Variable Representation Research Articles

Quantification of denitrification rate in shallow groundwater using the single-well, push-pull test technique.

Denitrification has been identified as a significant nitrate attenuation process in groundwater systems. Hence, accurate quantification of denitrification rates is consequently important for the better understanding and assessment of nitrate contamination of groundwater systems. There are, however, few studies that have investigated quantification of shallow groundwater denitrification rates using different analytical approaches or assuming different kinetic reaction models. In this study, we assessed different analytical approaches (reactant versus product) and kinetic reaction (zero-order and first-order) models analysing observations from a single-well, push-pull tests to quantify denitrification rates in shallow groundwater at two sites in the Manawatū River catchment, Lower North Island of New Zealand. Shallow groundwater denitrification rates analysed using the measurements of denitrification reactant (nitrate reduction) and zero-order kinetic models were quantified at 0.42-1.07mgNL-1h-1 and 0.05-0.12mgNL-1h-1 at the Palmerston North (PNR) and Woodville (WDV) sites, respectively. However, using first-order kinetic models, the denitrification rates were quantified at 0.03-0.09h-1 and 0.002-0.012h-1 at the PNR and WDV sites, respectively. These denitrification rates based on the measurements of denitrification reactant (nitrate reduction) were quantified significantly higher (6 to 60 times) than the rates estimated using the measurements of denitrification product (nitrous oxide production). However, the denitrification rate quantified based on the nitrate reduction may provide representative value of denitrification characteristics of shallow groundwater systems. This is more so when lacking practical methods to quantify all nitrogen species (i.e., total N, organic N, nitrite, nitrate, ammoniacal N, nitrous oxide, nitric oxide, and nitrogen gas) in a push-pull test. While estimates of denitrification rates also differed depending on the kinetic model used, both a zero-order and a first-order model appear to be valid to analyse and estimate denitrification rate from push-pull tests. However, a discrepancy in estimates of denitrification rates using either reactant or product and using zero- or first-order kinetics models may have implications in assessment of nitrate transport and transformation in groundwater systems. This necessitates further research and analysis for appropriate measurements and representation of spatial and temporal variability in denitrification characteristics of the shallow groundwater system.

Application of Statistical Methods for the Characterization of Radon Distribution in Indoor Environments: A Case Study in Lima, Peru

This study evaluates the effectiveness of advanced statistical and geospatial methods for analyzing radon concentration distributions in indoor environments, using the district of San Martín de Porres, Lima, Peru, as a case study. Radon levels were monitored using LR-115 nuclear track detectors over three distinct measurement periods between 2015 and 2016, with 86 households participating. Detectors were randomly placed in various rooms within each household. Normality tests (Shapiro–Wilk, Anderson–Darling, and Kolmogorov–Smirnov) were applied to assess the fit of radon concentrations to a log-normal distribution. Additionally, analysis of variance (ANOVA) was used to evaluate the influence of environmental and structural factors on radon variability. Non-normally distributed data were normalized using a Box–Cox transformation to improve statistical assumptions, enabling subsequent geostatistical analyses. Geospatial interpolation methods, specifically Inverse Distance Weighting (IDW) and Kriging, were employed to map radon concentrations. The results revealed significant temporal variability in radon concentrations, with geometric means of 146.4 Bq·m−3, 162.3 Bq·m−3, and 150.8 Bq·m−3, respectively, across the three periods. Up to 9.5% of the monitored households recorded radon levels exceeding the safety threshold of 200 Bq·m−3. Among the interpolation methods, Kriging provided a more accurate spatial representation of radon concentration variability compared to IDW, allowing for the precise identification of high-risk areas. This study provides a framework for using advanced statistical and geospatial techniques in environmental risk assessment.

Pair approximating the action for molecular rotations in path integral Monte Carlo.

Typical path integral Monte Carlo approaches use the primitive approximation to compute the probability density for a given path. In this work, we develop the pair discrete variable representation (pair-DVR) approach to study molecular rotations. The pair propagator, which was initially introduced to study superfluidity in condensed helium, is naturally well-suited for systems interacting with a pairwise potential. Consequently, paths sampled using the pair action tend to be closer to the exact paths (compared to primitive Trotter paths) for such systems leading to convergence with less imaginary time steps. Our approach relies on using the pair factorization approach in conjunction with a discretized path integral ground state paradigm to study a chain of planar rotors interacting with a pairwise dipole interaction. We first use the Wigner-Kirkwood density expansion to analyze the asymptotics of the pair propagator in imaginary time. Then, we exhibit the utility of the pair factorization scheme via convergence studies comparing the pair and primitive propagators. Finally, we compute energetic and structural properties of this system including the correlation function and Binder ratio as functions of the coupling strength to examine the behavior of the pair-DVR method near criticality. The density matrix renormalization group results are used for benchmarking throughout.

