Large Ensemble Research Articles

Insights from very‐large‐ensemble data assimilation experiments with a high‐resolution general circulation model of the Red Sea

AbstractEnsemble Kalman Filters (EnKFs), which assimilate observations based on statistics derived from an ensemble of samples of ocean states, have become the norm for ocean data assimilation (DA) and forecasting. These schemes are commonly implemented with inflation and localization techniques to increase their ensemble spread and to filter out spurious long‐range correlations resulting from the limited‐size ensembles imposed by computational burden constraints. Such ad‐hoc methods were found to be not necessary in ensemble DA experiments with simplified ocean/atmospheric models and large ensembles. Here, we conduct a series of one‐year‐long ensemble experiments with a fully realistic EnKF‐DA system in the Red Sea using tens ‐to thousands of ensemble members. The system assimilates satellite and in‐situ observations and accounts for model uncertainties by integrating a 4‐km‐resolution ocean model with European Center for Medium Range Weather Forecast (ECMWF) atmospheric ensemble fields, perturbed internal physics and initial conditions for forecasting. OceanOur results indicate that accounting for model uncertainties is more beneficial than simply increasing the ensemble size, with the improvements due to large ensembles leveling off at about 250 members. Besides, and in contrast to what is commonly observed with simplified models, the investigated ensemble DA system still required localization even when implemented with thousands of members. These findings are explained by: (i) amplified spurious long‐range correlations produced by the low‐rank nature of the ECMWF atmospheric forcing ensemble; and (ii) non‐Gaussianity generated by the perturbed internal physical parameterization schemes. Large‐ensemble forcing fields and non‐Gaussian DA methods might be needed to get full benefits from large ensembles in ocean DA.

Resting State EEG Analysis for Schizophrenia: from Alpha-Rhythm Reduction to Microstates Assessment

Background: due to the emergence of new technologies for analyzing of EEG signal, many new researches in this field have appeared in recent years, including those investigating EEG parameters of schizophrenia. The aim: this publication provides an overview of actual studies on the possibilities of using the assessment of resting state EEG recordings in the diagnostics and prognosis of schizophrenia course. Material and methods: publications were selected in eLibrary, PubMed, Google Scholar and CNKI databases using the keywords: “psychosis”, “schizophrenia”, “EEG”, “resting state”. Methodologically, the atricle is a narrative literature review. Thirty-three sources were selected for analysis. Discussion and conclusion: according to the data available to present date qualitive and quantitative assessment of resting EEGs cannot be used for the instrumental diagnosis of schizophrenia because the most commonly detected increase in the proportion of slow-wave activity is seen in a several disorders. However, some quantitative spectral estimates of resting state EEG could be used to identify poor prognosis response to antipsychotic therapy, as well as for objective assessment of the dynamics of the mental state. Estimation of the power of slow resting EEG rhythms and other methods of assessing the connectivity of different neural networks could be considered as potential markers of the presence of a specific endophenotype. Modern digital technologies, including machine learning and artificial intelligence algorithms, make it possible to identify resting EEG of the schizophrenic patients from healthy controls with accuracy, sensitivity and specificity more than 95%. EEG microstates assessment, which can be used to assess the functioning of large neuronal ensembles, are one of the methods for detecting the endophenotype of schizophrenia.

Controls on Early Cretaceous South Atlantic Ocean circulation and carbon burial – a climate model–proxy synthesis

