Dynamic Prediction Research Articles

Casting a sustainable future: a study on dynamic prediction and influencing factors of economic resilience in fisheries management

Marine fisheries are a critical component of the marine economy. Examining the changes in the economic resilience of marine fisheries allows for identifying potential development risks and supports informed decision-making to optimize growth patterns and mitigate risks. This study applied the CRITIC-based combined weights model to calculate China’s Marine Fisheries Economic Resilience (MFER). It investigated the temporal and spatial evolution of MFER and analyzed the influencing factors using the Geographical Detector and a Two-way fixed-effects model. Furthermore, the future development trends of MFER were predicted using the Markov chain. The results show that the overall MFER in China’s coastal provinces increased steadily from 2001 to 2020. Second, MFER exhibits a clear pattern, with low-value regions improving and high-value areas shifting toward medium- and low-value provinces. Fourth, the trajectory of the MFER center is moving from north to south, with high-value regions expanding in the same direction. Fourth, factors such as EDL, TEC, SPE, and SYS positively impact MFER, while OPE has a negative effect. Finally, the results of the Markov chain indicate that the current development state of MFER is relatively stable, with evidence of club convergence. However, the probability of maintaining the current development level is gradually decreasing. Based on these findings, provide suggestions for the preservation of marine economic security.

Assessment and Dynamic Prediction of Green Space Ecological Service Value in Guangzhou City, China

As an important part of the urban ecosystem, urban green space provides a variety of ecosystem services, including climate regulation, soil conservation, carbon sink and oxygen release, and biodiversity protection. However, existing remote sensing evaluation methods for ecological service value lack the evaluation indicators of ecosystem service value for Guangzhou, China, and the evaluation method depends on the land cover type. Based on remote sensing technology and random forest algorithm, this study addresses these gaps by integrating remote sensing technology with a random forest algorithm to enhance the accuracy and rationality of ESV assessments. Focusing on Guangzhou, China, we improved the ecological service value evaluation system and conducted dynamic predictions based on land-use change scenarios. Our results indicate that the total ESV of Guangzhou’s green space was USD 7.323 billion in 2020, with a projected decline to USD 6.496 billion by 2030, representing a 12.37% reduction due to urbanization-driven land-use changes. This research highlights the noticeable role of green spaces in urban sustainability and provides robust, data-driven insights for policymakers to design more effective green space protection and management strategies. The improved assessment framework offers a novel approach for accurately quantifying urban ecosystem services and predicting future trends.

Early Viral Dynamics Predict HIV Post-Treatment Control After Analytic Treatment Interruption

Abstract Background A key research priority for developing an HIV cure strategy is to define the viral dynamics and biomarkers associated with sustained post-treatment control. The ability to predict the likelihood of sustained post-treatment control or non-control could minimize the time off antiretroviral therapy (ART) for those destined to not control and anticipate longer periods off ART for those destined to control. Methods Mathematical modeling and machine learning were used to characterize virologic predictors of long-term virologic control using viral kinetics data from several studies in which participants interrupted ART. Predictors of post-ART outcomes were characterized using data accumulated from the time of treatment interruption, replicating real-time data collection in a clinical study, and classifying outcomes as either post-treatment control (plasma viremia ≤400 copies/mL at 2 of 3 time points for ≥24 weeks) or non-control. Results Potential predictors of virologic control were the time to rebound, the rate of initial rebound, and the peak plasma viremia. We found that people destined to be non-controllers could be identified within 3 weeks of rebound (prediction scores: accuracy, 80%; sensitivity, 82%; specificity, 71%). Conclusions Given the widespread use of analytic treatment interruption in cure-related trials, these predictors may be useful to increase the safety of analytic treatment interruption through the early identification of people who are unlikely to become post-treatment controllers.

