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At least 451 records · Page 25

9 Li($\textit{d,p}$) reaction as a specific probe of 10 Li, the paradigm of parity-inverted nuclei around the $\textit{N}$ = 6 closed shell

In this work, we show, within the framework of renormalized nuclear field theory and of the induced reaction surrogate formalism, that the highly debated 10 Li structure, studied in a recent high statistics 9 Li ($\textit{d,p}$) 10 Li one-neutron transfer experiment, is consistent with, or better, requires, the presence of a virtual 1/2 + state of similar single-particle strength than that of the 1/2 – resonance at 0.45 ± 0.03 MeV. Based on continuum spectroscopy self-energy techniques, we find that the physical mechanism responsible for parity inversion in $^{10}_3$Li is the same as that at the basis of the similar phenomenon observed in $^{11}_4$Be and as that needed in 11 Li to have an important $\textit{s}$-wave ground-state component. In particular the strong dynamical coupling between the $s_{1/2}$ and the $d_{5/2}$ states, mediated by the quadrupole vibration of the core 9 Li. A phenomenon which also affects the strength distribution of the $d_{5/2}$ state, in particular, in the energy range of 3–4.5 MeV. Furthermore, this mechanism is also consistent with the (normal) sequence of the $1_{p_{1/2}}$ and $2_{s_{1/2}}$ levels in the $\textit{N}$ = 7 isotones $^{12}_5$B and $^{13}_6$C. The main aim of the present Rapid Communication is that of treating structure and reactions on equal footing and in a common language. In other words, the calculation of the 9 Li ($\textit{d,p}$) 10 Li reaction as a single conceptual step from individual single-particle motion and collective vibrations to absolute double differential cross sections of renormalized virtual and resonant final states, which can be directly compared with experiment.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection

A key question in SARS-CoV-2 infection is why viral loads and patient outcomes vary dramatically across individuals. Because spatial-temporal dynamics of viral spread and immune response are challenging to study in vivo, we developed Spatial Immune Model of Coronavirus (SIMCoV), a scalable computational model that simulates hundreds of millions of lung cells, including respiratory epithelial cells and T cells. SIMCoV replicates viral growth dynamics observed in patients and shows how spatially dispersed infections can lead to increased viral loads. The model also shows how the timing and strength of the T cell response can affect viral persistence, oscillations, and control. By incorporating spatial interactions, SIMCoV provides a parsimonious explanation for the dramatically different viral load trajectories among patients by varying only the number of initial sites of infection and the magnitude and timing of the T cell immune response. When the branching airway structure of the lung is explicitly represented, we find that virus spreads faster than in a 2D layer of epithelial cells, but much more slowly than in an undifferentiated 3D grid or in a well-mixed differential equation model. These results illustrate how realistic, spatially explicit computational models can improve understanding of within-host dynamics of SARS-CoV-2 infection.

59 BASIC BIOLOGICAL SCIENCES↗

Bistable dynamics and Hopf bifurcation in a refined model of early stage HIV infection

Recent clinical studies have shown that HIV disease pathogenesis can depend strongly on many factors at the time of transmission, including the strength of the initial viral load and the local availability of CD4+ T-cells. In this article, a new within-host model of HIV infection that incorporates the homeostatic proliferation of T-cells is formulated and analyzed. Due to the effects of this biological process, the influence of initial conditions on the proliferation of HIV infection is further elucidated. The identifiability of parameters within the model is investigated and a local stability analysis, which displays additional complexity in comparison to previous models, is conducted. The current study extends previous theoretical and computational work on the early stages of the disease and leads to interesting nonlinear dynamics, including a parameter region featuring bistability of infectious and viral clearance equilibria and the appearance of a Hopf bifurcation within biologically relevant parameter regimes.

