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The eastern China seas are some of the largest marginal seas in the world, where high primary productivity and phytoplankton blooms are often observed. However, little is known about their systematic variation of phytoplankton blooms on large spatial and long temporal scales due to the difficulty of monitoring bloom events by field measurement. In this study, we investigated the seasonal and interannual variability and long-term changes in phytoplankton blooms in the eastern China seas using a 14 yr (1998–2011) time series of satellite ocean colour data. To ensure a proper satellite dataset to figure out the bloom events, we validated and corrected the satellite-derived chlorophyll concentration (chl a) using extensive in situ datasets from two large cruises. The correlation coefficients between the satellite retrieval data and the in situ chl a on the logarithmic scale were 0.85 and 0.72 for the SeaWiFS and Aqua/MODIS data, respectively. Although satellites generally overestimate the chl a, especially in highly turbid waters, both the in situ and satellite data show that the overestimation of satellite-derived chl a has an upper limit value (10 μg L−1), which can be used as a threshold for the identification of phytoplankton blooms to avoid the false blooms resulting from turbid waters. Taking 10 μg L−1 as the threshold, we present the spatial-temporal variability of phytoplankton blooms in the eastern China seas over the past 14 yr. Most blooms occur in the Changjiang Estuary and along the coasts of Zhejiang, with a maximal frequency of 20% (about 73 days per year). The coasts of the northern Yellow Sea and the Bohai Sea also have high-frequency blooms (up to 20%). The blooms show significant seasonal variation, with most occurring in spring (April–June) and summer (July–September). The study also revealed a doubling in bloom intensity in the Yellow Sea and Bohai Sea during the past 14 yr. The nutrient supply in the eastern China seas might be a major controlling factor in bloom variation. The time series in situ nutrient datasets show that both the nitrate and phosphate concentrations increased more than twofold between 1998 and 2005 in the Yellow Sea. This might be the reason for the doubling of the bloom intensity index in the Yellow Sea and Bohai Sea. In contrast, there has been no significant long-term increase or decrease in the Changjiang Estuary, which might be regulated by the Changjiang River discharge. These results offer a foundation for the study of the influence of phytoplankton blooms on the carbon flux estimation and biogeochemical processes in the eastern China seas.

Data assimilation (DA) is integrated with machine learning in order to perform entirely data‐driven online state estimation. To achieve this, recurrent neural networks (RNNs) are implemented as pretrained surrogate models to replace key components of the DA cycle in numerical weather prediction (NWP), including the conventional numerical forecast model, the forecast error covariance matrix, and the tangent linear and adjoint models. It is shown how these RNNs can be initialized using DA methods to directly update the hidden/reservoir state with observations of the target system. The results indicate that these techniques can be applied to estimate the state of a system for the repeated initialization of short‐term forecasts, even in the absence of a traditional numerical forecast model. Further, it is demonstrated how these integrated RNN‐DA methods can scale to higher dimensions by applying domain localization and parallelization, providing a path for practical applications in NWP. Plain Language Summary Weather forecast models derived from fundamental equations of physics continue to increase in detail and complexity. While this evolution leads to consistently improving daily weather forecasts, it also leads to associated increases in computational costs. In order to make a forecast at any given moment, these models must be initialized with our best guess of the current state of the atmosphere, which typically includes information from a limited set of observations as well as forecasts from the recent past. Modern methods for initializing these computer forecasts typically require running many copies of the model, either simultaneously or in sequence, to compare with observations over the recent past and ensure that our best guess estimate of the current state of the atmosphere agrees closely with those observations before making a new forecast. This repeated execution of the computer forecast model is often a time‐consuming and costly bottleneck in the initialization process. Here, it is shown that techniques from the fields of artificial intelligence and machine learning (AI/ML) can be used to produce simple surrogate models that provide sufficiently accurate approximations to replace the original costly model in the initialization phase. The resulting process is self‐contained, and does not require any further utilization of the original computer model when making daily forecasts. Key Points Recurrent neural networks (RNNs) can replace conventional forecast models, producing accurate ensemble forecast statistics and linearized dynamics Data assimilation (DA) is compatible with RNNs by applying state estimation in the hidden state space using a modified observation operator The integrated RNN‐DA methods can be scaled to higher dimensions by applying domain localization techniques