Characterization and prediction of hydraulic properties of traffic-compacted forest soils based on soil information and traffic treatments

Key messageThe hydraulic properties of compacted and rutted soils were evaluated through in-situ infiltration experiments and predicted based on soil texture class and traffic treatments. A significant decrease in saturated soil water content and soil hydraulic conductivity at saturation was observed. The resulting soil hydraulic parameters, when integrated into a soil water transfer model, effectively simulated water dynamics in these impacted forest soils, providing a crucial first step toward developing decision support tools for real-time trafficability. This approach can assist forest managers in minimizing the extent of soil compaction.ContextTo overcome trafficability issues of forest soils induced by heavy logging machinery, planning support tools are needed to determine suitable soil moisture conditions for traffic.AimsThis study aimed to identify the soil properties that differ significantly between undisturbed and compacted soils and to provide several estimation tools to predict the hydraulic properties of compacted soils beneath the skid trails.MethodsFour hundred seventeen water infiltration tests were conducted on 19 forest sites, mostly in North-eastern France, and analysed with the BEST method to estimate the hydraulic properties of the skid trails and undisturbed soils. The hydraulic properties of the skid trails were predicted thanks to linear mixed effect models using a bulk treatment effect, a site effect, or a skid trail degradation score. The predicted hydraulic properties were tested using a water flow model to assess their relevance regarding the prediction of water dynamics in skid trails.ResultsThe compaction effect was only significant for the logarithm of the hydraulic conductivity at saturation (log10(Ksat)) and the soil water content at saturation (θsat). For the skid trails, θsat was reduced by - 0.02 and − 0.11 m3m−3 in the 0 − 10 cm and 15 − 25 cm layers respectively, compared to undisturbed topsoil (0 − 10 cm). log10(Ksat) was reduced by − 0.38 and − 0.85 for skid trails in the 0 − 10 and 15 − 25 cm soil layers respectively, compared to undisturbed topsoil. The use of a pedotransfer function, in replacement of water infiltration tests, and their combination with the same correction coefficients proved to efficiently simulate the difference in water dynamics between skid trails and undisturbed forest soils.ConclusionEstimation of soil hydraulic properties based on in situ water infiltration experiments proved efficient to simulate water dynamics in compacted and rutted forest soils. Yet, further studies are needed to identify the most adapted pedotransfer function to forest soils and to test the generalisation of our findings in different conditions, especially deeply rutted soils (rut depths > 12 cm).

Dynamic Adaptability and Prediction Optimization in Power Material Supply Chains Using XGBoost

Dynamic Adaptability and Prediction Optimization in Power Material Supply Chains Using XGBoost

Multi-task Bayesian model combining FDG-PET/CT imaging and clinical data for interpretable high-grade prostate cancer prognosis.

We propose a fully automatic multi-task Bayesian model, named Bayesian Sequential Network(BSN), for predicting high-grade(Gleason 8) prostate cancer(PCa) prognosis using pre-prostatectomy FDG-PET/CT images and clinical data. BSN performs one classification task and five survival tasks: predicting lymph node invasion (LNI), biochemical recurrence-free survival (BCR-FS), metastasis-free survival, definitive androgen deprivation therapy-free survival, castration-resistant PCa-free survival, and PCa-specific survival(PCSS). Experiments are conducted using a dataset of 295patients. BSN outperforms widely used nomograms on all tasks except PCSS, leveraging multi-task learning and imaging data. BSN also provides automated prostate segmentation, uncertainty quantification, personalized feature-based explanations, and introduces dynamic predictions, a novel approach that relies on short-term outcomes to refine long-term prognosis. Overall, BSN shows great promise in its ability to exploit imaging and clinicopathological data to predict poor outcome patients that need treatment intensification with loco-regional or systemic adjuvant therapy for high-risk PCa.

AI-integrated network for RNA complex structure and dynamic prediction

RNA complexes are essential components in many cellular processes. The functions of these complexes are linked to their tertiary structures, which are shaped by detailed interface information, such as binding sites, interface contact, and dynamic conformational changes. Network-based approaches have been widely used to analyze RNA complex structures. With their roots in the graph theory, these methods have a long history of providing insight into the static and dynamic properties of RNA molecules. These approaches have been effective in identifying functional binding sites and analyzing the dynamic behavior of RNA complexes. Recently, the advent of artificial intelligence (AI) has brought transformative changes to the field. These technologies have been increasingly applied to studying RNA complex structures, providing new avenues for understanding the complex interactions within RNA complexes. By integrating AI with traditional network analysis methods, researchers can build more accurate models of RNA complex structures, predict their dynamic behaviors, and even design RNA-based inhibitors. In this review, we introduce the integration of network-based methodologies with AI techniques to enhance the understanding of RNA complex structures. We examine how these advanced computational tools can be used to model and analyze the detailed interface information and dynamic behaviors of RNA molecules. Additionally, we explore the potential future directions of how AI-integrated networks can aid in the modeling and analyzing RNA complex structures.

