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In November 2016, magnetotelluric (MT) data were collected at the Ceboruco Volcano in cooperation with the Centro de Sismología y Volcanología de Occidente (SisVoc, Universidad de Guadalajara, Mexico). The Ceboruco is a 2280 m high stratovolcano, located in Nayarit State, Mexico. It is placed in the central part of the Tepic-Zacoalco Rift (TZR), which constitutes the north-western end of the Trans-Mexican Volcanic Belt. Together with Chapala and Colima (in the Jalisco Block), they form the triple rift system developed as a consequence of the ongoing subduction of the Rivera and Cocos oceanic plates beneath the North American continental crust. Although its last eruption occurred in 1870, it is the most active volcano in the area, showing volcanic-earthquake activity together with ongoing vapor emissions. The survey was part of a geothermal project (CeMIEGeo-P24) and focused on the determination of electrical conductivity properties to characterize the deep structure and the geothermal potential of the Volcano. Frequency dependent magnetotelluric response functions were calculated from 25 broadband MT stations, which covered an area of 10 x 10 km2 including its crater, calderas and foreland. The results were interpreted using anisotropic 3-D forward modelling and isotropic 3-D inversion approaches, considering strong topographical effects. The final resistivity model implies a highly conductive layer, reaching from near-surface to approximately 2 km depth, which might be related to a hydrothermal system. Here, mineralized fluids and clay minerals can cause high conductivities around 1 S/m. For longer periods, the principal axes of the MT response tensors (phase tensor, apparent resistivity tensor) are in good agreement with the strike direction of the underlying rift system. However, they are not rendered by the isotropic inversion. Thus the data suggest an anisotropic electrical conductivity at greater depth with its principal axis determined by the response tensors.
Deriving stratospheric age of air spectra using an idealized set of chemically active trace gases
(2019)
Analysis of stratospheric transport from an observational point of view is frequently realized by evaluation of the mean age of air values from long-lived trace gases. However, this provides more insight into general transport strength and less into its mechanism. Deriving complete transit time distributions (age spectra) is desirable, but their deduction from direct measurements is difficult. It is so far primarily based on model work. This paper introduces a modified version of an inverse method to infer age spectra from mixing ratios of short-lived trace gases and investigates its basic principle in an idealized model simulation. For a full description of transport seasonality the method includes an imposed seasonal cycle to gain multimodal spectra. An ECHAM/MESSy Atmospheric Chemistry (EMAC) model simulation is utilized for a general proof of concept of the method and features an idealized dataset of 40 radioactive trace gases with different chemical lifetimes as well as 40 chemically inert pulsed trace gases to calculate pulse age spectra. It is assessed whether the modified inverse method in combination with the seasonal cycle can provide matching age spectra when chemistry is well-known. Annual and seasonal mean inverse spectra are compared to pulse spectra including first and second moments as well as the ratio between them to assess the performance on these timescales. Results indicate that the modified inverse age spectra match the annual and seasonal pulse age spectra well on global scale beyond 1.5 years of mean age of air. The imposed seasonal cycle emerges as a reliable tool to include transport seasonality in the age spectra. Below 1.5 years of mean age of air, tropospheric influence intensifies and breaks the assumption of single entry through the tropical tropopause, leading to inaccurate spectra, in particular in the Northern Hemisphere. The imposed seasonal cycle wrongly prescribes seasonal entry in this lower region and does not lead to a better agreement between inverse and pulse age spectra without further improvement. Tests with a focus on future application to observational data imply that subsets of trace gases with 5 to 10 species are sufficient for deriving well-matching age spectra. These subsets can also compensate for an average uncertainty of up to ±20 % in the knowledge of chemical lifetime if a deviation of circa ±10 % in modal age and amplitude of the resulting spectra is tolerated.
An accelerating Brewer-Dobson circulation (BDC) is a robust signal of climate change in model predictions but has been questioned by trace gas observations. We analyze stratospheric mean age of air and the full age spectrum as measures for the BDC and its trend. Age of air is calculated with the Chemical Lagrangian Model of the Stratosphere (CLaMS) driven by ERA-Interim, JRA-55 and MERRA-2 reanalysis data to assess the robustness of the representation of the BDC in current generation meteorological reanalyses. We find that climatological mean age significantly depends on the reanalysis, with JRA-55 showing the youngest and MERRA-2 the oldest mean age. Consideration of the age spectrum indicates that the older age for MERRA-2 is related to a stronger spectrum tail, likely related to weaker tropical upwelling and stronger recirculation. Seasonality of stratospheric transport is robustly represented in reanalyses, with similar mean age variations and age spectrum peaks. Long-term changes over 1989–2015 turn out to be similar for the reanalyses with mainly decreasing mean age accompanied by a shift of the age spectrum peak towards shorter transit times, resembling the forced response in climate model simulations to increasing greenhouse gas concentrations. For the shorter periods 1989–2001 and 2002–2015 age of air changes are less robust. Only ERA-Interim shows the hemispheric dipole pattern in age changes during 2002–2015 as viewed by recent satellite observations. Consequently, the representation of decadal variability of the BDC in current generation reanalyses appears less robust and a major uncertainty of modelling the BDC.
