Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title><jats:p>The electron butterfly distribution, characterized by pitch angles (PA) primarily at 45° and 135°, was rarely observed in Earth's magnetotail. Here using the high‐resolution measurements from Magnetospheric Multiscale mission, we present the observation of electron butterfly distribution in a contracting dipolarization front (DF), and propose a new physical mechanism to explain its formation. Specifically, we discover that the electron butterfly distribution only exhibited in the locally contracted DF and was observed above 1.7 keV. We infer that local contraction of the DF transformed its configuration from a magnetic bottle to an hourglass‐shaped magnetic structure, and the butterfly distribution was formed by the magnetic mirror effect of this magnetic hourglass. Additionally, the theoretically estimated loss cone of the magnetic hourglass fits well with the observations of electrons, validating our inference about the formation mechanism. These findings can improve our understanding of electron dynamics in Earth's magnetosphere.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Antarctic sea ice concentration (SIC) prediction at seasonal scale has been documented, but a gap remains at subseasonal scale (1–8 weeks) due to limited understanding of ice‐related physical mechanisms. To overcome this limitation, we developed a deep learning model named Sea Ice Prediction Network (SIPNet) that can predict SIC without the need to account for complex physical processes. Compared to mainstream dynamical models like European Centre for Medium‐Range Weather Forecasts, National Centers for Environmental Prediction, and Seamless System for Prediction and Earth System Research developed at Geophysical Fluid Dynamics Laboratory, as well as a relatively advanced statistical model like the linear Markov model, SIPNet outperforms them all, effectively filling the gap in subseasonal Antarctic SIC prediction capability. SIPNet results indicate that autumn SIC variability contributes the most to sea ice predictability, whereas spring contributes the least. In addition, the Weddell Sea displays the highest sea ice predictability, while predictability is low in the West Pacific. SIPNet can also capture the signal of ENSO and SAM on sea ice.</jats:p>

<jats:title>Abstract</jats:title><jats:p>An unprecedented heatwave impacted East Antarctica in March 2022, peaking at 39°C above climatology, the largest temperature anomaly ever recorded globally. We investigate the causes of the heatwave, the impact of climate change, and a climate model's ability in simulating such an event. The heatwave, which was skillfully forecast, resulted from a highly anomalous large‐scale circulation pattern that advected an Australian airmass to East Antarctica in 4 days and produced record atmospheric heat fluxes. Southern Ocean sea surface temperatures anomalies had a minimal impact on the heatwave's amplitude. Simulations from a climate model fail to simulate such a large temperature anomaly mostly due to biases in its large‐scale circulation variability, showcasing a pathway for future model improvement in simulating extreme heatwaves. The heatwave was made 2°C warmer by climate change, and end of 21st century heatwaves may be an additional 5–6°C warmer, raising the prospect of near‐melting temperatures over the interior of East Antarctica.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Understanding climate change impacts on Tropical Storm (TS) activity is crucial for effective adaptation planning and risk assessment, particularly in densely populated low‐lying delta rivers basins like the Ganges and Mekong. The change to TS characteristics with warming is uncertain due to limitations in global climate model resolution and process‐representation and storm tracking algorithms (trackers). Here, we used 13 HighResMIP models and two trackers to estimate the uncertainty in projections of TS characteristics. We found different trackers producing qualitatively similar but quantitatively different results. Our results show a decline (median ∼52%) in the frequency of TS but increase in the strongest TS and Available Cyclone Energy (ACE) of TS over both basins. The higher‐resolution models extract TS with much higher intensity and ACE values compared to the lower‐resolution models. These results have implications for adaptation planning and risk assessment for TS and suggest the need for further high‐resolution modeling studies.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Seismology is witnessing explosive growth in the diversity and scale of earthquake catalogs. A key motivation for this community effort is that more data should translate into better earthquake forecasts. Such improvements are yet to be seen. Here, we introduce the Recurrent Earthquake foreCAST (RECAST), a deep‐learning model based on recent developments in neural temporal point processes. The model enables access to a greater volume and diversity of earthquake observations, overcoming the theoretical and computational limitations of traditional approaches. We benchmark against a temporal Epidemic Type Aftershock Sequence model. Tests on synthetic data suggest that with a modest‐sized data set, RECAST accurately models earthquake‐like point processes directly from cataloged data. Tests on earthquake catalogs in Southern California indicate improved fit and forecast accuracy compared to our benchmark when the training set is sufficiently long (&gt;10<jats:sup>4</jats:sup> events). The basic components in RECAST add flexibility and scalability for earthquake forecasting without sacrificing performance.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Sea surface temperatures (SSTs) vary not only due to heat exchange across the air‐sea interface but also due to changes in effective heat capacity as primarily determined by mixed layer depth (MLD). Here, we investigate seasonal and regional characteristics of the contribution of MLD anomalies to the month‐to‐month variability of SST using observational datasets. First, we propose a metric called Flux Divergence Angle, which can quantify the relative contributions of surface heat fluxes and MLD anomalies to SST variability. Using this metric, we find that MLD anomalies tend to amplify SST anomalies in the extra‐tropics, especially in the eastern ocean basins, during spring and summer. In contrast, MLD anomalies tend to suppress SST anomalies in the eastern tropical Pacific during December‐January‐February. This paper provides the first global picture of the observed importance of MLD anomalies to the local SST variability.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Magnetism, redness, and Fe oxides are indicators of pedoclimatic conditions. However, uncertainties with observing how Fe oxides form within soils has led to debates about relationships between magnetic mineral assemblages, temperature, and rainfall. To address these issues, Fe oxides from the equatorial tropics of Kenya were examined in Pliocene soils that developed under orbital forcing of the monsoon. Results demonstrate that with warm‐wet monsoons, ferrimagnetic production was increased and correlated with hematite concentrations, in accordance with expectations that ferrimagnetic and hematite minerals codevelop from amorphous Fe oxides. With cool‐dry monsoons, hematite concentrations increased but ferrimagnetic production decreased and decoupled from hematite development. These findings suggest that decreased rainfall rather than temperature change favored the dehydration step required to catalyze hematite enrichment within soils. This study explains Fe oxides origins under variable monsoonal climates and recognizes moisture changes in comparison to temperature as stronger controls on the production of soil‐formed hematite.</jats:p>