Optical Properties of Thick TiO2-P25 Films.

In this study, TiO2-P25 films on FTO substrates were synthesized using the sol-gel process and studied using Variable Angle Spectroscopy Ellipsometry (VASE) to determine their optical constants and thickness. The measurements were carried out at room temperature in the wavelength range of (300-900) nm at incident angles varying from 55° to 70°. The resulting thicknesses were found to be around 1000 nm. A graded layer model, which allowed for accurate representation of the depth-dependent optical variations, was employed to model the properties of these TiO2-P25 films. This modeling approach provided deeper insights into the internal structure of the films, particularly how the graded structural characteristics impact the overall optical behavior. Understanding these depth-dependent variations is essential for optimizing the use of TiO2-P25 films in technologies such as solar cells and optical devices.

Path integral Monte Carlo in a discrete variable representation with Gibbs sampling: Dipolar planar rotor chain.

In this work, we propose a path integral Monte Carlo approach based on discretized continuous degrees of freedom and rejection-free Gibbs sampling. The ground state properties of a chain of planar rotors with dipole-dipole interactions are used to illustrate the approach. Energetic and structural properties are computed and compared to exact diagonalization and numerical matrix multiplication for N ≤ 3 to assess the systematic Trotter factorization error convergence. For larger chains with up to N = 100 rotors, Density Matrix Renormalization Group calculations are used as a benchmark. We show that using Gibbs sampling is advantageous compared to traditional Metropolis-Hastings rejection importance sampling. Indeed, Gibbs sampling leads to lower variance and correlation in the computed observables.

Modeling of Functionally Graded Material Shells

In this research, we propose an algorithm to study the buckling of thin Functionally Graded Material (FGM) shells, utilizing a novel implementation of the asymptotic numerical method (ANM). Our approach integrates a three-step process: representation of variables and loading conditions through a truncated Taylor series, discretization using the finite element method (FEM), and advancement via a continuation method. This method is particularly effective for tracking the solution curve through systematic matrix inversion and tolerance adjustments. By applying this robust algorithm within the framework of Kirchhoff-Love theory, we analyze how the properties of FGM shells vary smoothly from metal at the base to ceramic at the surface, crucial for applications in aeronautics and civil engineering where stability under load is paramount. The results are validated against the Abaqus industrial code, demonstrating the accuracy of our model. Furthermore, we detail the impact of the volume fraction index on the load-displacement behavior and structural deformations, providing valuable insights for enhancing the design and safety of these critical structures.

Copula Modeling and Uncertainty Propagation in Field‐Scale Simulation of CO2 Fault Leakage

AbstractSubsurface storage of is an important means to mitigate climate change, and the North Sea hosts considerable potential storage resources. To investigate the fate of over decades in vast reservoirs, numerical simulation based on realistic models is essential. Faults and other complex geological structures introduce modeling challenges as their effects on storage operations are subject to high uncertainty. We present a computational framework for forward propagation of uncertainty, including stochastic upscaling and copula representation of multivariate distributions for a storage site model with faults. The Vette fault zone in the Smeaheia formation in the North Sea is used as a test case. The stochastic upscaling method reduces the number of stochastic dimensions and the cost of evaluating the reservoir model. Copulas provide representation of dependent multidimensional random variables and a good fit to data, allow fast sampling and coupling to the forward propagation method via independent uniform random variables. The non‐stationary correlation within the upscaled flow functions are accurately captured by a data‐driven transformation model. The uncertainty in upscaled flow functions and other uncertain parameters are efficiently propagated to leakage estimates using numerical reservoir simulation of a two‐phase system of CO2 and brine. The expectations of leakage are estimated by an adaptive stratified sampling technique which effectively allocates samples in stochastic space. We demonstrate cost reduction compared to standard Monte Carlo of one or two orders of magnitude for simpler test cases, and factors 2–8 cost reduction for stochastic multi‐phase flow properties and more complex stochastic models.

White Matter-Gray Matter Correlation Analysis Based on White Matter Functional Gradient.

The spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signals of the brain's gray matter (GM) have been interpreted as representations of neural activity variations. In previous research, white matter (WM) signals, often considered noise, have also been demonstrated to reflect characteristics of functional activity and interactions among different brain regions. Recently, functional gradients have gained significant attention due to their success in characterizing the functional organization of the whole brain. However, previous studies on brain functional gradients have predominantly focused on GM, neglecting valuable functional information within WM. In this paper, we have elucidated the symmetrical nature of the functional hierarchy in the left and right brain hemispheres in healthy individuals, utilizing the principal functional gradient of the whole-brain WM while also accounting for gender differences. Interestingly, both males and females exhibit a similar degree of asymmetry in their brain regions, albeit with distinct regional variations. Additionally, we have thoroughly examined and analyzed the distribution of functional gradient values in the spatial structure of the corpus callosum (CC) independently, revealing that a simple one-to-one correspondence between structure and function is absent. This phenomenon may be associated with the intricacy of their internal structural connectivity. We suggest that the functional gradients within the WM regions offer a fresh perspective for investigating the structural and functional characteristics of WM and may provide insights into the regulation of neural activity between GM and WM.

Assessing the Reliability of Predicted Decadal Surface Temperatures in Southeast Asia

Climate predictions spanning 10-year periods, known as Decadal Climate Predictions (DCPs), have become an important aspect of the latest Coupled Model Intercomparison Project (CMIP6). These DCPs have the capability to capture the El Niño-Southern Oscillation (ENSO) phenomena, which affects heatwave frequency in Southeast Asia over years to decades. This research assesses the ability of six General Circulation Model (GCM) DCPs to predict surface temperature over the Southeast Asian region, using the dcpp-A hindcast as the main product. The metrics of Anomaly Correlation Coefficient (ACC) and Mean Error (ME) are employed to assess the model outputs, with 51 hindcast datasets spanning initial years from 1960 to 2010 and ERA5 reanalysis data serving as the reference. The evaluation reveals that DCP model skill varies across lead times and subregions, with no single model consistently outperforming the others. The highest correlation values are observed during the September-October-November (SON) season, and the ENSEMBLE model demonstrates the ability to increase correlation values compared to the individual DCP models. However, the ENSEMBLE approach is unable to effectively reduce ME values due to the contrasting errors among individual models. PBIAS metric aligns with the ME, consistently identifying similar areas of underestimation (mainland) and overestimation (maritime continent) across the models. Despite these challenges, the evaluation results highlight the potential of DCPs in predicting surface temperature variability for the Southeast Asian region over decadal periods, particularly in capturing ENSO-related signals. Further improvements in model initializations, internal variability representation, and bias reduction are necessary to enhance the utility of CMIP6 decadal predictions for heatwave preparedness and mitigation strategies in this vulnerable region.

Exploring New Algorithms for Molecular Vibrational Spectroscopy Using Physics-Informed Program Synthesis.

Inductive program synthesis (PS) has recently begun to emerge as a useful new approach to automatically generate algorithms in quantum chemistry, as demonstrated in recent applications to the vibrational Schrödinger equation for simple model systems with one or two degrees-of-freedom. Here, we report a new physics-informed approach to inductive PS that is more conducive to the generation of discrete variable representation algorithms for real molecular systems. The new framework ensures separability of the kinetic and potential operators and does not require an exact solution to compare synthesized algorithmic predictions with. Algorithms with a tridiagonal matrix structure are generated via a variational-based stochastic optimization procedure. Crucially, through an extensive testing procedure, we demonstrate that variationally synthesized algorithms perform just as well as those generated using a target function. Assuming a direct product representation of normal coordinates, these algorithms are applied to three triatomic molecules. In total, we identify a set of seven PS algorithms that accurately reproduce the vibrational spectra of H2O, NO2, and SO2, as predicted by Colbert-Miller and sine-DVR algorithms.

