Miracle-dz/UniFAR-FTUs huggingface dataset:
The ﬁrst 250 years of the Heinrich 11 iceberg discharge: Last Interglacial HadGEM3-GC3.1 simulations for CMIP6-PMIP4

The Heinrich 11 event is simulated using the HadGEM3 model during the Last Interglacial period. We apply 0.2 Sv of meltwater forcing across the North Atlantic during a 250 years long simulation. We ﬁnd that the strength of the Atlantic Meridional Overturning Circulation is reduced by 60% after 150 years of meltwater forcing, with an associated decrease of 0.2 to 0.4 PW in meridional ocean heat transport at all latitudes. The changes in ocean heat transport affect surface temperatures. 5 The largest increase in the meridional surface temperature gradient occurs between 40-50 N. This increase is associated with a strengthening of 20% in 850 hPa winds. The stream jet intensiﬁcation in the Northern Hemisphere in return alters the temperature structure of the ocean heat through an increased gyre circulation, and associated heat transport (+0.1-0.2 PW), at the mid-latitudes, and a decreased gyre ocean heat transport (-0.2 PW) at high-latitudes. The changes in meridional temperature and pressure gradients cause the Intertropical Convergence Zone (ITCZ) to move southward, leading to stronger westerlies and 10 a more positive Southern Annual Mode (SAM) in the Southern Hemisphere. The positive SAM inﬂuences sea ice formation leading to an increase in Antarctic sea ice. Our coupled-model simulation framework shows that the classical “thermal bipolar see-saw” has previously undiscovered consequences in both Hemispheres: these include Northern Hemisphere gyre heat transport and wind changes; alongside an increase in Antarctic sea ice during the ﬁrst 250 years of meltwater forcing. mechanism in our simulation that explains the weakened sub-tropical jet.
Signatures of Magnetic Reconnection at the Footpoints of Fan-shaped Jets on a Light Bridge Driven by Photospheric Convective Motions

Dynamical jets are generally found on light bridges (LBs), which are key to studying sunspot decay. So far, their formation mechanism is not fully understood. In this paper, we used state-of-the-art observations from the Goode Solar Telescope, the Interface Region Imaging Spectrograph, the Spectro-polarimeter on board Hinode, and the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory to analyze the fan-shaped jets on LBs in detail. A continuous upward motion of the jets in the ascending phase is found from the Hα velocity that lasts for 12 minutes and is associated with the Hα line wing enhancements. Two mini jets appear on the bright fronts of the fan-shaped jets visible in the AIA 171 and 193 Å channels, with a time interval as short as 1 minute. Two kinds of small-scale convective motions are identified in the photospheric images, along with the Hα line wing enhancements. One seems to be associated with the formation of a new convection cell, and the other manifests as the motion of a dark lane passing through the convection cell. The finding of three-lobe Stokes V profiles and their inversion with the NICOLE code indicate that there are magnetic field lines with opposite polarities in LBs. From the Hα −0.8 Å images, we found ribbon-like brightenings propagating along the LBs, possibly indicating slipping reconnection. Our observation supports the idea that the fan-shaped jets under study are caused by magnetic reconnection, and photospheric convective motions play an important role in triggering the magnetic reconnection.
Tighter Post-quantum Proof for Plain FDH, PFDH and GPV-IBE

In CRYPTO 2012, Zhandry developed generic semi-constant oracle technique and proved security of an identity-based encryption scheme, GPV-IBE, and full domain hash (FDH) signature scheme in the quantum random oracle model (QROM). However, the reduction provided by Zhandry incurred a quadratic reduction loss. In this work, we provide a much tighter proof, with linear reduntion loss, for the FDH, probabilistc FDH (PFDH), and GPV-IBE in the QROM. Our proof is based on the measure-and-reprogram technique developed by Don, Fehr, Majenz and Schaﬀner.
Evaluating unsupervised word segmentation in adults: a meta-analysis