Reducing uncertainty with iterative model updating parses effects of competition and environment on salamander occupancy.

Making timely management decisions is often hindered by uncertainty. Monitoring reduces two key types of uncertainty. First, it serves to reduce structural uncertainty of how the system works and provides support for expectations of how a system works. Second, it serves to reduce parametric uncertainty of the drivers of system dynamics. By combining monitoring data and quantitative models, we can reduce structural and parametric uncertainty. To demonstrate this, we focus on the Shenandoah salamander (Plethodon shenandoah), a United States Federally Endangered Species. Early work suggested that P. shenandoah extinction risk results from competition with a conspecific (Plethodon cinereus). However, more recent work has found equivocal support for this claim, instead suggesting that abiotic factors, such as moisture and temperature, drive P. shenandoah persistence. Using long-term monitoring data, we find that while competition may play a part in P. shenandoah extinction risk, measures of surface moisture are better predictors of occupancy dynamics. Further, we find decreased detection rates of P. shenandoah when P. cinereus is present, suggesting a conflation of detection probability with actual competition, which cautions against making inference from unadjusted observations of occurrence. Using multiple lines of inquiry allows for more robust understanding of system drivers in the face of high uncertainty, increasing opportunities to manage extinction risk.

Spike and Slab Regression for Nonstationary Gaussian Linear Mixed Effects Modeling of Rapid Disease Progression

ABSTRACTSelect measures of social and environmental determinants of health (referred to as “geomarkers”), predict rapid lung function decline in cystic fibrosis (CF), defined as a prolonged decline relative to patient and/or center‐level norms. The extent to which hyper‐localization, defined as increasing the spatiotemporal precision of geomarkers, aids in prediction of rapid lung decline remains unclear. Linear mixed effects (LME) models with specialized covariance functions have been used for predicting rapid lung function decline, but there are few options to properly incorporate spatial correlation into the covariance functions while inducing simultaneous variable selection. Our innovative Bayesian model uses a spike and slab prior for simultaneous variable selection and offers additional advantages when coupled with nonstationary Gaussian LME modeling. This model also incorporates spatial correlation through an additional random effect term that accounts for spatial correlation based on ZIP code distances. We validated the model with simulations and applied it to real CF data from a Midwestern CF Center. We demonstrate how a combination of demographic, clinical, and geomarker variables can be selected as optimal predictors using Bayesian false discovery rate controlling rule. Our results indicate that incorporating spatiotemporal effects and geomarkers into this novel Bayesian stochastic LME model enhances the dynamic prediction of rapid CF disease progression.

Liver stiffness as a dynamic predictor of decompensation and mortality in alcohol-related liver disease

Liver stiffness as a dynamic predictor of decompensation and mortality in alcohol-related liver disease

Bidirectional dynamic frame prediction network for total-body [68Ga]Ga-PSMA-11 and [68Ga]Ga-FAPI-04 PET images

