Abstract:The evolution of Earth's deep interior since core formation (Nimmo, 2015) >4 billion years ago (Ga) remains a topic of considerable study. Obtaining information of the deep interior is generally restricted to present-day observations. Alternatively, insights on processes occurring before the modern era require sampling geologic materials that formed at, or were transported to, Earth's surface. However, the geomagnetic field is generated in the liquid fraction of Earth's core through the geodynamo, and changes … Show more
“…The green shaded area indicates the uncertainty of the extrapolated RPI. The violet line is median dipole moment from time‐varying model (MCADAM.1b) of dipole strength from PINT v8.1.1 data (Bono, Paterson, & Biggin, 2022).…”
Section: Discussionmentioning
confidence: 99%
“…The extrapolated RPI curve for the entire CNS shows strong mean field strength in the middle and weak mean field strength at the beginning and the ending of CNS (Figure 9c). In comparison to the time‐averaged dipole strength variations (MCADAM.1b) based on the absolute paleointensity (API) data during CNS (PINT v8.1.1, Bono, Paterson, van der Boon, et al., 2022; Bono, Paterson, & Biggin, 2022) (Figure 9c), the two data sets show by and large similar variations at the beginning and ending of CNS, and the major discrepancy occurs in the middle CNS where the extrapolated RPI curve (green) shows a strong field while the dipole strength curve (violet) indicates a weak field (Figure 9c). The discrepancy might be due to the relatively limited availability of the API data (Figure 9c) upon which the dipole strength curve was constructed for the middle CNS, suggesting a need to acquire more API data for the middle CNS in the future.…”
Changes in Earth's magnetic field during the Cretaceous Normal Superchron (CNS) spanning ∼121 Ma to ∼84 Ma hold important clues about the geodynamo evolution. Canonical models predict a persistently strong geomagnetic field with low variability during CNS, which, however, has not been observed in the available absolute paleointensity data and seafloor marine magnetic anomaly (MMA) records. The lack of relative paleointensity (RPI) data across CNS further impedes tests of model predictions. Here, we present a ∼9‐Myr (∼94–∼85 Ma) RPI record from a Turonian to Santonian hemipelagic succession from IODP Site U1512 offshore southern Australia. Detailed paleomagnetic and rock magnetic analyses demonstrate that the ratio of natural remanent magnetization (NRM) demagnetized at 20 mT over magnetic susceptibility (MS), that is, NRM20mT/MS, as a reliable proxy for the RPI of the Upper Cretaceous succession. The new RPI record shows marked changes in both intensity and variability at ∼90.8 Ma. Also, the 6 Myr‐long (∼94–∼88 Ma), near‐continuous, ∼1.2 kyr‐resolution RPI record exhibits a strong positive correlation between field intensity and variability. Assuming this correlation holds for the entire CNS, an extrapolated RPI curve for the entire CNS is obtained by integrating the positive correlation with field variability estimates from the MMA data. The extrapolated RPI curve shows a strong and highly variable field in the middle CNS but a weak and stable field at its beginning and ending. These features imply a much more dynamic geodynamo than previously thought, and provide crucial benchmarks for unraveling the geodynamo evolution during CNS.
“…The green shaded area indicates the uncertainty of the extrapolated RPI. The violet line is median dipole moment from time‐varying model (MCADAM.1b) of dipole strength from PINT v8.1.1 data (Bono, Paterson, & Biggin, 2022).…”
Section: Discussionmentioning
confidence: 99%
“…The extrapolated RPI curve for the entire CNS shows strong mean field strength in the middle and weak mean field strength at the beginning and the ending of CNS (Figure 9c). In comparison to the time‐averaged dipole strength variations (MCADAM.1b) based on the absolute paleointensity (API) data during CNS (PINT v8.1.1, Bono, Paterson, van der Boon, et al., 2022; Bono, Paterson, & Biggin, 2022) (Figure 9c), the two data sets show by and large similar variations at the beginning and ending of CNS, and the major discrepancy occurs in the middle CNS where the extrapolated RPI curve (green) shows a strong field while the dipole strength curve (violet) indicates a weak field (Figure 9c). The discrepancy might be due to the relatively limited availability of the API data (Figure 9c) upon which the dipole strength curve was constructed for the middle CNS, suggesting a need to acquire more API data for the middle CNS in the future.…”
Changes in Earth's magnetic field during the Cretaceous Normal Superchron (CNS) spanning ∼121 Ma to ∼84 Ma hold important clues about the geodynamo evolution. Canonical models predict a persistently strong geomagnetic field with low variability during CNS, which, however, has not been observed in the available absolute paleointensity data and seafloor marine magnetic anomaly (MMA) records. The lack of relative paleointensity (RPI) data across CNS further impedes tests of model predictions. Here, we present a ∼9‐Myr (∼94–∼85 Ma) RPI record from a Turonian to Santonian hemipelagic succession from IODP Site U1512 offshore southern Australia. Detailed paleomagnetic and rock magnetic analyses demonstrate that the ratio of natural remanent magnetization (NRM) demagnetized at 20 mT over magnetic susceptibility (MS), that is, NRM20mT/MS, as a reliable proxy for the RPI of the Upper Cretaceous succession. The new RPI record shows marked changes in both intensity and variability at ∼90.8 Ma. Also, the 6 Myr‐long (∼94–∼88 Ma), near‐continuous, ∼1.2 kyr‐resolution RPI record exhibits a strong positive correlation between field intensity and variability. Assuming this correlation holds for the entire CNS, an extrapolated RPI curve for the entire CNS is obtained by integrating the positive correlation with field variability estimates from the MMA data. The extrapolated RPI curve shows a strong and highly variable field in the middle CNS but a weak and stable field at its beginning and ending. These features imply a much more dynamic geodynamo than previously thought, and provide crucial benchmarks for unraveling the geodynamo evolution during CNS.