Humans, even from infancy, are capable of unsupervised (“sta-tistical”) learning of linguistic information. However, it remains unclear which of the myriad algorithms for unsupervised learning captures human abilities. This matters because unsupervised learning algorithms vary greatly in how much can be learned how quickly. Thus, which algorithm(s) humans use may place a strong bound on how much of language can actually be learned in an unsupervised fashion. As a step towards more precisely characterizing human unsupervised learning capabilities, we quantitatively synthesize the literature on adult unsupervised (“statistical”) word segmentation. Unfortunately, most conﬁdence intervals were very large, and few moderators were found to be signiﬁcant. These ﬁndings are consistent with prior work suggesting low power and precision in the literature. Constraining theory will require more, higher-powered studies.
Landau gauge ghost and gluon propagators in SU(2) lattice gauge theory: Gribov ambiguity reexamined

We reinvestigate the problem of Gribov ambiguities within the Landau (or Lorentz) gauge for the ghost and gluon propagators in pure $SU(2)$ lattice gauge theory. We make use of the full symmetry group of the action taking into account large, i.e. nonperiodic, $\mathbb{Z}(2)$ gauge transformations leaving lattice plaquettes invariant. Enlarging in this way the gauge orbits for any given gauge field configuration the Landau gauge can be fixed at higher local extrema of the gauge functional in comparison with standard (overrelaxation) techniques. This has a clearly visible effect not only for the ghost propagator at small momenta but also for the gluon propagator, in contrast to the common belief.
Title: SARS-CoV-2 infection of African green monkeys results in mild respiratory disease discernible by PET/CT imaging and prolonged shedding of infectious virus from both respiratory and gastrointestinal tracts

: Vaccines are urgently needed to combat the global coronavirus disease 2019 (COVID-19) pandemic, and testing of candidate vaccines in an appropriate non-human primate (NHP) model is a critical step in the process. Infection of African green monkeys (AGM) with a low passage human isolate of SARS-CoV-2 by aerosol or
Direct Numerical Simulations of a High-Pressure Turbine Vane

In this paper we establish a benchmark data set of a generic high-pressure turbine vane generated by direct numerical simulation (DNS) to resolve fully the flow. The test conditions for this case are a Reynolds number of 0.57 million and an exit Mach number of 0.9, which is representative of a modern transonic high-pressure turbine vane. In this study we first compare the simulation results with previously published experimental data. We then investigate how turbulence affects the surface flow physics and heat transfer. An analysis of the development of loss through the vane passage is also performed. The results indicate that free-stream turbulence tends to induce streaks within the near wall flow, which augment the surface heat transfer. Turbulent breakdown is observed over the late suction surface, and this occurs via the growth of two-dimensional Kelvin-Helmholtz spanwise roll-ups, which then develop into lambda vortices creating large local peaks in the surface heat transfer. Turbulent dissipation is found to significantly increase losses within the trailing-edge region of the vane.
Probabilistic projections and past trends of sea level rise in Finland

Abstract. We explore past trends and future projections of mean sea level (MSL) at the Finnish coast, in the northeastern Baltic Sea, during the period 1901–2100. We decompose the relative MSL change into three components: regional sea level rise (SLR), postglacial land uplift, and the effect of changes in wind climate. Past trends of regional SLR can be calculated after subtracting the other two components from the MSL trends observed by tide gauges, as the land uplift rates obtained from the semi-empirical model NKG2016LU are independent of tide gauge observations. According to the results, local absolute SLR trends are close to global mean rates. To construct future projections, we combine an ensemble of global SLR projections in a probabilistic framework. In addition, we use climate model results to estimate future changes in wind climate and their effect on MSL in the semi-enclosed Baltic Sea. This yields probability distributions of MSL change for three scenarios representing different future emission pathways. Spatial variations in the MSL projections result primarily from different local land uplift rates: under the medium-emission scenario RCP4.5/SSP2-4.5, for example, the projected MSL change (5 % to 95 % range) over the 21st century varies from −28 (−54 to 24) cm in the Bothnian Bay to 31 (5 to 83) cm in the eastern Gulf of Finland.
Magnetic Geodesics via the Heat Flow