Abstract. Black shale sediments from the Barremian to Aptian South Atlantic document the intense and widespread burial of marine organic carbon during the initial stages of seafloor spreading between Africa and South America. The enhanced sequestration of atmospheric CO2 makes these young ocean basins potential drivers of the Early Cretaceous carbon cycle and climate perturbations. The opening of marine gateways between initially restricted basins and related circulation and ventilation changes are a commonly invoked explanation for the transient formation and disappearance of these regional carbon sinks. However, large uncertainties in palaeogeographic reconstructions limit the interpretation of available palaeoceanographic data and prevent any robust model-based quantifications of the proposed circulation and carbon burial changes. Here, we present a new approach to assess the principal controls on the Early Cretaceous South Atlantic and Southern Ocean circulation changes under full consideration of the uncertainties in available boundary conditions. Specifically, we use a large ensemble of 36 climate model experiments to simulate the Barremian to Albian progressive opening of the Falkland Plateau and Georgia Basin gateways with different configurations of the proto-Drake Passage, the Walvis Ridge, and atmospheric CO2 concentrations. The experiments are designed to complement available geochemical data across the regions and to test circulation scenarios derived from them. All simulations show increased evaporation and intermediate water formation at subtropical latitudes that drive a meridional overturning circulation whose vertical extent is determined by the sill depth of the Falkland Plateau. The densest water masses formed in the southern Angola Basin and potentially reached the deep Cape Basin as Walvis Ridge Overflow Water. Palaeogeographic uncertainties are as important as the lack of precise knowledge of atmospheric CO2 levels for the simulated temperature and salinity spread in large parts of the South Atlantic. Overall temperature uncertainties reach up to 15 °C and increase significantly with water depth. The ensemble approach reveals temporal changes in the relative importance of geographic and radiative forcings for the simulated oceanographic conditions and, importantly, nonlinear interactions between them. The progressive northward opening of the highly restricted Angola Basin increased the sensitivity of local overturning and upper-ocean stratification to atmospheric CO2 concentrations due to large-scale changes in the hydrological cycle, while the chosen proto-Drake Passage depth is critical for the ocean dynamics and CO2 response in the southern South Atlantic. Finally, the simulated processes are integrated into a recent carbon burial framework to document the principal control of the regional gateway evolution on the progressive shift from the prevailing saline and oxygen-depleted subtropical water masses to the dominance of ventilated high-latitude deep waters.

Estimating the gain of increasing the ensemble size from analytical considerations

AbstractModel ensembles may provide estimates of uncertainties arising from unknown initial conditions and model deficiencies. Often, the ensemble mean is taken as the best estimate, and quantities such as the mean‐squared error between model mean and observations decrease with the number of ensemble members. But the ensemble size is often limited by available resources, and so some idea of how many ensemble members that are needed before the error has saturated would be advantageous. The behaviour with ensemble size is often estimated by producing subsamples from a large ensemble. But this strategy requires that this large ensemble is already available. Fortunately, in many situations, the dependence on ensemble size follows simple analytical relations when the quantity under interest (such as the mean‐squared error between ensemble mean and observations) is calculated over many grid points or time points. This holds both for ensemble means and the related sampling variance. Here, we present such relations and demonstrate how they can be used to estimate the gain of increasing the ensemble. Whereas previous work has mainly focused on the size of the model ensemble, we recognize that uncertainties in observations play a role. We therefore also study the effect of using the mean of an ensemble of reanalyses. We show how the analytical relations can be used to estimate the point where the gain of increasing the size of the model ensemble is dwarfed by the gain of increasing the number of reanalyses. We demonstrate these points using two climate model ensembles: a large multimodel ensemble and a large single‐model initial‐condition ensemble.

Self-avoidance dominates the selection of hippocampal replay.

Spontaneous neural activity sequences are generated by the brain in the absence of external input 1-12 , yet how they are produced remains unknown. During immobility, hippocampal replay sequences depict spatial paths related to the animal's past experience or predicted future 13 . By recording from large ensembles of hippocampal place cells 14 in combination with optogenetic manipulation of cortical input in freely behaving rats, we show here that the selection of hippocampal replay is governed by a novel self-avoidance principle. Following movement cessation, replay of the animal's past path is strongly avoided, while replay of the future path predominates. Moreover, when the past and future paths overlap, early replays avoid both and depict entirely different trajectories. Further, replays avoid self-repetition, on a shorter timescale compared to the avoidance of previous behavioral trajectories. Eventually, several seconds into the stopping period, replay of the past trajectory dominates. This temporal organization contrasts with established and recent predictions 9,10,15,16 but is well-recapitulated by a symmetry-breaking attractor model of sequence generation in which individual neurons adapt their firing rates over time 26-35 . However, while the model is sufficient to produce avoidance of recently traversed or reactivated paths, it requires an additional excitatory input into recently activated cells to produce the later window of past-dominance. We performed optogenetic perturbations to demonstrate that this input is provided by medial entorhinal cortex, revealing its role in maintaining a memory of past experience that biases hippocampal replay. Together, these data provide specific evidence for how hippocampal replays are generated.