<jats:title>Abstract</jats:title><jats:p>The South China Sea (SCS) owns the world's strongest quasi‐biweekly oscillation (QBWO) in boreal summer, but the mechanism is still unclear. This case study summarizes two modes of QBWO over the summer SCS in 2019 by using empirical orthogonal function on the 10–20‐day bandpass‐filtered outgoing longwave radiation fields. The maximum positive irradiance anomalies for the two modes are 90 W m<jats:sup>−2</jats:sup>. The upward solar and downward longwave radiation anomalies own about 4%–8% of the irradiance magnitude, and the surface upward longwave radiation shows a weak response. Sea surface turbulent heat fluxes' responses to QBWO display different spatial patterns compared to radiation fluxes. Their changes are mainly ascribed to the surface wind in Mode1 and the air‐sea thermal contrast in Mode2. We also discuss the cause and impact of sea surface turbulent heat fluxes on QBWO.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Sudden stratospheric warming (SSW) events can form a window of forecast opportunity for polar vortex predictions on subseasonal‐to‐seasonal time scales. Analyzing numerical ensemble simulations, we quantify the associated enhanced predictability due to reduced upward planetary wave fluxes during the mostly radiatively driven recovery phase following SSWs. Ensembles that predict an SSW show reduced ensemble spread in terms of polar vortex strength for several weeks to follow, as well as a corresponding reduction in forecast errors. This increased predictability is particularly pronounced for strong SSWs and even occurs if not all ensemble members predict a major SSW. Furthermore, we found a direct impact of the occurrence of SSWs on the date of the final warming (FW): the decrease in upward wave fluxes delays the FW significantly. The reduced spread after SSWs and the delay in FW date have potentially further implications for (subseasonal) predictions of the tropospheric and mesospheric circulations.</jats:p>

<jats:title>Abstract</jats:title><jats:p>A diffuse magmatic province covering central‐eastern Asia continent displays a compositional transition at 120–100 Ma and probably reflects melting initiation in isotopically enriched lithospheric mantle, followed by melting of the asthenosphere. However, the cause for the transition across such a vast landmass remains poorly constrained. Here, analyses of newly found Chaoge basalts (∼95 Ma, central Asia) and compiled data from across the basaltic province are combined to reveal the factors controlling the basalt dichotomy. The Chaoge basalts are considered to originate from a hot pyroxenite‐bearing asthenosphere with potential temperatures of ∼1,450°C, overlapping the source thermochemical conditions for most post‐transition basaltic rocks. The asthenosphere in 120–100 Ma is suggested to be hotter and to have controlled the compositional transition in the studied basaltic province. We suggest that asthenospheric warming resulted from prolonged continental thermal blanketing and can account for other diffuse igneous provinces with similar compositional variations and tectonic histories.</jats:p>