Magnetic geodesics describe the trajectory of a particle in a Riemannian manifold under the influence of an external magnetic field. In this article, we use the heat flow method to derive existence results for such curves. We first establish subconvergence of this flow to a magnetic geodesic under certain boundedness assumptions. It is then shown that these conditions are satisfied provided that either the magnetic field admits a global potential or the initial curve is sufficiently small. Finally, we discuss different examples which illustrate our results.
Inferring surface energy fluxes using drone data assimilation in large eddy simulations

Abstract. Spatially representative estimates of surface energy exchange from field measurements are required for improving and validating Earth system models and satellite remote sensing algorithms. The scarcity of flux measurements can limit understanding of ecohydrological responses to climate warming, especially in remote regions with limited infrastructure. Direct field measurements often apply the eddy covariance method on stationary towers, but recently, drone-based measurements of temperature, humidity, and wind speed have been suggested as a viable alternative to quantify the turbulent fluxes of sensible (H) and latent heat (LE). A data assimilation framework to infer uncertainty-aware surface flux estimates from sparse and noisy drone-based observations is developed and tested using a turbulence-resolving large eddy simulation (LES) as a forward model to connect surface fluxes to drone observations. The proposed framework explicitly represents the sequential collection of drone data, accounts for sensor noise, includes uncertainty in boundary and initial conditions, and jointly estimates the posterior distribution of a multivariate parameter space. Assuming typical flight times and observational errors of light-weight, multi-rotor drone systems, we first evaluate the information gain and performance of different ensemble-based data assimilation schemes in experiments with synthetically generated observations. It is shown that an iterative ensemble smoother outperforms both the non-iterative ensemble smoother and the particle batch smoother in the given problem, yielding well-calibrated posterior uncertainty with continuous ranked probability scores of 12 W m−2 for both H and LE, with standard deviations of 37 W m−2 (H) and 46 W m−2 (LE) for a 12 min vertical step profile by a single drone. Increasing flight times, using observations from multiple drones, and further narrowing the prior distributions of the initial conditions are viable for reducing the posterior spread. Sampling strategies prioritizing space–time exploration without temporal averaging, instead of hovering at fixed locations while averaging, enhance the non-linearities in the forward model and can lead to biased flux results with ensemble-based assimilation schemes. In a set of 18 real-world field experiments at two wetland sites in Norway, drone data assimilation estimates agree with independent eddy covariance estimates, with root mean square error values of 37 W m−2 (H), 52 W m−2 (LE), and 58 W m−2 (H+LE) and correlation coefficients of 0.90 (H), 0.40 (LE), and 0.83 (H+LE). While this comparison uses the simplifying assumptions of flux homogeneity, stationarity, and flat terrain, it is emphasized that the drone data assimilation framework is not confined to these assumptions and can thus readily be extended to more complex cases and other scalar fluxes, such as for trace gases in future studies.
Understanding pattern scaling errors across a range of emissions pathways

Abstract. The regional climate impacts of hypothetical future emissions scenarios can be estimated by combining Earth system model simulations with a linear pattern scaling model such as MESMER (Modular Earth System Model Emulator with spatially Resolved output), which uses estimated patterns of the local response per degree of global temperature change. Here we use the mean trend component of MESMER to emulate the regional pattern of the surface temperature response based on historical single-forcer and future Shared Socioeconomic Pathway (SSP) CMIP6 (Coupled Model Intercomparison Project Phase 6) simulations. Errors in the emulations for selected target scenarios (SSP1–1.9, SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5) are decomposed into two components, namely (1) the differences in scaling patterns between scenarios as a consequence of varying combinations of external forcings and (2) the intrinsic time series differences between the local and global responses in the target scenario. The time series error is relatively small for high-emissions scenarios, contributing around 20 % of the total error, but is similar in magnitude to the pattern error for lower-emissions scenarios. This irreducible time series error limits the efficacy of linear pattern scaling for emulating strong mitigation pathways and reduces the dependence on the predictor pattern used. The results help guide the choice of predictor scenarios for simple climate models and where to target for the introduction of other dependent variables beyond global surface temperature into pattern scaling models.
AMORE-Isoprene v1.0: a new reduced mechanism for gas-phase isoprene oxidation