59 BASIC BIOLOGICAL SCIENCES↗

Many-Exciton Quantum Dynamics in a Ruddlesden–Popper Tin Iodide

We present a study on the many-body exciton interactions in a Ruddlesden–Popper tin halide, namely, (PEA) 2 SnI 4 (PEA = phenylethylammonium), using coherent two-dimensional electronic spectroscopy. The optical dephasing times of the third-order polarization observed in these systems are determined by exciton many-body interactions and lattice fluctuations. We investigate the excitation-induced dephasing (EID) and observe a significant reduction of the dephasing time with increasing excitation density as compared to its lead counterpart (PEA) 2 PbI 4 , which we have previously reported in a separate publication [J. Chem. Phys. 2020, 153, 164706]. Surprisingly, we find that the EID interaction parameter is four orders of magnitude higher in (PEA) 2 SnI 4 than that in (PEA) 2 PbI 4 . This increase in the EID rate may be due to exciton localization arising from a more statically disordered lattice in the tin derivative. This is supported by the observation of multiple closely spaced exciton states and the broadening of the linewidth with increasing population time (spectral diffusion), which suggests a static disordered structure relative to the highly dynamic lead-halide. Additionally, we find that the exciton nonlinear coherent lineshape shows evidence of a biexcitonic state with low binding energy (<10 meV) not observed in the lead system. We model the lineshapes based on a stochastic scattering theory that accounts for the interaction with a nonstationary population of dark background excitations. Our study provides evidence of differences in the exciton quantum dynamics between tin- and lead-based Ruddlesden–Popper metal halides (RPMHs) and links them to the exciton–exciton interaction strength and the static disorder aspect of the crystalline structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

An Observational Comparison of Level of Neutral Buoyancy and Level of Maximum Detrainment in Tropical Deep Convective Clouds

Tropical deep convective clouds are important drivers of large-scale atmospheric circulation representing the main vertical transport pathway through the depth of the troposphere for heat, momentum, water, and chemical species. The strength and depth of this transport are impacted by the convective updraft size and intensity that are driven by buoyancy, dynamical forcing, and mixing of environmental air, i.e., entrainment. In this paper, we identify tropical deep convective systems with well-defined forward anvils using Atmospheric Radiation Measurement (ARM) ground-based profiling radars, at three ARM fixed-sites in the Tropical Western Pacific (TWP; i.e., Manus, Nauru, Darwin) and three ARM Mobile Facility deployments in Niamey, Niger; Gan Island, Maldives; and Manacapuru, Brazil. We use the difference between the level of neutral buoyancy (LNB) and the level of maximum detrainment (LMD) as a proxy for the effective bulk convective entrainment (εproxy). The LNB, the theoretical height that a parcel raised above the level of free convection would reach with no mixing, is calculated based on pre-convection radiosonde measurements using parcel theory. The LMD is the height of the maximum reflectivity observed in forward anvil clouds by profiling radars. Deep convective systems over the TWP show higher LNBs that extend to 16.3 km on average and larger εproxy (median LNB minus LMD up to 6.5 km) compared to their continental counterparts in the Amazon and West Africa. Oceanic conditions show larger convective available potential energy (CAPE) coupled with higher moisture at low levels which favors larger εproxy. In contrast, continental cases initiate and develop under high convective inhibition, steeper environmental lapse rate, and high wind shear conditions, which show smaller offset between LNB and LMD. Deep convective cases that promote significant cold pools at the surface experience less εproxy. Using a Random Forest regression algorithm, CAPE is associated with the highest feature importance score for predicting convective εproxy, followed by low-level relative humidity. For continental cases, the low-level wind shear also indicates higher importance.

54 ENVIRONMENTAL SCIENCES↗

Renormalization of states and quasiparticles in many-body downfolding

We explore the principles of many-body Hamiltonian complexity reduction via downfolding on an effective low-dimensional representation. We show that the renormalization factor provides a unique measure of the quality of the compression as it directly represents the projection between the approximate stationary state of the many-body Hamiltonian and the full many-body wavefunction. Hence, the renormalization factor is a measure of fidelity between the effective (reduced-rank) description and the full many-body treatment for arbitrary (i.e., ground and excited) states. When the entire problem is mapped on a system of interacting quasiparticles [Romanova et al., npj Comput. Mater. 9, 126 (2023)], the effective Hamiltonians can faithfully reproduce the physics only when a clear energy scale separation exists between the subsystems and their environment. We also demonstrate that it is necessary to include quasiparticle renormalization at distinct energy scales, capturing the distinct interaction between subsystems and their surrounding environments. Numerical results from simple, exactly solvable models highlight the limitations and strengths of this approach, particularly for ground and low-lying excited states. This work lays the groundwork for applying dynamical downfolding techniques to problems concerned with (quantum) interfaces.