PurposeTotal-body dynamic positron emission tomography (PET) imaging with total-body coverage and ultrahigh sensitivity has played an important role in accurate tracer kinetic analyses in physiology, biochemistry, and pharmacology. However, dynamic PET scans typically entail prolonged durations (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\\ge\\:$$\\end{document}60 minutes), potentially causing patient discomfort and resulting in artifacts in the final images. Therefore, we propose a dynamic frame prediction method for total-body PET imaging via deep learning technology to reduce the required scanning time.MethodsOn the basis of total-body dynamic PET data acquired from 13 subjects who received [68Ga]Ga-FAPI-04 (68Ga-FAPI) and 24 subjects who received [68Ga]Ga-PSMA-11 (68Ga-PSMA), we propose a bidirectional dynamic frame prediction network that uses the initial and final 10 min of PET imaging data (frames 1–6 and frames 25–30, respectively) as inputs. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were employed as evaluation metrics for an image quality assessment. Moreover, we calculated parametric images (68Ga-FAPI: \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{K}_{1}$$\\end{document}, 68Ga-PSMA: \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{K}_{i}$$\\end{document}) based on the supplemented sequence data to observe the quantitative accuracy of our approach. Regions of interest (ROIs) and statistical analyses were utilized to evaluate the performance of the model.ResultsBoth the visual and quantitative results illustrate the effectiveness of our approach. The generated dynamic PET images yielded PSNRs of 36.056 ± 0.709 dB for the 68Ga-PSMA group and 33.779 ± 0.760 dB for the 68Ga-FAPI group. Additionally, the SSIM reached 0.935 ± 0.006 for the 68Ga-FAPI group and 0.922 ± 0.009 for the 68Ga-PSMA group. By conducting a quantitative analysis on the parametric images, we obtained PSNRs of 36.155 ± 4.813 dB (68Ga-PSMA, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{K}_{1}$$\\end{document}) and 43.150 ± 4.102 dB (68Ga-FAPI, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{K}_{i}$$\\end{document}). The obtained SSIM values were 0.932 ± 0.041 (68Ga-PSMA) and 0.980 ± 0.011 (68Ga-FAPI). The ROI analysis conducted on our generated dynamic PET sequences also revealed that our method can accurately predict temporal voxel intensity changes, maintaining overall visual consistency with the ground truth.ConclusionIn this work, we propose a bidirectional dynamic frame prediction network for total-body 68Ga-PSMA and 68Ga-FAPI PET imaging with a reduced scan duration. Visual and quantitative analyses demonstrated that our approach performed well when it was used to predict one-hour dynamic PET images. https://github.com/OPMZZZ/BDF-NET.

A Hybrid Data Assimilation and Dynamic Mode Decomposition Approach for Xenon Dynamic Prediction of Nuclear Reactor Cores

In this paper, a dynamic prediction scheme that combines the data assimilation method and dynamic mode decomposition (DMD) is brought out for the prediction of the whole-core power distribution under xenon oscillations within the HRP1000 reactor. The DMD is used to predict the power values over the nodes where in-core detectors exist, and predicted power is then extended to the whole core using data assimilation methodologies, e.g. the inverse distance–based data assimilation method. In the data assimilation stage, the selection of the background physical field and the regularization factor under different noise levels is investigated. A series of numerical experiments, based on the HPR1000 proof of feasibility of the coupling scheme, is conducted under low noise levels or low prediction step sizes. Finally, the optimal application conditions and the prediction performance of the coupling scheme in different noise levels are analyzed for practical engineering usage.

Synapses learn to utilize stochastic pre-synaptic release for the prediction of postsynaptic dynamics.

Synapses in the brain are highly noisy, which leads to a large trial-by-trial variability. Given how costly synapses are in terms of energy consumption these high levels of noise are surprising. Here we propose that synapses use noise to represent uncertainties about the somatic activity of the postsynaptic neuron. To show this, we developed a mathematical framework, in which the synapse as a whole interacts with the soma of the postsynaptic neuron in a similar way to an agent that is situated and behaves in an uncertain, dynamic environment. This framework suggests that synapses use an implicit internal model of the somatic membrane dynamics that is being updated by a synaptic learning rule, which resembles experimentally well-established LTP/LTD mechanisms. In addition, this approach entails that a synapse utilizes its inherently noisy synaptic release to also encode its uncertainty about the state of the somatic potential. Although each synapse strives for predicting the somatic dynamics of its postsynaptic neuron, we show that the emergent dynamics of many synapses in a neuronal network resolve different learning problems such as pattern classification or closed-loop control in a dynamic environment. Hereby, synapses coordinate themselves to represent and utilize uncertainties on the network level in behaviorally ambiguous situations.

Prediction and assessment of meteorological drought in southwest China using long short-term memory model