Abstract. Gas-phase oxidation of isoprene by ozone (O3) and the hydroxyl (OH) and nitrate (NO3) radicals significantly impacts tropospheric oxidant levels and secondary organic aerosol formation. The most comprehensive and up-to-date chemical mechanism for isoprene oxidation consists of several hundred species and over 800 reactions. Therefore, the computational expense of including the entire mechanism in large-scale atmospheric chemical transport models is usually prohibitive, and most models employ reduced isoprene mechanisms ranging in size from ∼ 10 to ∼ 200 species. We have developed a new reduced isoprene oxidation mechanism using a directed-graph path-based automated model reduction approach, with minimal manual adjustment of the output mechanism. The approach takes as inputs a full isoprene oxidation mechanism, the environmental parameter space, and a list of priority species which are protected from elimination during the reduction process. Our reduced mechanism, AMORE-Isoprene (where AMORE stands for Automated Model Reduction), consists of 12 species which are unique to the isoprene mechanism as well as 22 reactions. We demonstrate its performance in a box model in comparison with experimental data from the literature and other current isoprene oxidation mechanisms. AMORE-Isoprene's performance with respect to predicting the time evolution of isoprene oxidation products, including isoprene epoxydiols (IEPOX) and formaldehyde, is favorable compared with other similarly sized mechanisms. When AMORE-Isoprene is included in the Community Regional Atmospheric Chemistry Multiphase Mechanism 1.0 (CRACMM1AMORE) in the Community Multiscale Air Quality Model (CMAQ, v5.3.3), O3 and formaldehyde agreement with Environmental Protection Agency (EPA) Air Quality System observations is improved. O3 bias is reduced by 3.4 ppb under daytime conditions for O3 concentrations over 50 ppb. Formaldehyde bias is reduced by 0.26 ppb on average for all formaldehyde measurements compared with the base CRACMM1. There was no significant change in computation time between CRACMM1AMORE and the base CRACMM. AMORE-Isoprene shows a 35 % improvement in agreement between simulated IEPOX concentrations and chamber data over the base CRACMM1 mechanism when compared in the Framework for 0-D Atmospheric Modeling (F0AM) box model framework. This work demonstrates a new highly reduced isoprene mechanism and shows the potential value of automated model reduction for complex reaction systems.
On the linkage between future Arctic sea ice retreat, Euro-Atlantic circulation regimes and temperature extremes over Europe

Abstract. The question to what extent Arctic sea ice loss is able to affect atmospheric dynamics and climate extremes over mid-latitudes still remains a highly debated topic. In this study we investigate model experiments from the Polar Amplification Model Intercomparison Project (PAMIP) and compare experiments with future sea ice loss prescribed over the entire Arctic, as well as only locally over the Barents and Kara seas, with a present-day reference experiment. The first step is to perform a regime analysis and analyze the change in occurrence frequencies of five computed Euro-Atlantic winter circulation regimes. Forced by future Arctic sea ice conditions, most models show more frequent occurrences of a Scandinavian blocking pattern in at least 1 winter month, whereas there is an overall disagreement between individual models on the sign of frequency changes of two regimes that, respectively, resemble the negative and positive phase of the North Atlantic Oscillation. Focusing on the ECHAM6 PAMIP experiments, we subsequently employ a framework of conditional extreme-event attribution. It demonstrates how detected regime frequency changes can be used to decompose sea-ice-induced frequency changes of European temperature extremes into two different contributions: one “changed-regime” term that is related to dynamical changes in regime occurrence frequencies and another more thermodynamically motivated “fixed-regime” contribution that is related to increased surface temperatures during a specific circulation regime. We show how the overall fixed-regime warming effect and also an increased Scandinavian blocking pattern frequency under future sea ice reductions can equally contribute to and shape the overall response signal of European cold extremes in midwinter. We also demonstrate how a decreased occurrence frequency of an anticyclonic regime over the eastern Atlantic dynamically counteracts the fixed-regime warming response and results in no significant changes in overall January warm-extreme occurrences. However, when compared to other characteristics of future climate change, such as the thermodynamical impact of globally increased sea surface temperatures, the effects of Arctic sea ice loss on European temperature extremes are of secondary relevance.
Temporal variations of surface mass balance over the last 5000 years around Dome Fuji, Dronning Maud Land, East Antarctica