Green-functions technique↗

The mechanics of plant morphogenesis

Understanding the mechanism by which patterned gene activity leads to mechanical deformation of cells and tissues to create complex forms is a major challenge for developmental biology. Plants offer advantages for addressing this problem because their cells do not migrate or rearrange during morphogenesis, which simplifies analysis. We synthesize results from experimental analysis and computational modeling to show how mechanical interactions between cellulose fibers translate through wall, cell, and tissue levels to generate complex plant tissue shapes. Genes can modify mechanical properties and stresses at each level, though the values and pattern of stresses differ from one level to the next. Here, the dynamic cellulose network provides elastic resistance to deformation while allowing growth through fiber sliding, which enables morphogenesis while maintaining mechanical strength.

59 BASIC BIOLOGICAL SCIENCES↗

A Multi-Phase Modeling Framework Suitable for Dynamic Applications

Under dynamic loading conditions and the associated extreme conditions many metals will undergo phase transformations. The change in crystal structure associated with solid–solid phase transformations can significantly alter the subsequent mechanical response of the material. For the interpretation of experiments involving dynamic loading it is beneficial to have a modeling framework that captures key features of the material response while remaining relatively simple. We introduce a candidate framework and apply it to the metal tin to highlight a range of behaviors that are captured by the model. We also discuss potential extensions to capture additional behaviors that could be important for certain materials and loading scenarios. The model is useful for analysis of results from dynamic experiments and offers a point of departure for more complex model formulations.

36 MATERIALS SCIENCE↗

Coal-based Bricks & Blocks (CBBs): Process Development to Prototype Fabrication Coupled with Techno-Economic Analysis and Market Survey

The decline of coal use for energy production provides an abundance of local feedstock for new innovative uses and value-added products. Expanding the U.S. coal-value chain to manufacture high-value carbon products can strengthen the nation’s energy and mineral security, enhance the U.S. national defense security, increase the United States’ economic prosperity, while achieving U.S. environmental objectives. The primary project goal is a relatively light-weight composite product with superior (or comparable) compressive strength. Both virgin and, where available, post-consumer recycled thermoplastic versions are tested for each thermoplastic species. A useful attribute of thermoplastics as binders is that they can be heated to their melting point, cooled, and reheated again without significant degradation. A key advantage of thermoset CBBs is that they require only mixing and molding. CBB advantages include low cost, availability, binding ability and processability. Coal-based bricks and blocks (CBBs) weigh about 50% less than clay bricks and can be manufactured with an interlocking design to promote ease of use for the novice builder. CBB formulation is evaluated according to a design-of-experiments (DoE) approach. DoE variables are a) relative weight fractions of binder, b) relative proportions of large versus small (milled) anthracite size fractions, and c) additive percentage. Fabrication methods include hot-press molding and extrusion, the later being the most commercially viable. CBBs are tested for compressive strength, modulus of rupture (by flexure test) and water absorption per ASTM C67, with density determined by the Archimedes drainage method. Fractured interfaces are examined by SEM (Scanning Electron Microscopy) to resolve fracture dynamics and interior microstructure uniformity. Differential scanning calorimetry (DSC) is used to compare plastic transition temperatures i.e., glass and melting temperatures to contrast virgin with post-consumer recycled thermoplastics and optimize their usage. These results are used in the DoE analysis to identify the binder and relative weight percentages for optimum strength, density, and porosity. Overall, CBBs possess strength comparable to clay-based bricks but are non-permeable and hydrophobic, and hence resistant to degradation by freeze-fracturing, corrosion, and efflorescence. The strongest composites have been made with the following thermoplastic binders (in order of strength): thermoset, high-density polyethylene crosslink resin, high-density polyethylene, nylon 6/6, and polypropylene. Results from a techno-economic analysis TEA show economy of scale for CBBs by modularization and reveal the binder as the cost driver for material costs. Ideally, the incorporation of post-consumer recycled thermoplastic will decrease material acquisition costs and increase product sustainability. Notably, CBBs do not require the high temperature calcination needed to produce cement, nor do they require firing in the 1600-2400 °F range for three days using natural gas, as do clay brick equivalents. Instead, CBBs are heated to a modest <600 °F according to the melt flow index of the thermoplastic binder. Existing anthracite mines can be expanded to produce CBBs to reduce aggregate transportation costs and emissions that exist for clay bricks. TEA reflects this reduced energy cost while a comparative CO2 emission analysis quantifies the reduced environmental footprint. The market survey identifies several commercialization opportunities, dependent upon the brick classification.