The endemic argan tree (Argania spinosa) populations in South Morocco are highly degraded due to their use as a biomass resource in dry years and illegal firewood extraction. The intensification and expansion of agricultural land lead to a retreat of the wooded area, while the remaining argan open woodlands are often overgrazed. Thus, canopy-covered areas decrease while areas without vegetation cover between the argan trees increase. In total, 36 rainfall simulation experiments as well as 60 infiltration measurements were conducted to investigate the potential difference between tree-covered areas and bare intertree areas. In addition, 60 soil samples were taken under the trees and in the intertree areas parallel to the contour lines. Significant differences using a t-test were found between tree and intertree areas for the studied parameters Ksat, Kh, pH, electric conductivity, percolation stability, total C-content, total N-content, K-content, Na-content, and Mg-content. Surface runoff and soil losses were not as conclusive but showed similar trends. The results showed that argan trees influence the soil underneath significantly, while the soil in intertree areas is less protected and more degraded. It is therefore reasonable to assume further degradation of the soil when intertree areas extend further due to lack of rejuvenation of argan trees.
In global hydrological models, groundwater storages and flows are generally simulated by linear reservoir models. Recently, the first global gradient-based groundwater models were developed in order to improve the representation of groundwater-surface water interactions, capillary rise, lateral flows and human water use impacts. However, the reliability of model outputs is limited by a lack of data as well as model assumptions required due to the necessarily coarse spatial resolution. The impact of data quality is presented by showing the sensitivity of a groundwater model to changes in the only available global hydraulic conductivity data-set. To better understand the sensitivity of model output to uncertain spatially distributed parameter inputs, we present the first application of a global sensitivity method for a global-scale groundwater model using nearly 2000 steady-state model runs of the global gradient-based groundwater model G3M. By applying the Morris method in a novel domain decomposition approach that identifies global hydrological response units, spatially distributed parameter sensitivities are determined for a computationally expensive model. Results indicate that globally simulated hydraulic heads are equally sensitive to hydraulic conductivity, groundwater recharge and surface water body elevation, though parameter sensitivities vary regionally. For large areas of the globe, rivers are simulated to be either losing or gaining, depending on the parameter combination, indicating a high uncertainty of simulating the direction of flow between the two compartments. Mountainous and dry regions show a high variance in simulated head due to numerical difficulties of the model, limiting the reliability of computed sensitivities in these regions. This instability is likely caused by the uncertainty in surface water body elevation. We conclude that maps of spatially distributed sensitivities can help to understand complex behaviour of models that incorporate data with varying spatial uncertainties. The findings support the selection of possible calibration parameters and help to anticipate challenges for a transient coupling of the model.
Understanding new particle formation and growth is important because of the strong impact of these processes on climate and air quality. Measurements to elucidate the main new particle formation mechanisms are essential; however, these mechanisms have to be implemented in models to estimate their impact on the regional and global scale. Parameterizations are computationally cheap ways of implementing nucleation schemes in models, but they have their limitations, as they do not necessarily include all relevant parameters. Process models using sophisticated nucleation schemes can be useful for the generation of look-up tables in large-scale models or for the analysis of individual new particle formation events. In addition, some other important properties can be derived from a process model that implicitly calculates the evolution of the full aerosol size distribution, e.g., the particle growth rates. Within this study, a model (SANTIAGO – Sulfuric acid Ammonia NucleaTIon And GrOwth model) is constructed that simulates new particle formation starting from the monomer of sulfuric acid up to a particle size of several hundred nanometers. The smallest sulfuric acid clusters containing one to four acid molecules and a varying amount of base (ammonia) are allowed to evaporate in the model, whereas growth beyond the pentamer (five sulfuric acid molecules) is assumed to be entirely collision-controlled. The main goal of the present study is to derive appropriate thermodynamic data needed to calculate the cluster evaporation rates as a function of temperature. These data are derived numerically from CLOUD (Cosmics Leaving OUtdoor Droplets) chamber new particle formation rates for neutral sulfuric acid–water–ammonia nucleation at temperatures between 208 and 292 K. The numeric methods include an optimization scheme to derive the best estimates for the thermodynamic data (dH and dS) and a Monte Carlo method to derive their probability density functions. The derived data are compared to literature values. Using different data sets for dH and dS in SANTIAGO detailed comparison between model results and measured CLOUD new particle formation rates is discussed.