Land-Use Feedback under Global Warming—A Transition from Radiative to Hydrological Feedback Regime

Abstract This study examines the effects of land-use (LU) change on regional climate, comparing historical and future scenarios using seven climate models from phase 6 of Coupled Model Intercomparison Project–Land Use Model Intercomparison Project experiments. LU changes are evaluated relative to land-use conditions during the preindustrial climate. Using the Community Earth System Model, version 2–Large Ensemble (CESM2-LE) experiment, we distinguish LU impacts from natural climate variability. We assess LU impact locally by comparing the impacts of climate change in neighboring areas with and without LU changes. Further, we conduct CESM2 experiments with and without LU changes to investigate LU-related climate processes. A multimodel analysis reveals a shift in LU-induced climate impacts, from cooling in the past to warming in the future climate across midlatitude regions. For instance, in North America, LU’s effect on air temperature changes from −0.24° ± 0.18°C historically to 0.62° ± 0.27°C in the future during the boreal summer. The CESM2-LE shows a decrease in LU-driven cooling from −0.92° ± 0.09°C in the past to −0.09° ± 0.09°C in future boreal summers in North America. A hydroclimatic perspective linking LU and climate feedback indicates LU changes causing soil moisture drying in the midlatitude regions. This contrasts with hydrology-only views showing wetter soil conditions due to LU changes. Furthermore, global warming causes widespread drying of soil moisture across various regions. Midlatitude regions shift from a historically wet regime to a water-limited transitional regime in the future climate. This results in reduced evapotranspiration, weakening LU-driven cooling in future climate projections. A strong linear relationship exists between soil moisture and evaporative fraction in midlatitudes. Significance Statement Land–atmosphere feedback involving soil moisture can increase local temperature and affect how land-use (LU) change impacts manifest in a warming climate. Conversely, an increased surface reflectance due to LU change can decrease local temperature in the midlatitude regions. Further, the LU change signal is often mixed with the internal climate variability, making it harder to separate. This study uses a novel technique to separate LU change impact from other climate forcing in the latest generation of climate and Earth system models. In the future climate, soil moisture drying lessens the cooling impact. A large-ensemble climate experiment analysis confirms a significant weakening of the LU-driven cooling impact in the midlatitudes. Both LU and climate changes exacerbate soil moisture drying, leading to a shift toward a water-limited system where hydrological feedback becomes more influential than radiative feedback.

Variability in flood frequency in sub-Saharan Africa: The role of large-scale climate modes of variability and their future impacts

Sub-Saharan Africa (SSA) is strongly affected by flood hazards, endangering human lives and economic stability. However, the role of internal climate modes of variability in driving fluctuations in SSA flood occurrence remains poorly documented and understood. To address this gap, we quantify the relative and combined contribution of large-scale climate drivers to seasonal and regional flood occurrence using a new 65-year daily streamflow dataset, sea-surface temperatures derived from observations, and 12 Single Model Initial-condition Large Ensembles (SMILEs) from the Coupled Model Intercomparison Project Phases 5 and 6. We find significant relationships between floods and large-scale climate variability across SSA, with climatic drivers accounting for 30–90 % of the variability in floods. Notably, western, central, and the summer-rain region of southern Africa display stronger teleconnections to large-scale climate variability in comparison to East Africa and the winter-rain region of South Africa, where regional circulation patterns and human activities may play a more important role. In southern and eastern Africa, floods are mainly influenced by teleconnections with the Pacific and Indian Oceans, while in western and central Africa, teleconnections with the Atlantic Ocean and Mediterranean Sea play a larger role. We also find that the number of floods is projected to fluctuate by ± 10–50 % during the 21st century in response to different sequences of key modes of climate variability. We also note that the relative contributions of large-scale climate variability to future flood risks are generally consistent across all SMILEs. Our findings thus provide valuable information for long-term disaster risk reduction and management.