01 COAL, LIGNITE, AND PEAT↗

RAVEN Theory Manual

RAVEN is a software framework able to perform parametric and stochastic analysis based on the response of complex system codes. The initial development was aimed at providing dynamic risk analysis capabilities to the thermohydraulic code RELAP-7, currently under development at Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose stochastic and uncertainty quantification platform, capable of communicating with any system code. In fact, the provided Application Programming Interfaces (APIs) allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by input files or via python interfaces. RAVEN is capable of investigating system response and explore input space using various sampling schemes such as Monte Carlo, grid, or Latin hypercube. However, RAVEN strength lies in its system feature discovery capabilities such as: constructing limit surfaces, separating regions of the input space leading to system failure, and using dynamic supervised learning techniques. The development of RAVEN started in 2012 when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework arose. RAVEN’s principal assignment is to provide the necessary software and algorithms in order to employ the concepts developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just to identify the frequency of an event potentially leading to a system failure, but the proximity (or lack thereof) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. peak pressure in a pipe) is exceeded under certain conditions. Most of the capabilities, implemented having RELAP-7 as a principal focus, are easily deployable to other system codes. For this reason, several side activates have been employed (e.g. RELAP5-3D, any MOOSE-based App, etc.) or are currently ongoing for coupling RAVEN with several different software. The aim of this document is to provide a set of commented examples that can help the user to become familiar with the RAVEN code usage.

97 MATHEMATICS AND COMPUTING↗

An Engineered Multifunctional Composite for Passive Sensing, Power Harvesting, and In Situ Damage Identification with Enhanced Mechanical Performance

A multifunctional fiber-reinforced composite with passive self-sensing, energy-harvesting, and damage detection capabilities is presented. In this work, barium titanate piezoelectric microparticles are deposited on basalt fibers by a scalable, low-cost, environmentally friendly continuous feed-through process. The resulting composite derives a superior interlaminar shear strength from the microparticle-modified fiber–matrix interfaces. The composite also demonstrates passive self-sensing capabilities that produce electrical signals proportional to various dynamic loading events. Vibration and strain-controlled experiments are performed on composite beams to quantify the sensitivity and power output as a function of input acceleration and strain. Furthermore, these composite-generated electrical signals are used to identify in situ damage initiation for structural health monitoring to inform composite damage prior to structural failure. In brief, this truly multifunctional composite simultaneously displays a sensitivity of 0.5–2.6 mV g -1 at a resolution of 0.045–0.20g (g = gravitational acceleration), energy harvesting in the range of nW cc -1 , and prediction of early damage by exhibiting 0.017–1.17 mV peaks in voltage–time history profiles while assuring ≈20% improved interlaminar shear strength.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Thermal coupling-decoupling mechanism of heat transfer across van der Waals interfaces in n-eicosane

Progress toward improving thermal transport properties of n-eicosane, a promising phase change material for thermal energy storage near human body temperature, is hindered by lack of a general theoretical framework to model thermal interfacial conductance (TIC) of the van der Waals (vdW) force bonded molecular interfaces, which have little compositional/structural mismatch. Combining molecular dynamics simulations at temperatures up to the melting point and a time-domain analysis of interfacial heat currents in individual molecules, we unveil that (1) the heat flux across a molecular vdW interface has an alternating coupling-decoupling pattern at the single molecule level due to thermal fluctuations, and (2) the net heat transfer during the thermally coupled periods is orders of magnitude larger than that during the thermally decoupled periods. Furthermore, we demonstrate that a vdW interface's TIC correlates almost linearly with two atomistic-scale coupling-decoupling parameters: αc and σc, that represent the duration and strength of the thermal coupling, respectively. This thermal coupling-decoupling mechanism proposes a new paradigm that explicitly accounts for the effects of structural flexibility and dynamical fluctuation on heat transfer across molecular interfaces, and will likely provide new insights into modeling thermal transport properties in a wide-ranging soft materials, including self-assembled monolayers, paraffins or lipids.