IntroductionUndergraduate students encounter developmental challenges during their transition into adulthood. Previous studies have claimed that adults with later chronotypes usually manifest negative psychological effects: poor sleep quality, greater stress, depression, and cognitive dysfunction. However, knowledge about the relationship between chronotype, stress, and sleep quality among young adults is lacking.ObjectivesThe present study investigated the relationship between undergraduates’ chronotypes and perceived stress on sleep quality.MethodsAn online survey with a descriptive, cross-sectional design was conducted with a convenience sample of undergraduate students at a university in southern Taiwan. Those who were 20-25 years old and enrolled as a student were included; but who had been suspended or had deferred graduation were excluded. Students’ chronotype, stress, and sleep quality were assessed with three self-reported instruments: Munich Chronotype Questionnaire (MCTQ), Perceived Stress Scale (PSS), and Pittsburgh Sleep Quality Index (PSQI).ResultsOf 161 undergraduates who completed the questionnaires, 51 reported using an alarm clock to wake and were removed from data analysis. One hundred and ten students’ mean age is 20.3 and perceived moderate stress. Sixty-one percent were poor-quality sleepers. The mean chronotype score was 5.7, and 85.5% had an intermediate chronotype, while 13.6% had an evening chronotype. Chronotype and perceived stress were positively correlated with sleep quality (p < .001). Social jetlag was positively correlated with chronotype (p =.036). Undergraduate’s later chronotype and higher stress perception predicted 30% of poorer sleep quality (p < .001).ConclusionsUndergraduate students’ chronotype and perceived stress were positively correlated and acted as predictors of the sleep quality. The findings could help to develop health-promotion interventions for these emerging adults to adjust their daily routines; and reduce their social jetlag, stress levels, and sleep disturbance.Disclosure of InterestNone Declared

In iron-pnictide superconductivity, the interband interaction between the hole and electron Fermi surfaces (FSs) is believed to play an important role. However, KFe 2 As 2 has three zone-centered hole FSs and no electron FS but still exhibits superconductivity. Our ultrahigh-resolution laser angle-resolved photoemission spectroscopy unveils that KFe 2 As 2 is a nodal s-wave superconductor with highly unusual FS-selective multi-gap structure: a nodeless gap on the inner FS, an unconventional gap with \"octet-line nodes\" on the middle FS, and an almost-zero gap on the outer FS. This gap structure may arise from the frustration between competing pairing interactions on the hole FSs causing the eightfold sign reversal. Our results suggest that the A 1g superconducting symmetry is universal in iron-pnictides, in spite of the variety of gap functions.

The origin of superconductivity in the iron pnictides has been attributed to antiferromagnetic spin ordering that occurs in close combination with a structural transition, but there are also proposals that link superconductivity to orbital ordering. We used bulk-sensitive laser angle—resolved photoemission spectroscopy on BaFe₂(As 0.65 P 0.35 )₂ and Ba 0.6 K 0.4 Fe₂As₂ to elucidate the role of orbital degrees of freedom on the electron-pairing mechanism. In strong contrast to previous studies, an orbital-independent superconducting gap magnitude was found for the hole Fermi surfaces. Our result is not expected from the superconductivity associated with spin fluctuations and nesting, but it could be better explained invoking magnetism-induced interorbital pairing, orbital fluctuations, or a combination of orbital and spin fluctuations. Regardless of the interpretation, our results impose severe constraints on theories of iron pnictides.

Quantum phase transitions play an important role in shaping the phase diagram of high-temperature cuprate superconductors. These cuprates possess intertwined orders which interact strongly with superconductivity. However, the evidence for the quantum critical point associated with the charge order in the superconducting phase remains elusive. Here, we reveal the short-range charge orders and the spectral signature of the quantum fluctuations inLa2−xSrxCuO4(LSCO) near the optimal doping using high-resolution resonant inelastic x-ray scattering. On performing calculations through a diagrammatic framework, we discover that the charge correlations significantly soften several branches of phonons. These results elucidate the role of charge order in the LSCO compound, providing evidence for quantum critical scaling and discommensurations associated with charge order.