Understanding new particle formation and growth is important because of the strong impact of these processes on climate and air quality. Measurements to elucidate the main new particle formation mechanisms are essential; however, these mechanisms have to be implemented in models to estimate their impact on the regional and global scale. Parameterizations are computationally cheap ways of implementing nucleation schemes in models but they have their limitations, as they do not necessarily include all relevant parameters. Process models using sophisticated nucleation schemes can be useful for the generation of look-up tables in large scale models or for the analysis of individual new particle formation events. In addition, some other important properties can be derived from a process model that implicitly calculates the evolution of the full aerosol size distribution, e.g., the particle growth rates. Within this study, a model (SANTIAGO, Sulfuric acid Ammonia NucleaTIon And GrOwth model) is constructed that simulates new particle formation starting from the monomer of sulfuric acid up to a particle size of several hundred nanometers. The smallest sulfuric acid clusters containing one to four acid molecules and varying amount of base (ammonia) are allowed to evaporate in the model, whereas growth beyond the pentamer (5 sulfuric acid molecules) is assumed to be entirely collision-controlled. The main goal of the present study is to derive appropriate thermodynamic data needed to calculate the cluster evaporation rates as a function of temperature. These data are derived numerically from CLOUD (Cosmics Leaving OUtdoor Droplets) chamber new particle formation rates for neutral sulfuric acid-water-ammonia nucleation at temperatures between 208 K and 292 K. The numeric methods include an optimization scheme to derive the best estimates for the thermodynamic data (dH and dS) and a Monte Carlo method to derive their probability density functions. The derived data are compared to literature values. Using different data sets for dH and dS in SANTIAGO detailed comparison between model results and measured CLOUD new particle formation rates is discussed.
The complex magnetotelluric (MT) apparent resistivity tensor can be decomposed into two real tensors, the apparent resistivity and the resistivity phase tensors, which represent relationships between the observed electric field at a point on the Earth's surface and an associated apparent current density. We explain the differences between these tensors and conventional estimates of apparent resistivity and phase for simple resistivity environments and demonstrate, using canonical models in 1‐D and 2‐D environments, that both tensors are more sensitive to vertical and horizontal resistivity gradients than their conventional counterparts. The properties of the new tensors are explained using electromagnetic induction theory and the effects of associated charges at resistivity boundaries. We introduce a new way to plot tensor ellipses, which brings significant improvements to the interpretation of MT data, using appropriate visualization software. The apparent resistivity tensor gives information about the magnitude and direction of apparent resistivity subsurface structures and has a strong response to vertical resistivity contrasts. The resistivity phase tensor is highly sensitive to vertical boundaries and the associated fields in the TM mode. It is also free from static distortions under the same conditions implied for the conventional phase tensor. These findings have prompted a study in the potential of the new tensors for 3‐D inversions. The results from a 3‐D inversion of a canonical oblique conductor straddling two quarter spaces show distinct improvements in resolving the boundaries of the conductor and open a promising field for future studies.
Tetra-auricupride, ideally AuCu, represents the only species showing the coexistence of Au with an elevated level of Pt, as in the case of a detrital grain studied structurally for the first time, from an ophiolite-associated placer at Bolshoy Khailyk, western Sayans, Russia. We infer that tetra-auricupride can incorporate as much as ~30 mol. % of a “PtCu” component, apparently without significant modification of the unit cell. The unit-cell parameters of platiniferous tetra-auricupride are: a 2.790(1) Å, c 3.641(4) Å, with c/a = 1.305, which are close to those reported for ordered AuCu(I) in the system Au–Cu, and close also to the cell parameters of tetraferroplatinum (PtFe), which both appear to crystallize in the same space group, P4/mmm. These intermetallic compounds and natural alloys are thus isostructural. The closeness of their structures presumably allows Pt to replace Au atoms so readily. The high extent of Cu + Au enrichment is considered to be a reflection of geochemical evolution and buildup in levels of the incompatible Cu and Au with subordinate Pt in a remaining volume of melt at low levels of fO2 and fS2 in the system.