42 ENGINEERING↗

Probing multiscale dissolution dynamics in natural rocks through microfluidics and compositional analysis

Mineral dissolution significantly impacts many geological systems. Carbon released by diagenesis, carbon sequestration, and acid injection are examples where geochemical reactions, fluid flow, and solute transport are strongly coupled. The complexity in these systems involves interplay between various mechanisms that operate at timescales ranging from microseconds to years. Current experimental techniques characterize dissolution processes using static images that are acquired with long measurement times and/or low spatial resolution. These limitations prevent direct observation of how dissolution reactions progress within an intact rock with spatially heterogeneous mineralogy and morphology. We utilize microfluidic cells embedded with thin rock samples to visualize dissolution with significant temporal resolution (100 ms) in a large observation window (3 × 3 mm). Here we injected acidic fluid into eight shale samples ranging from 8 to 86 wt % carbonate. The pre- and postreaction microstructures are characterized at the scale of pores (0.1 to 1 µm) and fractures (1 to 1,000 µm). We observe that nonreactive particle exposure, fracture morphology, and loss of rock strength are strongly dependent on both the relative volume of reactive grains and their distribution. Time-resolved images of the rock unveil the spatiotemporal dynamics of dissolution, including two-phase flow effects in real time and illustrate the changes in the fracture interface across the range of compositions. Moreover, the dynamical data provide an approach for characterizing reactivity parameters of natural heterogeneous samples when porous media effects are not negligible. The platform and workflow provide real-time characterization of geochemical reactions and inform various subsurface engineering processes.

58 GEOSCIENCES↗

Updated systematics of intermediate-energy single-nucleon removal cross sections

The body of experimental measurements of intermediate-energy reactions that remove a single nucleon from a secondary beam of neutron- or proton-rich nuclei continues to grow. These data have been analyzed consistently using an approximate, eikonal-model treatment of the reaction dynamics combined with appropriate shell-model descriptions of the projectile initial state, the bound final states spectrum of the reaction residue, and single-particle removal strengths computed from their wave-function overlaps. Here, the systematics of the ratio R s of the measured inclusive cross section to all bound final states and the calculated cross section to bound shell-model states—in different regions of the nuclear chart and involving both very weakly bound and strongly bound valence nucleons—is important in relating the empirically deduced orbital occupancies to those from the best available shell-model predictions. Importantly, several new higher-energy measurements, for which the sudden-approximation aspect of the dynamical description is placed on an even stronger footing, now supplement the previously analyzed measurements. These additional data sets are discussed. Their R s values are shown to conform to and reinforce the earlier-observed systematics, with no indication that the approximately linear reduction in R s with increasing nucleon separation energy is a consequence of a breakdown of the sudden approximation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Anharmonic Cation–Anion Coupling Dynamics Assisted Lithium‐Ion Diffusion in Sulfide Solid Electrolytes

Abstract Sulfide‐based lithium superionic conductors often show higher Li‐ion conductivity than other types of electrolyte materials. This work unveils a unique Li‐ion conductive behavior in these materials through the perspective of anharmonic coupling assisted Li‐ion diffusion. Li hopping events can happen simultaneously with various types of lattice dynamics, while only a statistically important synchronization of motions may indicate coupling. This method enables a direct evaluation of the coupling strength between these motions, which more fundamentally decides if a specific type of lattice motion is really anharmonically coupled to the Li hopping event and whether the coupling can facilitate the Li diffusion. By a new ab initio computational approach, this work unveils a unique phenomenon in prototype sulfide electrolytes in comparison with typical halide ones, that Li‐ion conduction can be boosted by the anharmonic coupling of low‐frequency Li phonon modes with high‐frequency anion stretching or flexing phonon modes, rather than the low‐frequency rotational modes. The coupling pushes Li ions toward the diffusion channels for reduced diffusion barriers. The result from the lower temperature range (≈0–300 K) of simulation can also be more relevant to the application of solid‐state batteries.