Abstract Drought prediction is crucial for mitigating risks and designing measures to alleviate its impact. Machine learning models have been widely applied in the field of drought prediction in recent years. This study concentrated on predicting meteorological droughts in southwest China, a region prone to frequent and severe droughts, particularly in areas with sparse meteorological station coverage. The long short-term memory (LSTM) predictive model, which is a deep learning model, was constructed by calculating standardized precipitation evapotranspiration index (SPEI) values based on 144 weather station observations from 1980 to 2020. The 5-fold cross-validation method was used for the hyperparameter optimization of the model. The LSTM model underwent comprehensive assessment and validation through multiple methods. This included the use of several accuracy assessment indicators and a comparison of results. The comparison covered different drought characteristics among the LSTM predictive model, the benchmark random forest (RF) predictive model, the historical drought situations, and the calculated SPEI values based on observations from 144 weather stations. The results showed that the training results of the LSTM predictive model basically agreed with the SPEI values calculated from weather station observations. The model-predicted variation trend of SPEI values for 2020 was similar to the variation in SPEI values calculated based on weather station observations. On the test set, the coefficient of determination (R 2), the root mean square error, the explained variance score, the Nash–Sutcliffe efficiency, and the Kling–Gupta efficiency were 0.757, 0.210, 0.802, 0.761, and 0.212, respectively. The total consistency rate of the drought grade was 59.26%. The spatial correlation distribution of SPEI values between LSTM model prediction and calculation from meteorological stations in 2020 was more than 0.5 for most regions. The correlation coefficients exceeded 0.6 in western Tibet and Chengdu Plains. Compared to the RF model, the LSTM model excelled in all five performance evaluation metrics and demonstrated a higher overall consistency rate for drought categories. The Kruskal–Wallis test for both the LSTM and RF models all indicated no significant difference in the distributions between the predicted and observed data. Scatter plots revealed that the prediction accuracy for both models in 2020 was suboptimal, with the SPEI showing a comparatively narrow range of values. Nonetheless, the LSTM model significantly outperformed the RF model in terms of prediction accuracy. In summary, the LSTM model demonstrated good overall performance, accuracy, and applicability. It has the potential to enhance dynamic drought prediction in regions with complex terrain, diverse climatic factors, and sparse weather station networks.

Multi-factor dynamic correlation prediction and analysis of carbon peaking for building sector: a case study of Shaanxi province

The factors influencing carbon emissions in the construction sector are numerous, and the relationships between these factors are complex. Previous studies on carbon peaking have often overlooked the dynamic changes between influencing factors and limited the number of variables to simplify the computation of predictive models. Based on the goal of carbon peaking, this study explores the relationships between internal factors within the construction industry and establishes a network of factor correlation. Furthermore, this network is embedded into an improved STIRPAT model, and a multi-factor dynamic correlation prediction model is constructed by incorporating scenario analysis. Taking Shaanxi Province, China, as a case for empirical analysis, the study explores carbon-peaking solutions for the building sector under different development scenarios. The findings indicate that carbon emissions in Shaanxi's building sector continuously increased during the study period, reaching 213 MtCO2 in 2020. Through factor screening, 12 driving factors were found to be significantly related to carbon emissions, all showing positive correlations, with the urbanization rate contributing the most to emissions. The dynamic association prediction model constructed had an accuracy of 0.996. Using this model, nine carbon emission scenarios were predicted, with optimizing the energy structure identified as the critical pathway, achieving a 5.01% reduction in emissions. A comprehensive strategy could achieve a 12.49% reduction and meet the carbon peaking target. Finally, the study proposes policy recommendations for the coordinated management of emissions reductions in cities and the construction industry, contributing to the development of sustainable cities and societies.

Prediction of chaotic dynamics and extreme events: A recurrence-free quantum reservoir computing approach

Prediction of chaotic dynamics and extreme events: A recurrence-free quantum reservoir computing approach

Dynamic mode decomposition of GRACE satellite data

Advancements in satellite technology yield environmental data with ever improving spatial coverage and temporal resolution. This necessitates the development of techniques to discern actionable information from large amounts of such data. We explore the potential of dynamic mode decomposition (DMD) to discover the dynamics of spatially correlated structures present in global-scale data, specifically in observations of total water storage anomalies provided by GRACE satellite missions. Our results demonstrate that DMD enables data compression and extrapolation from a reduced set of dominant spatiotemporal structures. The accuracy of its predictions of global system dynamics is preserved in its reconstruction of local time series. These findings suggest potential uses of DMD in analysis of remote-sensing data for hydrologic applications.