Electron quasiparticles play a crucial role in simplifying the description of many-body physics in solids with surprising success. Conventional Landau’s Fermi-liquid and quasiparticle theories for high-temperature superconducting cuprates have, however, received skepticism from various angles. A path-breaking framework of electron fractionalization has been established to replace the Fermi-liquid theory for systems that show the fractional quantum Hall effect and the Mott insulating phenomena; whether it captures the essential physics of the pseudogap and superconducting phases of cuprates is still an open issue. Here, we show that excitonic excitation of optimally doped Bi 2 Sr 2 CaCu 2 O 8+ δ with energy far above the superconducting-gap energy scale, about 1 eV or even higher, is unusually enhanced by the onset of superconductivity. Our finding proves the involvement of such high-energy excitons in superconductivity. Therefore, the observed enhancement in the spectral weight of excitons imposes a crucial constraint on theories for the pseudogap and superconducting mechanisms. A simple two-component fermion model which embodies electron fractionalization in the pseudogap state provides a possible mechanism of this enhancement, pointing toward a novel route for understanding the electronic structure of superconducting cuprates. The nature of the excitations in the pseudogap regime and their relation to superconductivity remain core issues in cuprate high- T c superconductivity. Here, using resonant inelastic x-ray scattering, the authors find that high-energy excitons in optimally-doped Bi 2 Sr 2 CaCu 2 O 8+ δ are enhanced by the onset of superconductivity, an effect possibly explained in terms of electron fractionalization.

Conventional superconductivity follows Bardeen-Cooper-Schrieffer(BCS) theory of electrons-pairing in momentum-space, while superfluidity is the Bose-Einstein condensation(BEC) of atoms paired in real-space. These properties of solid metals and ultra-cold gases, respectively, are connected by the BCS-BEC crossover. Here we investigate the band dispersions in FeTe 0.6 Se 0.4 ( T c = 14.5 K ~ 1.2 meV) in an accessible range below and above the Fermi level( E F ) using ultra-high resolution laser angle-resolved photoemission spectroscopy. We uncover an electron band lying just 0.7 meV (~8 K) above E F at the Γ-point, which shows a sharp superconducting coherence peak with gap formation below T c . The estimated superconducting gap Δ and Fermi energy indicate composite superconductivity in an iron-based superconductor, consisting of strong-coupling BEC in the electron band and weak-coupling BCS-like superconductivity in the hole band. The study identifies the possible route to BCS-BEC superconductivity.

The first known magnetic mineral, magnetite, has unusual properties, which have fascinated mankind for centuries; it undergoes the Verwey transition around 120 K with an abrupt change in structure and electrical conductivity. The mechanism of the Verwey transition, however, remains contentious. Here we use resonant inelastic X-ray scattering over a wide temperature range across the Verwey transition to identify and separate out the magnetic excitations derived from nominal Fe 2+ and Fe 3+ states. Comparison of the experimental results with crystal-field multiplet calculations shows that the spin–orbital dd excitons of the Fe 2+ sites arise from a tetragonal Jahn-Teller active polaronic distortion of the Fe 2+ O 6 octahedra. These low-energy excitations, which get weakened for temperatures above 350 K but persist at least up to 550 K, are distinct from optical excitations and are best explained as magnetic polarons. The Verwey transition of magnetite is complex due to the coexistence of strong correlations and electron-phonon coupling. Here, the authors use resonant inelastic X-ray scattering to show evidence for magnetic polarons in magnetite and provide insight into the nature of the transition.

Measurements of integer and half-integer transitions of the quantized magnetic flux through a superconducting niobium–iron pnictide ring provide strong evidence to support predictions that the Cooper pairs within iron-based superconductors show an unconventional ‘reversed s -wave symmetry’. The recent discovery of iron-based superconductors 1 , 2 , 3 challenges the existing paradigm of high-temperature superconductivity. Owing to their unusual multi-orbital band structure 4 , 5 , magnetism 6 and electron correlation 7 , theories propose a unique sign-reversed s -wave pairing state, with the order parameter changing sign between the electron and hole Fermi pockets 8 , 9 , 10 , 11 , 12 , 13 , 14 . However, because of the complex Fermi surface topology and materials-related issues, the predicted sign reversal remains unconfirmed. Here we report a new phase-sensitive technique for probing unconventional pairing symmetry in the polycrystalline iron pnictides. Through the observation of both integer and half-integer flux-quantum transitions in composite niobium–iron pnictide loops, we provide the first phase-sensitive evidence of the sign change of the order parameter in NdFeAsO 0.88 F 0.12 , lending strong support for microscopic models predicting unconventional s -wave pairing symmetry 9 , 10 , 11 , 12 , 13 , 14 . These findings have important implications on the mechanism of iron pnictide superconductivity, and lay the groundwork for future studies of new physics arising from the exotic order in the FeAs-based superconductors.