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Accurate prediction of seasonal variations in salinity is essential for assessing the health of estuarine environments. Traditional estuarine salinity models face challenges such as high computational demands and extensive data requirements. Here, we introduce a novel, reduced‐complexity model that computes seasonal variations in estuarine salinity based on three key inputs: river discharge, tidal water levels, and marine salinity. The model predicts the seasonal (monthly moving average) salinity at a given location using a single dimensionless variable that represents the ratio of freshwater discharge to tidally driven discharge. The model is validated using data from 11 estuaries globally, showing strong predictive performance for seasonal salinity time series in each estuary, with mean absolute errors (MAEs) of 2.5 ± 1.3 psu across all estuaries. Moreover, we also show that our reduced‐complexity model predicts seasonal estuarine salinity with comparable accuracy as a fully three‐dimensional Delft3D simulation in one estuary.

Reduced‐complexity models of river behavior that neglect much of the physics governing fluvial processes have become increasingly popular in recent years. However, previous studies have demonstrated that channel morphology simulated by such models can be both unrealistic and highly sensitive to model grid resolution. A new reduced‐complexity model is presented here and applied to simulate the development and migration of free alternate bars in straight channels. This model incorporates a simple new treatment of lateral flow redistribution driven by topographic steering but neglects the role of momentum conservation, secondary circulation, and factors such as spatial variability in bed roughness. This approach is shown to replicate many of the characteristics of alternate bars observed in individual laboratory experiments conducted by Lanzoni (2000) and to produce results that are in agreement with more generic relationships evident in a larger experimental data set presented previously by Ikeda (1984). Moreover, model results are shown to be independent of grid resolution and are largely consistent with those obtained using more sophisticated models based on the depth‐averaged form of the Navier‐Stokes equations. There remains a need to evaluate and refine model process parameterizations for a wider range of conditions (including those in natural channels) and to examine model behavior in situations involving variable channel width and curvature. However, the results presented here provide the first evidence that such reduced‐complexity models may be able to simulate the vertical and streamwise scaling of free bars in alluvial channels and represent the morphodynamic feedbacks that control their temporal evolution.

Reduced complexity climate models are useful tools for quantifying decision‐relevant uncertainties, given their flexibility, computational efficiency, and suitability for large‐ensemble frameworks necessary for statistical estimation using resampling techniques (e.g., Markov chain Monte Carlo). Here we document a new version of the simple, open‐source, global climate model Hector, coupled with a 1‐D diffusive heat and energy balance model (Diffusion Ocean Energy balance CLIMate model) and a sea level change module (Building blocks for Relevant Ice and Climate Knowledge) that also represents contributions from thermal expansion, glaciers and ice caps, and polar ice sheets. We apply a Bayesian calibration approach to quantify model uncertainties surrounding 39 model parameters with prescribed radiative forcing, using observational information from global surface temperature, thermal expansion, and other contributors to sea level change. We find the addition of thermal expansion as an observational constraint sharpens inference for the upper tail of posterior equilibrium climate sensitivity estimates (the 97.5 percentile is tightened from 7.1 to 6.6 K), while other contributors to sea level change play a lesser role. The thermal expansion constraint also has implications for probabilistic projections of global surface temperature (the 97.5 percentile for RCP8.5 2100 temperature decreases 0.3 K). Due to the model's parameterization of thermal expansion as an uncertain function of global ocean heat, we note a trade‐off between two ways of incorporating thermal expansion information: Ocean heat data provide a somewhat sharper equilibrium climate sensitivity estimate while thermal expansion data allow for constrained sea level projections. Plain Language Summary Global warming poses considerable climate risks, such as increasing sea level and temperature extremes. Constraining the upper bounds of these salient and decision‐relevant aspects of climate change can provide important information for assessing vulnerabilities and risk and adaptation planning. Simple climate models that are both flexible and computationally efficient can be constrained by historical observations to statistically estimate key uncertain climate parameters and characterize climate upper bounds. Previous studies have shown that statistical estimates of the global temperature response to atmospheric CO2 depend on both global surface temperature and ocean heat content observational constraints. Here we use the Hector simple climate model to statistically estimate the temperature response to CO2 using several different sets of observational constraints, including several contributors to sea level. We find that the inclusion of thermal expansion tightens estimates of the temperature response to atmospheric CO2 and the upper bounds of temperature projections, while other contributors to sea level play a lesser role. Key Points We document a version of the Hector climate model featuring a sea level component with expansion, polar land ice, and glacier contributions Our calibration approach examines the effect of constraints related to sea level on estimates of equilibrium climate sensitivity Including thermal expansion information in the calibration sharpens the upper tail of equilibrium climate sensitivity

