutions. We found that the image porosity at the highest resolution is generally closer to the experimental porosity. However, the altered sample, even at this resolution, presents some variations. The image porosity of this sample at the other resolutions shows values far from the experimental porosity, as the pore space could not be fully capture. As a matter of fact, the fractal dimension increases from the lower resolution to the higher resolution as a mainly magnification effect, as well as the porosity. The fractal-scaling porosity, which uses the fractal dimension, the average spherical equivalent diameter, and the size of the image gives estimates of porosity very close to the image porosity. This demonstrates the fractal character of all these samples, implying that the pore structure in these rocks can be better evaluated using Fractal geometry. The different results obtained in the altered sample corroborates its diverse processes. To complete this study, we are working on finding potential correlations between these porous structures and permeability, and adding more samples.

Deterministic Numerical Weather Prediction (NWP) models are the state-of-the-science models to provide reliable weather forecasts that provide indispensable actionable data to society. However, each NWP model run carry uncertainty caused by errors in initial conditions and assumptions in the model. As a result, probabilistic forecasts can be used to quantify and even correct the model bias. The Analog Ensemble (AnEn) technique uses a historical dataset of past deterministic forecasts and their associated observations to generate an ensemble of future outcomes. One of the main advantages of the AnEn technique, along with other related statistical ensemble techniques, is that it is not necessary to run multiple NWP runs by varying initial conditions or model settings. However, all these techniques require access to the entire historical dataset to generate analogs. Moreover, these techniques require reading the dataset for every forecast which is computationally expensive. In this work, the whole historical dataset is replaced by a model that has the capability to learn the Probability Density Function (PDF) of that dataset. Specifically, we utilize a Conditional Variational Autoencoder (CVAE) deep generative machine learning model in order to correct the wind speed forecasts of North American Mesoscale (NAM) forecasting system. As a result, we feed the values forecasted by the NWP model as a condition to our CVAE and generate an ensemble used to correct the forecasted value in constant time and with small memory usage. Initial results show that CVAE probabilistic performance is comparable to AnEn while CVAE can be up to 25 and 2 times smaller in memory and runtime, respectively, for 5 years of historical data.

We consider the statistical properties of solutions of the stochastic fractional relaxation equation that has been proposed as a model for the earth’s energy balance. In thisequation, the (scaling) fractional derivative term modelsenergy storage processes that occur over a wide range of space and time scales. Up until now, stochastic fractionalrelaxation processes have only been considered withRiemann-Liouville fractional derivatives in the context of random walk processes where it yields highlynonstationary behaviour. For our purposes we require the stationary processes that are the solutions of the Weyl fractional relaxation equations whose domain is −∞ to t rather than 0 to t. We develop a framework for handling fractional equationsdriven by white noise forcings. To avoid divergences, wefollow the approach used in fractional Brownian motion(fBm). The resulting fractional relaxation motions (fRm) and fractional relaxation noises (fRn) generalize the more familiar fBm and fGn (fractional Gaussian noise). Weanalytically determine both the small and large scale limitsand show extensive analytic and numerical results on the autocorrelation functions, Haar fluctuations and spectra. We display sample realizations. Finally, we discuss the prediction of fRn, fRm which – due to long memories - is a past value problem, not an initial value problem. We develop an analytic formula for the fRnforecast skill and compare it to fGn. Although the large scale limit is an (unpredictable) white noise that is attainedin a slow power law manner, when the temporal resolutionof the series is small compared to the relaxation time, fRncan mimick a long memory process with a wide range of exponents ranging from fGn to fBm and beyond. Wediscuss the implications for monthly, seasonal, annualforecasts of the earth’s temperature.

Cloud-top heights (CTH) from the Multiangle Imaging Spectroradiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra constitute our longest-running single-platform CTH record from a stable orbit. Here, we provide the first evaluation of the Terra Level 2 CTH record against collocated International Space Station Cloud-Aerosol Transport System (CATS) lidar observations between 50ºN - 50ºS. Bias and precision of Terra CTH relative to CATS, calculated from the normality of CTH error histograms, are shown to be strongly tied to cloud horizontal and vertical heterogeneity and altitude. For single-layered, unbroken, optically thick clouds observed for all altitudes, the uncertainty in MODIS and MISR CTH are -540±690 m and -280±370 m, respectively. The uncertainties are generally smaller for lower altitude clouds and larger for optically thinner clouds. For multi-layered clouds, errors are summarized herein using both absolute CTH and CATS-layer-altitude proximity to Terra CTH. We show that MISR detects the lower cloud in a two-layered system, provided top-layer optical depth < ~0.3, but MISR low-cloud errors are unaltered by the presence of thin cirrus. Systematic and random errors are propagated to explain inter-sensor disagreements, as well as to provide the first estimate of MISR stereo-opacity bias. For MISR, altitude-dependent wind-retrieval bias (-90 to -110 m) and stereo-opacity bias (-110 to -240 m) and for MODIS, bias due to low opacity near cloud-top lead to overall negative CTH bias. MISR’s precision is largely driven by wind-speed uncertainty (3.7 m s-1), whereas MODIS precision is driven by forward-modeling uncertainty.

We present a comparison of the detailed present-day crust to upper-mantle (400km) structure in the Western Mediterranean along two transects in Alboran and Algerian domains. The transect across the Alboran domain is NW-SE oriented crossing the Betics and the Alboran Basin and the northern margin of Africa between the Tell and Rif mountains. The Algerian domain transect is also NW-SE oriented crossing the Valencia Trough, the Balearic Promontory, the Algerian basin and ending in the Tell-Atlas Mountains in the northern margin of Africa. The structure is computed using integrated geophysical-petrological modelling tool (LitMod2D) which combines petrological, geochemical and geophysical dataset in a self-consistent framework. We model thermal, compositional, density and seismological structure, also incorporating slabs imaged by seismic tomography, constrained by simultaneously fitting elevation, gravity, geoid, surface heat flow and seismic tomography models to reduce the uncertainties in the modelling. Preliminary results suggest that crust is thickest beneath the Betics and thins beneath the Alboran basin within a distance of ~100 km. Farther SE, crust gradually thickens beneath the north margin of Africa, between Tell and Rif mountains over ~300 km distance. The LAB shows a similar trend though it is affected by the presence of the slab underneath Betics. For the Algerian domain transect, maximum crustal thickness occurs beneath the Tell-Atlas Mountains with noticeable variations across the Algerian basin, Balearic Promontory and Valencia Trough, the LAB showing a similar tendency. Comparing the modelled geometries suggests that both transects have opposite trends with the deepest Moho and LAB in the NW side of the Alboran transect and in the SE side of the Algerian transect, imposing important restrictions on the geodynamic evolution of the Western Mediterranean.