Advancing membrane-associated protein docking with improved sampling and scoring in Rosetta.

The oligomerization of protein macromolecules on cell membranes plays a fundamental role in regulating cellular function. From modulating signal transduction to directing immune response, membrane proteins (MPs) play a crucial role in biological processes and are often the target of many pharmaceutical drugs. Despite their biological relevance, the challenges in experimental determination have hampered the structural availability of membrane proteins and their complexes. Computational docking provides a promising alternative to model membrane protein complex structures. Here, we present Rosetta-MPDock, a flexible transmembrane (TM) protein docking protocol that captures binding-induced conformational changes. Rosetta-MPDock samples large conformational ensembles of flexible monomers and docks them within an implicit membrane environment. We benchmarked this method on 29 TM-protein complexes of variable backbone flexibility. These complexes are classified based on the root-mean-square deviation between the unbound and bound states (RMSDUB) as: rigid (RMSDUB <1.2 Å), moderately-flexible (RMSDUB ∈ [1.2, 2.2) Å), and flexible targets (RMSDUB > 2.2 Å). In a local docking scenario, i.e. with membrane protein partners starting ≈10 Å apart embedded in the membrane in their unbound conformations, Rosetta-MPDock successfully predicts the correct interface (success defined as achieving 3 near-native structures in the 5 top-ranked models) for 67% moderately flexible targets and 60% of the highly flexible targets, a substantial improvement from the existing membrane protein docking methods. Further, by integrating AlphaFold2-multimer for structure determination and using Rosetta-MPDock for docking and refinement, we demonstrate improved success rates over the benchmark targets from 64% to 73%. Rosetta-MPDock advances the capabilities for membrane protein complex structure prediction and modeling to tackle key biological questions and elucidate functional mechanisms in the membrane environment. The benchmark set and the code is available for public use at github.com/Graylab/MPDock.

Emergence of lake conditions that exceed natural temperature variability

Lake surface temperatures are projected to increase under climate change, which could trigger shifts in the future distribution of thermally sensitive aquatic species. Of particular concern for lake ecosystems are when temperatures increase outside the range of natural variability, without analogue either today or in the past. However, our knowledge of when such no-analogue conditions will appear remains uncertain. Here, using daily outputs from a large ensemble of SSP3-7.0 Earth system model projections, we show that these conditions will emerge at the surface of many northern lakes under a global warming of 4.0 °C above pre-industrial conditions. No-analogue conditions will occur sooner, under 2.4 °C of warming, at lower latitudes, primarily due to a weaker range of natural variability, which increases the likelihood of the upper natural limit of lake temperature being exceeded. Similar patterns are also projected in subsurface water, with no-analogue conditions occurring first at low latitudes and occurring last, if at all, at higher latitudes. Our study suggests that global warming will induce changes across the water column, particularly at low latitudes, leading to the emergence of unparalleled climates with no modern counterparts, probably affecting their habitability and leading to rearrangements of freshwater habitats this century.

Drivers of future extratropical sea surface temperature variability changes in the North Pacific

Under anthropogenic warming, future changes to climate variability beyond specific modes such as the El Niño-Southern Oscillation (ENSO) have not been well-characterized. In the Community Earth System Model version 2 Large Ensemble (CESM2-LE) climate model, the future change to sea surface temperature (SST) variability (and correspondingly marine heatwave intensity) on monthly timescales and longer is spatially heterogeneous. We examined these projected changes (between 1960–2000 and 2060–2100) in the North Pacific using a local linear stochastic-deterministic model, which allowed us to quantify the effect of changes to three drivers on SST variability: ocean “memory” (the SST damping timescale), ENSO teleconnections, and stochastic noise forcing. The ocean memory declines in most areas, but lengthens in the central North Pacific. This change is primarily due to changes in air-sea feedbacks and ocean damping, with the shallowing mixed layer depth playing a secondary role. An eastward shift of the ENSO teleconnection pattern is primarily responsible for the pattern of SST variance change.