Over the past two decades, comparative sequence analysis using codon-substitution models has been honed into a powerful and popular approach for detecting signatures of natural selection from molecular data. A substantial body of work has focused on developing a class of “branch-site” models which permit selective pressures on sequences, quantified by the ω ratio, to vary among both codon sites and individual branches in the phylogeny. We develop and present a method in this class, adaptive branch-site random effects likelihood (aBSREL), whose key innovation is variable parametric complexity chosen with an information theoretic criterion. By applying models of different complexity to different branches in the phylogeny, aBSREL delivers statistical performance matching or exceeding best-in-class existing approaches, while running an order of magnitude faster. Based on simulated data analysis, we offer guidelines for what extent and strength of diversifying positive selection can be detected reliably and suggest that there is a natural limit on the optimal parametric complexity for “branch-site” models. An aBSREL analysis of 8,893 Euteleostomes gene alignments demonstrates that over 80% of branches in typical gene phylogenies can be adequately modeled with a single ω ratio model, that is, current models are unnecessarily complicated. However, there are a relatively small number of key branches, whose identities are derived from the data using a model selection procedure, for which it is essential to accurately model evolutionary complexity.

Emissions of anthropogenic aerosols are rapidly changing, in amounts, composition and geographical distribution. In East and South Asia in particular, strong aerosol trends combined with high population densities imply high potential vulnerability to climate change. Improved knowledge of how near‐term climate and weather influences these changes is urgently needed, to allow for better‐informed adaptation strategies. To understand and decompose the local and remote climate impacts of regional aerosol emission changes, we perform a set of Systematic Regional Aerosol Perturbations (SyRAP) using the reduced‐complexity climate model FORTE 2.0 (FORTE2). Absorbing and scattering aerosols are perturbed separately, over East Asia and South Asia, to assess their distinct influences on climate. In this paper, we first present an updated version of FORTE2, which includes treatment of aerosol‐cloud interactions. We then document and validate the local responses over a range of parameters, showing for instance that removing emissions of absorbing aerosols over both East Asia and South Asia is projected to cause a local drying, alongside a range of more widespread effects. We find that SyRAP‐FORTE2 is able to reproduce the responses to Asian aerosol changes documented in the literature, and that it can help us decompose regional climate impacts of aerosols from the two regions. Finally, we show how SyRAP‐FORTE2 has regionally linear responses in temperature and precipitation and can be used as input to emulators and tunable simple climate models, and as a ready‐made tool for projecting the local and remote effects of near‐term changes in Asian aerosol emissions. Plain Language Summary Emissions of anthropogenic aerosols are rapidly changing, both in amounts, composition, and geographical distribution. Aerosol‐climate impacts follow patterns and time evolutions different to those from greenhouse gas‐driven surface warming, potentially enhancing climate risk. However, our understanding of these patterns and processes is still limited. In East and South Asia, strong aerosol trends and high population densities imply a high potential vulnerability to climate change. To allow for better‐informed adaptation strategies, there is an urgent need for improved knowledge of how near‐term climate and weather influences these changes. Here we perform a set of Systematic Regional Aerosol Perturbations (SyRAP) using the reduced‐complexity climate model FORTE 2 to decompose the climate impacts of regional aerosol emission changes. We developed a new functionality in the model, allowing for the ability to emulate the indirect aerosol effect—in isolation or in combination with aerosol radiation interactions. We investigate the separate roles of both light‐absorbing and ‐scattering aerosols, and the distinct impacts of emission perturbations in East versus South Asia. We find that SyRAP‐FORTE2 is able to reproduce the responses to Asian aerosol changes documented in the literature, and that it can help us decompose regional climate impacts of aerosols from the two regions. Key Points Removing emissions of absorbing aerosols over both East Asia and South Asia is projected to cause a local drying In certain subregions, the impact of SO4 on precipitation is strongly dependent on the background climate state Results show regionally linear responses in temperature and precipitation and can be used as input to emulators and simple climate models

Maxent, one of the most commonly used methods for inferring species distributions and environmental tolerances from occurrence data, allows users to fit models of arbitrary complexity. Model complexity is typically constrained via a process known as L 1 regularization, but at present little guidance is available for setting the appropriate level of regularization, and the effects of inappropriately complex or simple models are largely unknown. In this study, we demonstrate the use of information criterion approaches to setting regularization in Maxent, and we compare models selected using information criteria to models selected using other criteria that are common in the literature. We evaluate model performance using occurrence data generated from a known \"“true\"” initial Maxent model, using several different metrics for model quality and transferability. We demonstrate that models that are inappropriately complex or inappropriately simple show reduced ability to infer habitat quality, reduced ability to infer the relative importance of variables in constraining species' distributions, and reduced transferability to other time periods. We also demonstrate that information criteria may offer significant advantages over the methods commonly used in the literature.