Modeling carbon dynamics from a heterogeneous watershed in the mid-Atlantic USA: A distributed-calibration and independent verification (DCIV) approach

The terrestrial ecosystem plays a vital role in regulating regional and global carbon budgets. Ecosystem models are extensively employed to estimate carbon fluxes across different spatial scales. However, there remains a need to reduce the uncertainties associated with model parameterization and input data. To address these limitations, we assessed a distributed-calibration and independent-verification (DCIV) approach that uses (1) remotely sensed net primary production (NPP) and evapotranspiration (ET) data from the Moderate Resolution Imaging Spectroradiometer (MODIS), (2) multi-site eddy covariance net ecosystem exchange (NEE) data; and (3) field sampling of soil organic carbon (SOC) and aboveground biomass (ABG) data to improve the overall predictability of carbon fluxes for the different land use and land cover (LULC) types at a watershed scale. The DCIV approach was applied to an advanced version of the Soil and Water Assessment Tool (SWAT)-Carbon (or SWAT-C), equipped with Century-based SOC algorithms to simulate carbon dynamics for watersheds with heterogeneous vegetation. The objective of the modeling effort was to assess carbon stocks and fluxes under different land management scenarios for a 3000-acre experimental farm and forest preserve in the northeastern United States. Our study showed that a large SOC stock of at least 100 tons ha−1 is stored under mixed forest, deciduous, shrubland, and floodplain (grass). Our study also showed that converting floodplain (grass) to deciduous forest has the potential to increase CO2 uptake (-NEE) by an order of three magnitude and ABG by 77 %, leading to an increased SOC stock of 23 % after twenty years. Similarly, we found that converting ungrazed grassland to grazed pasture leads to a non-statistically decreasing trend of SOC, especially in the 0–30 cm soil layer. Thus, the methodology used in this study can be applied to improve carbon dynamic prediction from a heterogeneous watershed at a regional scale.

Predictability of Severe Convective Storm Environments in Global Ensemble Forecast System, Version 12, Reforecasts

Abstract Prediction of severe convective storms at time scales of 2–4 weeks is of interest to forecasters and stakeholders due to their impacts on life and property. Prediction of severe convective storms on this time scale is challenging, since the large-scale weather patterns that drive this activity begin to lose dynamic predictability beyond week 1. Previous work related to severe convective storms on the subseasonal time scale has mostly focused on observed relationships with teleconnections. The skill of numerical weather prediction forecasts of convective-related variables has been comparatively less explored. In this study over the United States, a forecast evaluation of variables relevant in the prediction of severe convective storms is conducted using Global Ensemble Forecast System, version 12, reforecasts at lead times of up to 4 weeks. We find that kinematic and thermodynamic fields are predicted with skill out to week 3 in some cases, while composite parameters struggle to achieve meaningful skill into week 2. Additionally, using a novel method of weekly summations of daily maximum composite parameters, we suggest that the aggregation of certain variables may assist in providing additional predictability beyond week 1. These results should serve as a reference for forecast skill for the relevant fields and help inform the development of convective forecasting tools at time scales beyond current operational products. Significance Statement Prediction of severe weather beyond 1 week in advance is of interest to stakeholders given their impacts on life and property. In this study, we evaluate 20 years of forecast data generated by a numerical model ensemble to determine whether variables relevant in severe weather forecasts can be predicted in weeks 2–4. The variables with the best skill measures generally represent large-scale weather patterns that are more predictable on longer time scales, although some manipulation of other severe weather parameters yielded additional results. We suggest that the results found in this study can inform future work that assesses the predictability of severe weather in weeks 2–4 using more complex methods such as machine learning.

Aridity-Driven Change in Microbial Carbon Use Efficiency and Its Linkage to Soil Carbon Storage.

Global warming is generally predicted to increase aridity in drylands, while the effects of aridity changes on microbial carbon use efficiency (CUE) and its linkage to soil organic carbon (SOC) storage remain unresolved, limiting the accuracy of soil carbon dynamic predictions under changing climates. Here, by employing large-scale soil sampling from 50 sites along an ~6000 km aridity gradient in northern China, we report a significant decreasing trend in microbial CUE (ranging from approximately 0.07 to 0.59 across the aridity gradient) with increasing aridity. The negative effect of aridity on microbial CUE was further verified by an independent moisture manipulation experiment, which revealed that CUE was lower under lower moisture levels than under higher moisture levels. Aridity-induced increases in physicochemical protection or decreases in microbial diversity primarily mediated the decrease in CUE with increasing aridity. Moreover, we found a highly positive microbial CUE-SOC relationship, and incorporating CUE improved the explanatory power of SOC variations along the aridity gradient. Our findings provide empirical evidence for aridity-induced reductions in microbial CUE over a broad geographic scale and highlight that increasing aridity may be a crucial mechanism underlying SOC loss by suppressing the ability of soil microorganisms to sequester carbon.