Large-ensemble assessment of the Arctic stratospheric polar vortex morphology and disruptions

Abstract. The stratospheric polar vortex (SPV) comprises strong westerly winds during winter in each hemisphere. Despite ample knowledge on the SPV's high variability and its frequent disruptions by sudden stratospheric warmings (SSWs) in the Northern Hemisphere (NH), questions on how well current climate models can simulate these dynamics remain open. Specifically the accuracy in reproducing SPV morphology and the differentiation between split and displacement SSW events are crucial to assess the models in this regard. In this study, we evaluate the capability of climate models to simulate the NH SPV by comparing large ensembles of historical simulations to ERA5 reanalysis data. For this, we analyze geometric-based diagnostics at three pressure levels that describe SPV morphology. Our analysis reveals that no model exactly reproduces SPV morphology of ERA5 in all diagnostics at all altitudes. Concerning the SPV morphology as stretching (aspect ratio) and location (centroid latitude) parameters, most models are biased to some extent, but the strongest deviations can be found for the vortex-splitting parameter (excess kurtosis). Moreover, some models underestimate the variability of SPV strength. Assessing the reliability of the ensembles in distinguishing SSWs subdivided into SPV displacement and split events, we find large differences between the model ensembles. In general, SPV displacements are represented better than splits in the simulation ensembles, and high-top models and models with finer vertical resolution perform better. A good performance in representing the morphological diagnostics does not necessarily imply reliability and therefore a good performance in simulating displacements and splits. Assessing the model biases and their representation of SPV dynamics is needed to improve credibility of climate model projections, for example, by giving stronger weightings to better performing models.

Trapped Atoms and Superradiance on an Integrated Nanophotonic Microring Circuit

Interfacing cold atoms with integrated nanophotonic devices could offer new paradigms for engineering atom-light interactions and provide a potentially scalable route for quantum sensing, metrology, and quantum information processing. However, it remains a challenging task to efficiently trap a large ensemble of cold atoms on an integrated nanophotonic circuit. Here, we demonstrate direct loading of an ensemble of up to 70 atoms into an optical microtrap on a nanophotonic microring circuit. Efficient trap loading is achieved by employing degenerate Raman-sideband cooling in the microtrap, where a built-in spin-motion coupling arises directly from the vector light shift of the evanescent-field potential on a microring. Atoms are cooled into the trap via optical pumping with a single free space beam. We have achieved a trap lifetime approaching 700 ms under continuous cooling. We show that the trapped atoms display large cooperative coupling and superradiant decay into a whispering-gallery mode of the microring resonator, holding promise for explorations of new collective effects. Our technique can be extended to trapping a large ensemble of cold atoms on nanophotonic circuits for various quantum applications. Published by the American Physical Society 2024

Assessing the impact of climate change on high return levels of peak flows in Bavaria applying the CRCM5 large ensemble

Abstract. ​​​​​​​Severe floods with extreme return periods of 100 years and beyond have been observed in several large rivers in Bavaria in the last 3 decades. Flood protection structures are typically designed based on a 100-year event, relying on statistical extrapolations of relatively short observation time series while ignoring potential temporal non-stationarity. However, future precipitation projections indicate an increase in the frequency and intensity of extreme rainfall events, as well as a shift in seasonality. This study aims to examine the impact of climate change on the 100-year flood (HF100) events of 98 hydrometric gauges within hydrological Bavaria. A hydrological climate change impact (CCI) modeling chain consisting of a regional Single Model Initial-condition Large Ensemble (SMILE) and a single hydrological model was created. The 50 equally probable members of the Canadian Regional Climate Model version 5 large ensemble (CRCM5-LE) were used to drive the hydrological model WaSiM (Water balance Simulation Model) to create a hydro-SMILE. As a result, a database of 1500 model years (50 members × 30 years) per investigated time period was established for extreme value analysis (EVA) to illustrate the benefit of the hydro-SMILE approach for a robust estimation of HF100 based on annual maxima (AM) and to examine the CCI on the frequency and magnitude of HF100 in different discharge regimes under a strong-emission scenario (RCP8.5). The results demonstrate that the hydro-SMILE approach provides a clear advantage for a robust estimation of HF100 using the empirical probability of 1500 AM compared to its estimation using the generalized extreme value (GEV) distribution of 1000 samples of typically available time series sizes of 30, 100, and 200 years. Thereby, by applying the hydro-SMILE framework, the uncertainty from statistical estimation can be reduced. The study highlights the added value of using hydrological SMILEs to project future flood return levels. The CCI of HF100 varies for different flow regimes, with snowmelt-driven catchments experiencing severe increases in frequency and magnitude, leading to unseen extremes that impact the distribution. Pluvial regimes show a lower intensification or even decline. The dynamics of HF100 driving mechanisms depict a decline in snowmelt-driven events in favor of rainfall-driven events, an increase in events driven by convective rainfall, and almost no change in the ratio between single-driver and compound events towards the end of the century.