Abstract. We reconstructed surface mass balance (SMB) around Dome Fuji, Antarctica, over the last 5000 years using the data from 15 shallow ice cores and seven snow pits. The depth–age relationships for the ice cores were determined by synchronizing them with a layer-counted ice core from West Antarctica (WAIS Divide ice core) using volcanic signals. The reconstructed SMB records for the last 4000 years show spatial patterns that may be affected by their locations relative to the ice divides around Dome Fuji, proximity to the ocean, and wind direction. The SMB records from the individual ice cores and snow pits were stacked to reconstruct the SMB history in the Dome Fuji area. The stacked record exhibits a long-term decreasing trend at -0.037±0.005 kg m−2 per century over the last 5000 years in the preindustrial period. The decreasing trend may be the result of long-term surface cooling over East Antarctica and the Southern Ocean and sea ice expansion in the water vapor source areas. The multidecadal to centennial variations of the Dome Fuji SMB after detrending the record shows four distinct periods during the last millennium: a mostly negative period before 1300 CE, a slightly positive period from 1300 to 1450 CE, a slightly negative period from 1450 to 1850 CE with a weak maximum around 1600 CE, and a strong increase after 1850 CE. These variations are consistent with those of previously reconstructed SMB records in the East Antarctic plateau. The low accumulation rate periods tend to coincide with the combination of strong volcanic forcings and solar minima for the last 1000 years, but the correspondence is not clear for the older periods, possibly because of the lack of coincidence of volcanic and solar forcings or the deterioration of the SMB record due to a smaller number of stacked cores.
Playgrounds for Abstraction and Reasoning

While research on reasoning using large models is in the spotlight, a symbolic 1 method of making a compact model capable of reasoning is also attracting public 2 attention. We introduce the Mini-ARC dataset, a 5x5 compact version of the 3 Abstraction and Reasoning Corpus (ARC) to measure the abductive reasoning 4 capability. The dataset is small but creative, which maintains the difficulty of the 5 original dataset but improves usability for model training. Along with Mini-ARC, 6 we introduce the O2ARC interface, which includes richer features for humans 7 to solve the ARC tasks. By solving Mini-ARC with O2ARC, we collect human 8 trajectories called Mini-ARC traces
The CMCC Decadal Prediction System

Decadal climate predictions, obtained by constraining the initial condition of a dynamical model through a truthful estimate of the observed climate state, provide an accurate assessment of climate change in the near-term range and a useful tool to inform decision-makers on future climate-related risks. Here we present results from the CMIP6 DCPP-A decadal hindcasts produced with the operational CMCC decadal prediction system (CMCC DPS), based on the fully-coupled CMCC-CM2-SR5 dynamical model. A 15-member suite of 10-year 15 retrospective forecasts, initialized every year from 1960 to 2020, is performed using a full-field initialization strategy. The predictive skill for key variables is assessed and compared with the skill of an ensemble of non-initialized historical simulations so as to quantify the added value of initialization. In particular, the CMCC DPS is able to skilfully reproduce past-climate surface temperature fluctuations over large parts of the globe. The North Atlantic Ocean is the region that benefits the most from initialization, with the largest skill enhancement occurring over the subpolar region compared to historical simulations. On the other hand, the predictive skill over the Pacific Ocean rapidly decays with forecast time, especially over the North Pacific. In terms of precipitation, the CMCC DPS skill is significantly higher than that of the historical simulations over a few specific regions, including Sahel, Northern Eurasia and over the Western and Central Europe. The Atlantic Multidecadal Variability is also skilfully predicted, and this likely contributes to the skill found over remote areas through downstream influence, circulation changes and teleconnections. Considering the relatively small ensemble size, a remarkable prediction skill is also found for the North Atlantic Oscillation, with maximum correlations obtained in the 1–9 lead-year range.