Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title><jats:p>In 1950 the first successful numerical weather forecast was obtained by solving the barotropic vorticity equation using the Electronic Numerical Integrator and Computer (ENIAC), which marked the beginning of the age of numerical weather prediction. Here, we ask the question of how these numerical forecasts would have turned out, if machine learning based solvers had been used instead of standard numerical discretizations. Specifically, we recreate these numerical forecasts using physics‐informed neural networks. We show that physics‐informed neural networks provide an easier and more accurate methodology for solving meteorological equations on the sphere, as compared to the ENIAC solver.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Aerosol‐cloud interactions (ACI) in warm clouds are the primary source of uncertainty in effective radiative forcing (ERF) during the historical period and, by extension, inferred climate sensitivity. The ERF due to ACI (ERFaci) is composed of the radiative forcing due to changes in cloud microphysics and cloud adjustments to microphysics. Here, we examine the processes that drive ERFaci using a perturbed parameter ensemble (PPE) hosted in CAM6. Observational constraints on the PPE result in substantial constraints in the response of cloud microphysics and macrophysics to anthropogenic aerosol, but only minimal constraint on ERFaci. Examination of cloud and radiation processes in the PPE reveal buffering of ERFaci by the interaction of precipitation efficiency and radiative susceptibility.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Diagnosing the role of internal variability over recent decades is critically important for both model validation and projections of future warming. Recent research suggests that for 1980–2022 internal variability manifested as Global Cooling and Arctic Warming (i‐GCAW), leading to enhanced Arctic Amplification (AA), and suppressed global warming over this period. Here we show that such an i‐GCAW is rare in CMIP6 large ensembles, but simulations that do produce similar i‐GCAW exhibit a unique and robust internally driven global surface air temperature (SAT) trend pattern. This unique SAT trend pattern features enhanced warming in the Barents and Kara Sea and cooling in the Tropical Eastern Pacific and Southern Ocean. Given that these features are imprinted in the observed record over recent decades, this work suggests that internal variability makes a crucial contribution to the discrepancy between observations and model‐simulated forced SAT trend patterns.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Cloud organization impacts the radiative effects and precipitation patterns of the cloud field. Deviating from randomness, clouds exhibit either clustering or a regular grid structure, characterized by the spacing between clouds and the cloud size distribution. The two measures are coupled but do not fully define each other. Here, we present the deviation from randomness of the cloud‐ and void‐chord length distributions as a measure for both factors. We introduce the <jats:italic>LvL</jats:italic> representation and an associated 2D score that allow for unambiguously quantifying departure from well‐defined baseline randomness in cloud spacing and sizes. This approach demonstrates sensitivity and robustness in classifying cloud field organization types. Its delicate sensitivity unravels the temporal evolution of a single cloud field, providing novel insights into the underlying governing processes.</jats:p>

<jats:title>Abstract</jats:title><jats:p>The diurnal variability of mixed layer (ML) overturning instabilities remains poorly understood due to the challenge in capturing their rapid evolutions across large spatiotemporal ranges. Using high‐resolution data from 52 gliders in the South China Sea, we examine the diurnal modulations of ML overturning instabilities. The results of the 3‐month field observation show that negative potential vorticity occupies ∼16% of the ML and facilitates several types of forced overturning instabilities, especially symmetric instability (SI). Surface heat fluxes are identified to primarily modulate the diurnal variability of these overturning cells, where nighttime surface cooling is found to energize SI with an ∼2‐hr phase lag. As a result, over 60% of forced submesoscale overturning cells tend to restratify the ML at night. These findings quantitatively highlight the modulation of diabatic atmospheric forcing in submesoscale restratification, which should be considered in submesoscale parameterizations of ocean and climate models.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Dense and broad‐coverage ocean‐bottom observation networks enable us to obtain near‐fault displacement records associated with an offshore earthquake. However, simple integration of ocean‐bottom strong‐motion acceleration records leads to physically unrealistic displacement records. Here we propose a new method using a Kalman filter to estimate coseismic displacement waveforms using the collocated ocean‐bottom seismometers and pressure gauges. First, we evaluate our method using synthetic records and then apply it to an offshore M<jats:sub>w</jats:sub> 6.0 event that generated a small tsunami. In both the synthetic and real cases, our method successfully estimates reasonable displacement waveforms. Additionally, we show that the computed waveforms improve the results of the finite fault modeling process. In other words, the proposed method will be useful for estimating the details of the rupture mechanism of offshore earthquakes as a complement to onshore observations.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Summertime Greenland blocking (GB) can drive melting of the Greenland ice sheet, which has global implications. A strongly increasing trend in GB in the early twenty‐first century was observed but is missing in climate model simulations. Here, we analyze the temporal evolution of GB in nearly 500 members from the CMIP6 archive. The recent period of increased GB is not present in the members considered. The maximum 10‐year trend in GB in the reanalysis, associated with the recent increase, lies almost outside the distributions of trends for any 10‐year period in the climate models. GB is shown to be partly driven by the sea surface temperatures and/or sea ice concentrations, as well as by anthropogenic aerosols. Further work is required to understand why climate models cannot represent a period of increased GB, and appear to underestimate its decadal variability, and what implications this may have.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Transient creep in olivine aggregates has been studied by stress‐relaxation experiments at pressures of 1.7–3.6 GPa and at temperatures of ≤1020 K in a DIA apparatus. Time‐dependent deformation of olivine at small strains (&lt;0.07) was monitored with an ∼1 s of time resolution using a combination of a high‐flux synchrotron X‐ray and a cadmium telluride imaging detector. The observed deformation was found to follow the Burgers creep function with the transient relaxation time ranging from 50 (±20) to 1,880 (±750) s. We show that the Burgers creep for olivine cannot account for the low viscosities in early post‐seismic deformation reported by geodetic observations (&lt;7 × 10<jats:sup>17</jats:sup> Pa·s). In contrast, the time‐dependent increase in viscosity observed in late post‐seismic deformation (10<jats:sup>18</jats:sup>−10<jats:sup>20</jats:sup> Pa·s) is explained by the Burgers rheology, suggesting that the combination of the Burgers model and another model is needed for the interpretation of post‐seismic deformation.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Recent distributed acoustic sensing (DAS) experiments in ocean areas throughout the world have accumulated records for various wavefields. However, there are few tsunami records because tsunami observation depends on the DAS experimental period and its location. From continuous DAS records, we found tsunami signals at a frequency band of 5–30 mHz, which correspond to high‐frequency components of tsunamis and their propagation velocities differ from low‐frequency tsunamis. We estimated time series of the tsunami excitations at the source using the DAS records, which are consistent with those using records of ocean‐bottom absolute pressure gauges. Our study suggests that DAS records can be used for detecting tsunami propagations in the regions where other geophysical instruments are not available, and contribute to elucidating their excitation mechanisms.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Bedform evolution and preserved cross strata are known to respond to floods. However, it is unclear if autogenic dynamics mask the flood signal in bedform evolution and cross strata. To address this, we characterize the temporal structure of autogenic noise in steady‐state bedform evolution in a physical experiment. Results reveal the existence of bedform groups—quasi‐stable collections of bedforms—that migrate at a similar speed as bedforms. We find that bedform and bedform‐group turnover timescales are the key autogenic timescales of bed evolution that set the transition time‐periods between different noise regimes in bedform evolution. Results suggest that bedform‐group turnover timescale sets the lower limit for detecting flood signals in bedform evolution, and floods with duration shorter than bedform turnover timescale can be severely degraded in bedform evolution and cross strata. Our work provides a new framework for interrogating fluvial cross strata for reconstruction of past floods.</jats:p>