Efficient Fabrication of High‐Density Ensembles of Color Centers via Ion Implantation on a Hot Diamond Substrate

AbstractNitrogen‐vacancy (NV) centers in diamonds are one of the most promising systems for quantum technologies, including quantum metrology and sensing. A promising strategy for the achievement of high sensitivity to external fields relies on the exploitation of large ensembles of NV centers, whose fabrication by ion implantation is upper limited by the amount of radiation damage introduced in the diamond lattice. In this work an approach is demonstrated to increase the density of NV centers upon the high‐fluence implantation of MeV N2+ ions on a hot target substrate (&gt;550 °C). The results show that with respect to room‐temperature implantation, the high‐temperature process increases the vacancy density threshold required for the irreversible conversion of diamond to a graphitic phase, thus enabling to achieve higher density ensembles. Furthermore, the formation efficiency of color centers is investigated on diamond substrates implanted at varying temperatures with MeV N2+ and Mg+ ions revealing that the formation efficiency of both NV centers and magnesium‐vacancy (MgV) centers increases with the implantation temperature.

Robust, high-yield, rapid fabrication of DNA constructs for Magnetic Tweezers

Single-molecule techniques are highly sensitive tools that can reveal reaction intermediates often obscured in experiments involving large ensembles of molecules. Therefore, they provide unprecedented information on the mechanisms that control biomolecular reactions. Currently, one of the most significant single-molecule assays is Magnetic Tweezers (MT), which probes enzymatic reactions at high spatio-temporal resolutions on tens, if not hundreds, of molecules simultaneously. For high-resolution MT experiments, a short double-stranded DNA molecule (less than 2000 base pairs) is typically attached between a micron-sized superparamagnetic bead and a surface. The fabrication of such a substrate is key for successful single-molecule assays, and several papers have discussed the possibility of improving the fabrication of short DNA constructs. However, reported yields are usually low and require additional time-consuming purification steps (e.g., gel purification). In this paper, we propose the use of a Golden Gate Assembly assay that allows for the production of DNA constructs within minutes (starting from PCR products). We discuss how relevant parameters may affect the yield and offer single-molecule experimentalists a simple yet robust approach to fabricate DNA constructs.

Future Changes of Extreme Precipitation and Related Atmospheric Conditions in East Asia under Global Warming Projected in Large Ensemble Climate Prediction Data

Abstract Extreme precipitation is expected to pose a more severe threat to human society in the future. This work assessed the historical performance and future changes of extreme precipitation and related atmospheric conditions in a large ensemble climate prediction dataset, the database for Policy Decision-making for Future climate change (d4PDF), over East Asia. Compared with the TRMM and ERA5 datasets, the historical climate in d4PDF represents favorably the precipitation characteristics and the atmospheric conditions, although some differences are notable in the moisture, vertical motion, and cloud water fields. The future climate projection indicates that both the frequency and intensity of heavy precipitation events over East Asia increase compared with those in the present climate. However, when comparing the atmospheric conditions in the historical and future climates for the same precipitation intensity range, the future climate indicates smaller relatively humidity, weaker ascent, less cloud water content, and smaller temperature lapse rate, which negatively affect generating extreme precipitation events. The comparison of the precipitation intensity at the same amount of precipitable water between the historical and future climates indicates that extreme precipitation is weaker in the future, because of the more stabilized troposphere in the future. The general increase in extreme precipitation under future climate is primarily due to the enhanced increase in precipitable water in the higher temperature ranges, which counteracts the negative conditions of the stabilized troposphere.