Chemistry↗

Effects of rejuvenation modes on the microstructures and mechanical properties of metallic glasses

This study systematically investigates the effects of three rejuvenation modes, namely biaxial loading, thermal pressure loading, and cryogenic thermal cycling, on the microstructures and mechanical properties of Cu 64 Zr 36 metallic glasses (MGs) using molecular dynamics (MD) simulations. The simulation results demonstrate that biaxial tension application during loading results in a rejuvenated glassy state with high strength. Specifically, we find that the yield stress, Young's modulus, and shear modulus of the rejuvenated glass obtained by biaxial tensile loading decreased by 2.8%, 2.1%, and 2.7%, respectively, compared to an as-cast MG reference. We also find that MGs achieve the greatest degree of rejuvenation, at the expense of strength, with the use of thermal pressure loading. Interestingly, the application of cryogenic thermal cycling results in a rejuvenated state with a lower strength drop compared to thermal pressure loading. Furthermore, analysis of the potential energy and medium-range-order (MRO) size indicates that the microstructures of MGs achieves a high-energy ordered glass state after thermal pressure loading, but a high-energy disordered glass state after biaxial tensile loading and cryogenic thermal cycling. Furthermore, these findings provide meaningful insights into the effect of rejuvenation modes on the microstructures and mechanical properties of MGs, as well as useful guidelines for designing MGs with high strength and superior plasticity for prospective applications in industry.

36 MATERIALS SCIENCE↗

Impacts of Latent Energy and Snow Fall Speed on a Wintertime Midlatitude Cyclone

This study aimed to understand the impacts of latent energy as well as snow fall speeds on precipitation properties and synoptic-scale storm characteristics of a wintertime midlatitude cyclone. Simulations of a potent winter storm that impacted the Rocky Mountains and northern Great Plains of the United States in February 2017 were performed using the Weather Research and Forecasting model with latent heating or cooling from individual microphysical processes systematically turned off and fall speeds adjusted by ±50%. Results indicated substantial impacts on the microphysical characteristics of the simulated storm to fall speed, cooling from sublimation, and warming from deposition. The impacts of cooling and warming were manifested as differences in accumulated snowfall. Increased (decreased) fall speeds led to smaller (larger) ice crystals and total mass, resulting in offsetting effects on the precipitation flux, and thus minimal impacts on snowfall and large-scale characteristics of the storm. Warming and cooling associated with deposition and sublimation, respectively, impacted the synoptic-scale dynamics, whereby removing warming from deposition resulted in an increased meridional temperature gradient near the jet stream, thus increasing the jet strength and causing it to be more westerly with less curvature aloft. This in turn limited upper-level divergence, creating a weaker surface low and shifting the precipitation shield southward. The opposite occurred with the removal of latent cooling due to sublimation. This study highlights the potential importance of latent energy associated with ice sublimation and deposition and fall speeds in the larger-scale characteristics of winter storms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Improving Seasonal Forecast Using Probabilistic Deep Learning

The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits relies on improving general circulation model (GCM) based dynamical forecast systems. To improve dynamical seasonal forecasts, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model initialization errors, formulation deficiencies, and internal climate variability. With huge costs in generating large forecast ensembles, and limited observations for forecast verification, the seasonal forecast benchmarking and diagnosing task proves challenging. Here, we develop a probabilistic deep learning-based statistical forecast methodology, drawing on a wealth of climate simulations to enhance seasonal forecast capability and forecast diagnosis. By explicitly modeling the internal climate variability and GCM formulation differences, the proposed Conditional Generative Forecasting (CGF) methodology enables bypassing crucial barriers in dynamical forecast, and offers a top-down viewpoint to examine how complicated GCMs encode the seasonal predictability information. We apply the CGF methodology for global seasonal forecast of precipitation and 2 m air temperature, based on a unique data set consisting 52,201 years of climate simulation. Results show that the CGF methodology can faithfully represent the seasonal predictability information encoded in GCMs. We successfully apply this learned relationship in real-world seasonal forecast, achieving competitive performance compared to dynamical forecasts. Using this CGF as benchmark, we reveal the impact of insufficient forecast spread sampling that limits the skill of the considered dynamical forecast system. Finally, we introduce different strategies for composing ensembles using the CGF methodology, highlighting the potential for leveraging the strengths of multiple GCMs to achieve advantgeous seasonal forecast.

54 ENVIRONMENTAL SCIENCES↗