We examine the behavior of natural basaltic and trachytic samples during paleointensity experiments on both the original and laboratory‐acquired thermal remanences and characterize the samples using proxies for domain state including curvature (
Molnar and England (1990,
- Award ID(s):
- 1850634
- NSF-PAR ID:
- 10445559
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geochemistry, Geophysics, Geosystems
- Volume:
- 20
- Issue:
- 7
- ISSN:
- 1525-2027
- Page Range / eLocation ID:
- p. 3268-3288
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
Abstract k ) and the bulk domain stability parameters of Paterson (2011,https://doi.org/10.1029/2011JB008369 ) and Paterson et al. (2017,https://doi.org/10.1073/pnas.1714047114 ), respectively. A curvature value of 0.164 (suggested by Paterson, 2011,https://doi.org/10.1029/2011JB008369 ) as a critical threshold that separates single‐domain‐like remanences from multidomain‐like remanances on the original paleointensity data was used to separate samples into “straight” (single‐domain‐like) and “curved” (multidomain‐like) groups. Specimens from the two sample sets were given a “fresh” thermal remanent magnetization in a 70 μT field and subjected to an infield‐zerofield, zerofield‐infield (IZZI)‐type (Yu et al., 2004,https://doi.org/10.1029/2003GC000630 ) paleointensity experiment. The straight sample set recovered the laboratory field with high precision while the curved set had much more scattered results (70.5 ± 1.5 and 71.9 ± 5.2 μT, respectively). The average intensity of both sets for straight and curved was quite close to the laboratory field of 70 μT, however, suggesting that if experiments contain a sufficient number of specimens, there does not seem to be a large bias in the field estimate. We found that the dependence of the laboratory thermal remanent magnetization on cooling rate was significant in most samples and did not depend on domain states inferred from proxies based on hysteresis measurements and should be estimated for all samples whose cooling rates differ from that used in the laboratory. -
Abstract The objective of this comment is to correct two sets of statements in Litwin et al. (2022,
https://doi.org/10.1029/2021JF006239 ), which consider our research work (Bonetti et al., 2018,https://doi.org/10.1098/rspa.2017.0693 ; Bonetti et al., 2020,https://doi.org/10.1073/pnas.1911817117 ). We clarify here that (a) the specific contributing area is defined in the limit of an infinitesimal contour length instead of the product of a reference contour width (Bonetti et al., 2018,https://doi.org/10.1098/rspa.2017.0693 ), and (b) not all solutions obtained from the minimalist landscape evolution model of Bonetti et al. (2020,https://doi.org/10.1073/pnas.1911817117 ) are rescaled copies of each other. We take this opportunity to demonstrate that the boundary conditions impact the obtained solutions, which has not been considered in the dimensional analysis of Litwin et al. (2022,https://doi.org/10.1029/2021JF006239 ). We clarify this point by using dimensional analysis and numerical simulations for a square domain, where only one horizontal length scale (the side lengthl ) enters the physical law. -
Abstract Carbonic anhydrase (CA) has been shown to promote calcite dissolution (Liu, 2001,
https://doi.org/10.1111/j.1755-6724.2001.tb00531.x ; Subhas et al., 2017,https://doi.org/10.1073/pnas.1703604114 ), and understanding the catalytic mechanism will facilitate our understanding of the oceanic alkalinity cycle. We use atomic force microscopy (AFM) to directly observe calcite dissolution in CA‐bearing solution. CA is found to etch the calcite surface only when in extreme proximity (~1 nm) to the mineral. Subsequently, the CA‐induced etch pits create step edges that serve as active dissolution sites. The possible catalytic mechanism is through the adsorption of CA on the calcite surface, followed by proton transfer from the CA catalytic center to the calcite surface during CO2hydration. This study shows that the accessibility of CA to particulate inorganic carbon (PIC) in the ocean is critical in properly estimating oceanic CaCO3and alkalinity cycles. -
Abstract The mechanical, physical, and frictional properties of incoming materials play an important role in subduction zone structure and slip behavior because these properties influence the strength of the accretionary wedge and megathrust plate boundary faults. Incoming sediment sections often show an increase in compressional wave speed (Vp) and a decrease in porosity with depth due to consolidation. These relations allow seismic‐velocity models to be used to elucidate properties and conditions at depth. However, variations in these properties are controlled by lithology and composition as well as cementation and diagenesis. We present an analysis of shipboard measurements of Vpand porosity on incoming sediment cores from International Ocean Discovery Program (IODP) expeditions at the Hikurangi Margin, Nankai Trough, Aleutian Trench, Middle America Trench, and Sunda Trench. Porosity for these samples ranges from 5% to 85% and Vpranges from 1.5 to 6 km/s. Vp‐porosity relations developed by Erikson & Jarrad (1998),
https://doi.org/10.1029/98JB02128 and Hoffman & Tobin (2004)https://10.2973/odp.proc.sr.190196.355.2004 , with a critical porosity of ∼30%, can represent carbonate‐poor (<50 wt% CaCO3), mainly hemipelagic, incoming sediment regardless of the margin. But these relations tend to underestimate porosity in incoming sediments with carbonate content greater than 50 wt%, which appear to have a critical porosity of between 45% and 50%. This discrepancy will lead to inaccuracy in estimates of fluid budget and overpressure in subduction zones. The velocity‐porosity relation in carbonate sediments is non‐unique due to the complexity that results from the greater susceptibility of carbonate rocks to diagenetic processes. -
Abstract We present empirical conductance relations that are derived from incoherent scatter radar observations and correlated with all sky imager observations to identify the morphology of the aurora. We use 75,461 events collected using the Poker Flat Incoherent Scatter Radar (PFISR) with associated all sky imagers observations spanning the years 2012–2016. In addition to classifying these events based on auroral morphology, we estimated the Hall and Pedersen conductance and the differential number flux from which the energy flux and the average energy can be calculated. The differential number flux was estimated using the maximum entropy inversion method described in Semeter and Kamalabadi (2005,
https://doi.org/10.1029/2004RS003042 ), but now incorporating the Fang et al. (2010,https://doi.org/10.1029/2010GL045406 ) ionization model. The main results of this investigation are the power law equations that describe the median, 90th, and 10th percentile Hall and Pedersen conductance as a function of energy flux and average energy. These power law fits are performed for different auroral morphology including all events, discrete, diffuse, and pulsating auroral events. The median Pedersen conductance is found to be in good agreement with past empirical conductance specifications by Robinson et al. (1987,https://doi.org/10.1029/JA092iA03p02565 ); however, the median Hall conductance from the PFISR observations is found to be larger than the empirical Hall conductance formulas by Robinson et al. (1987,https://doi.org/10.1029/JA092iA03p02565 ). Pulsating aurora is found to be the most frequently occurring auroral morphology. Furthermore, pulsating aurora has an important contribution to Hall conductance since it has higher average energies than discrete aurora. The results from this investigation are applicable to space weather models and may enable better agreement between model‐data comparisons.