Local hydroclimate drives differential warming rates between regular summer days and extreme hot days in the Northern Hemisphere

In this work, we compare the rate of warming of summertime extreme temperatures (summer maximum value of daily maximum temperature; TXx) relative to the local mean (summer mean daily maximum temperature; TXm) over the Northern Hemisphere in observations and one set of large ensemble (LE) simulations. During the 1979–2021 historical period, observations and simulations show robust warming trends in both TXm and TXx almost everywhere in the Northern Hemisphere, except over the eastern U.S. where observations show a slight cooling trend in TXx, which may be a manifestation of internal variability. We find that the observed warming rate in TXx is significantly smaller than in TXm in North Africa, western North America, Siberia, and Eastern Asia, whereas the warming rate in TXx is significantly larger over the Eastern U.S., the U.K., and Northwestern Europe. This observed geographical pattern is successfully reproduced by the vast majority of the LE members over the historical period, and is persistent (although less intense) in future climate projections over the 2051–2100 period. We also find that these relative warming patterns are mostly driven by the local hydroclimate conditions. TXx warms slower than TXm in the hyper-arid, arid, semi-arid and moist regions, where trends in the partitioning of the turbulent surface fluxes between the latent and sensible heat flux are similar during regular and extreme hot days. In contrast, TXx warms faster than TXm in dry-subhumid regions where trends in the partitioning of the surface fluxes are significantly different between regular and extreme hot days, with a larger role of sensible heat flux during the extreme hot days.

Self-gating stochastic-resonance-based autoencoder for unsupervised learning.

Incorporating additive noise components to an ensemble of McCulloch-Pitts neurons can enhance the information representation of the input, asymptotically approaching the average firing probability for large enough ensembles. We further multiply the input by the average firing probability to control the higher probability of self-gating, thereby forming a unified noise-boosted activation model with learnable noise-related hyperparameters. This gating strategy plays a crucial role in improving the performance of neural networks, as evidenced by the optimization of the autoencoder loss at nonzero optimal-noise-scaling hyperparameters, a phenomenon termed self-gating stochastic resonance. Experiments with designed autoencoders using noise-boosted activation functions demonstrate the potential applications of the self-gating stochastic resonance effect in the field of unsupervised learning.

Modified Split-Ring Resonators for Efficient and Homogeneous Microwave Control of Large Volume Spin Ensembles

Modified Split-Ring Resonators for Efficient and Homogeneous Microwave Control of Large Volume Spin Ensembles

Investigating the Model Hypothesis Space: Benchmarking Automatic Model Structure Identification With a Large Model Ensemble

AbstractSelecting an appropriate model for a catchment is challenging, and choosing an inappropriate model can yield unreliable results. The Automatic Model Structure Identification (AMSI) method simultaneously calibrates model structural choices and model parameters, which reduces the workload of comparing different models. In this study we benchmark AMSI's capabilities in two ways, using 12 hydro‐climatically diverse Model Parameter Estimation Experiment catchments. First, we calibrate parameter values for 7,488 different model structures and test AMSI's ability to find the best‐performing models in this set. Second, we compare the performance of these 7,488 models and AMSI's selection to the performance of 45 commonly used, structurally more diverse, conceptual models. In both cases, we quantify model accuracy (through the Kling‐Gupta Efficiency) and model adequacy (through various hydrologic signatures). AMSI effectively identifies high‐accuracy models among the 7,488 options, with Kling‐Gupta‐Efficiency scores comparable to the best among the 45 models. However, model adequacy remains poor for the accurate models, regardless of the selection method. In nine of the tested catchments, none of the most accurate models replicate observed signatures with less than 50% errors; in the remaining three catchments, only a handful of models do so. This paper thus provides strong empirical evidence that relying on aggregated efficiency metrics is unlikely to result in hydrologically adequate models, no matter how the models themselves are selected. Nevertheless, AMSI has been shown to effectively search the model hypothesis space it was given. Combined with an improved calibration approach it can therefore offer new ways to address the challenges of model structure selection.