“…A similar approach using curve fitting to calculate time‐averaged paleointensities was recently used in Bono et al. (2022).…”
Section: Methodsmentioning
confidence: 99%
“…We discuss the rationale for using this approach in Section 4.3. A similar approach using curve fitting to calculate time-averaged paleointensities was recently used in Bono et al (2022).…”
A foundational assumption in paleomagnetism is that the Earth's magnetic field behaves as a geocentric axial dipole (GAD) when averaged over sufficient timescales. Compilations of directional data averaged over the past 5 Ma yield a distribution largely compatible with GAD, but the distribution of paleointensity data over this timescale is incompatible. Reasons for the failure of GAD include: (a) Arbitrary “selection criteria” to eliminate “unreliable” data vary among studies, so the paleointensity database may include biased results. (b) The age distribution of existing paleointensity data varies with latitude, so different latitudinal averages represent different time periods. (c) The time‐averaged field could be truly non‐dipolar. Here, we present a consistent methodology for analyzing paleointensity results and comparing time‐averaged paleointensities from different studies. We apply it to data from Plio/Pleistocene Hawai'ian igneous rocks, sampled from fine‐grained, quickly cooled material (lava flow tops, dike margins and scoria cones) and subjected to the IZZI‐Thellier technique; the data were analyzed using the Bias Corrected Estimation of Paleointensity method of Cych et al. (2021, https://doi.org/10.1029/2021GC009755), which produces accurate paleointensity estimates without arbitrarily excluding specimens from the analysis. We constructed a paleointensity curve for Hawai'i over the Plio/Pleistocene using the method of Livermore et al. (2018, https://doi.org/10.1093/gji/ggy383), which accounts for the age distribution of data. We demonstrate that even with the large uncertainties associated with obtaining a mean field from temporally sparse data, our average paleointensities obtained from Hawai'i and Antarctica (reanalyzed from Asefaw et al., 2021, https://doi.org/10.1029/2020JB020834) are not GAD‐like from 0 to 1.5 Ma but may be prior to that.
“…However, it becomes statistically significant (with R values comparable to those obtained for the 0.773 to 130 Ma interval) when strict filters on both intensities and directions are applied, with the caveat that the number of determinations is lower than 50 and P is close to its critical value ( Table 1 ). For the sake of comparison, we also reproduced the analysis with the PINT v8.1.1 database ( 17 , 18 ), filtered using our selection criteria or its own qualitative selection criteria [QPI ( 19 ); SI Appendix , Table S3 ] and noting that the PINT database starts at 50 kyr. We obtained robust correlations for the 0.773 to 130 Ma interval, noting that the highest values of R, albeit lower than with our selection criteria, were reached for the simultaneous fulfilment of QAGE (reliable age), QSTAT (five or more determinations per cooling unit with a relative SD lower or equal to 25%) and QDIR (well-defined paleomagnetic directions).…”
Section: Correlation In the Paleomagnetic Recordmentioning
Recovering the geomagnetic field strength in the past is key to understanding deep Earth dynamics and detecting potential geodynamo regimes throughout the history of Earth. To better constrain the predictive power of the paleomagnetic record, we propose an approach based on the analysis of the dependency between geomagnetic field strength and inclination (angle made by the horizontal with the field lines). Based on the outcomes of statistical field models, we show that these two quantities should correlate for a wide range of Earth-like magnetic fields, even with enhanced secular variation, persistent nonzonal components, and severe noise contamination. Focusing on the paleomagnetic record, we show that the correlation is not significant for the Brunhes polarity chron, what we ascribe to inadequate spatiotemporal sampling. In contrast, the correlation is significant for the 1 to 130 Ma interval, whereas it only marginally succeeds prior to 130 Ma when strict filters on both paleointensities and paleodirections are applied. As we cannot detect significant variations in the strength of the correlation over the 1 to 130 Ma interval, we conclude that the Cretaceous Normal Superchron may not be associated with enhanced dipolarity of the geodynamo. The strong correlation obtained prior to 130 Ma when strict filters are applied indicates that the ancient field may not be on average so different from the present-day field. If long-term fluctuations nevertheless existed, detecting potential geodynamo regimes during the Precambrian is currently impeded by the sparsity of high-quality data passing strict filters in both paleointensities and paleodirections.
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