Balancing model complexity is a key challenge of modern computational ecology, particularly so since the spread of machine learning algorithms. Species distribution models are often implemented using a wide variety of machine learning algorithms that can be fine‐tuned to achieve the best model prediction while avoiding overfitting. We have released SDMtune, a new R package that aims to facilitate training, tuning, and evaluation of species distribution models in a unified framework. The main innovations of this package are its functions to perform data‐driven variable selection, and a novel genetic algorithm to tune model hyperparameters. Real‐time and interactive charts are displayed during the execution of several functions to help users understand the effect of removing a variable or varying model hyperparameters on model performance. SDMtune supports three different metrics to evaluate model performance: the area under the receiver operating characteristic curve, the true skill statistic, and Akaike's information criterion corrected for small sample sizes. It implements four statistical methods: artificial neural networks, boosted regression trees, maximum entropy modeling, and random forest. Moreover, it includes functions to display the outputs and create a final report. SDMtune therefore represents a new, unified and user‐friendly framework for the still‐growing field of species distribution modeling. The main innovations of SDMtune are a novel genetic algorithm to tune the hyperparameters of a model and functions to perform data‐driven variable selection. Real‐time and interactive charts are displayed during the execution of several functions to help users understand the effect of removing a variable or varying model hyperparameters on model performance.

Background Deep learning (DL) models are highly vulnerable to adversarial attacks for medical image classification. An adversary could modify the input data in imperceptible ways such that a model could be tricked to predict, say, an image that actually exhibits malignant tumor to a prediction that it is benign. However, adversarial robustness of DL models for medical images is not adequately studied. DL in medicine is inundated with models of various complexity—particularly, very large models. In this work, we investigate the role of model complexity in adversarial settings. Results Consider a set of DL models that exhibit similar performances for a given task. These models are trained in the usual manner but are not trained to defend against adversarial attacks. We demonstrate that, among those models, simpler models of reduced complexity show a greater level of robustness against adversarial attacks than larger models that often tend to be used in medical applications. On the other hand, we also show that once those models undergo adversarial training, the adversarial trained medical image DL models exhibit a greater degree of robustness than the standard trained models for all model complexities. Conclusion The above result has a significant practical relevance. When medical practitioners lack the expertise or resources to defend against adversarial attacks, we recommend that they select the smallest of the models that exhibit adequate performance. Such a model would be naturally more robust to adversarial attacks than the larger models.

AIM: Ecological niche models are increasingly being used to aid in predicting the effects of future climate change on species distributions. Complex models that show high predictive performance on current distribution data may do a poor job of predicting new data due to overfitting. In addition, model performance is often evaluated using techniques that are sensitive to spatial sampling bias. Here, we explore the effects of model complexity and spatial sampling bias on niche models for 90 vertebrate taxa of conservation concern. LOCATION: California, USA. METHODS: We used Akaike information criterion (AICc) to select variables and tune Maxent's built‐in regularization parameter (β) to constrain model complexity. In addition, we incorporated several estimates of spatial sampling bias based on interpolations of target group data. Ensemble forecasts were developed for future conditions from two emission scenarios and three climate change models for the year 2050. RESULTS: Reducing the number of predictors and tuning β resulted in a reduction in the number of parameters in models built with sample sizes greater than approximately 10 occurrence points. Reducing the number of predictors had a substantially higher impact on the relative prioritization of different grid cells than did increasing regularization. There was little difference in prioritization of habitat when comparing models built using different spatial sampling bias estimates. Over half of the taxa were predicted to experience >80% reductions in environmental suitability in currently occupied cells, and this pattern was consistent across taxonomic groups. MAIN CONCLUSIONS: Our results demonstrate that reducing the number of correlated predictor variables tends to decrease the breadth of models, while tuning regularization using AICc tends to increase it. These two strategies may provide a reasonable bracketing strategy for assessing climate change impacts.

Over the last four decades, sequential accumulation models for choice response times have spread through cognitive psychology like wildfire. The most popular style of accumulator model is the diffusion model (Ratcliff Psychological Review, 85 , 59–108, 1978 ), which has been shown to account for data from a wide range of paradigms, including perceptual discrimination, letter identification, lexical decision, recognition memory, and signal detection. Since its original inception, the model has become increasingly complex in order to account for subtle, but reliable, data patterns. The additional complexity of the diffusion model renders it a tool that is only for experts. In response, Wagenmakers et al. ( Psychonomic Bulletin & Review, 14 , 3–22, 2007 ) proposed that researchers could use a more basic version of the diffusion model, the EZ diffusion. Here, we simulate experimental effects on data generated from the full diffusion model and compare the power of the full diffusion model and EZ diffusion to detect those effects. We show that the EZ diffusion model, by virtue of its relative simplicity, will be sometimes better able to detect experimental effects than the data–generating full diffusion model.