Gastritis over Gastrisus Sharp (Coleoptera: Staphylinidae: Xanthopygina): Resolving a major taxonomic impediment with phylogenomics

AbstractWith 334 species, the subtribe Xanthopygina forms a diverse and ecologically dominant component of the Neotropical beetle fauna, ranging from the southern United States south to Argentina. Species can be abundant in a variety of microhabitats and are frequently encountered in tropical forests. Although a number of morphologically distinct genera are now well defined and identifiable, the subtribe still suffers from several poorly defined genera with relatively generalized morphology. The worst of these is the genus Gastrisus Sharp, which has never had a morphological definition per se and has, over time, accumulated many morphologically disparate species. This nebulous concept of Gastrisus has further made it difficult to generically assign and describe new species. Here we assembled a phylogenomic dataset using anchored hybrid enrichment across a broad sample of Xanthopygina, including nearly all described genera, a representation of morphological variation within Gastrisus and a number of undescribed taxa we were unable to assign to a genus. Both maximum likelihood and coalescent analyses converged on a well resolved and stable topology for the subtribe, which will serve as a critical framework for continued taxonomic progress in Xanthopygina. Nine major lineages were identified, most congruent with previous work. The limits of Gastrisus were successfully identified, and the monophyletic core of the genus was recovered as sister to a redefined Nausicotus Sharp, which included Torobus syn. n. Several large species of Gastrisus were resolved as a clade of the Xanthopygus group and are here placed in Drepanagrios gen. n. An additional six new genera were discovered but will be described and treated in detail in future papers. In addition to Gastrisus, Phanolinus Sharp, Xenopygus Bernhauer, Phanolinopsis Scheerpeltz and Ocyolinus Sharp were recovered as paraphyletic, resulting in Elecatopselaphus syn. n. (=Phanolinus), the re‐validation of Leptodiastemus Bernhauer stat. ressur. and the redefinition of Xenopygus, Phanolinopsis and Ocyolinus. We propose Phanolinus scheerpeltzi nom. n. as a replacement name for Phanolinus peruvianus (Scheerpeltz 1972, nec Bernhauer 1917) (previously Elecatopselaphus).

The High-resolution Urban Meteorology for Impacts Dataset (HUMID) daily for the Conterminous United States

Many current gridded surface meteorological datasets are inadequate for quantifying near-surface spatiotemporal variability because they do not fully represent the impacts of land surface heterogeneity. Of note, explicit representation of the spatial structure and magnitude of local urban warming are usually lacking. Here we enhance the representation of spatial meteorological variability over urban areas in the conterminous United States (CONUS) by employing the High-Resolution Land Data Assimilation System (HRLDAS), which accounts for the fine-scale impacts of spatiotemporally varying land surfaces on weather. We also synthesize in situ meteorological data including local mesonets to create a 1 km grid spacing model-observation fusion product spanning 1981–2018 over the CONUS at daily temporal resolution. Daily maximum, minimum, and mean values for a variety of temperature estimates, humidity, and surface energy budget terms, among others, are included. This High-resolution Urban Meteorology for Impacts Dataset (HUMID) will be useful for studies examining spatial variability of near surface meteorology and the impacts of urban heat islands across many disciplines including epidemiology, ecology, and climatology.

A formulation of the fast multipole boundary element method applied to the analysis of anisotropic materials under body forces

This work presents a formulation of the boundary element method with fast multipole expansion (MECMP) applied to the analysis of anisotropic elastic materials subject to body forces. Integral equations are obtained using the Somigliana identity. Integrals are divided into near field and far field. Near field, when the source points and integration elements are close, are treated as in the standard boundary element method, that is, integrating along the element and considering the interaction between source points (nodes) and the elements. On the other hand, in the far field, when the source points and integration elements are far away, the fast multipole method is applied. In this case, the fundamental solution is expanded in a Laurent series and the node-to-node interaction is replaced by a cell-to-cell interaction. Cells are generated by a hierarchical decomposition of the domain using the quad-tree algorithm. Different fast multipole operations are used to take advantage of the hierarchical domain decomposition and expansions of the fundamental solutions. Influence matrices are never explicitly obtained and the matrix-vector product are carried out with linear complexity. The linear system is solved by an iterative method. A preconditioning matrix is used to reduce the number of iterations to obtain a result with an specified accuracy. Effectiveness and efficiency in solving large-scale problems are discussed. The treatment of problems involving body forces are taken into account by the utilization of the modified boundary condition method. This approach entails augmenting the boundary condition with a specific solution tailored to the problem. Following the solution of the linear system, the particular solution is subsequently subtracted from both displacements and tractions. Importantly, this procedural step obviates the need for generating additional vectors or matrices within the matrix equation. The formulation presented in this article is based on a representation of complex variables of the integrands, similar to the formulation previously developed for potential (scalar) problems. Validation is carried out by comparing the results obtained by the two formulations: the standard boundary element method and the boundary element method with fast multipole expansion. The influence of the number of terms of the series expansion in the calculation of fundamental solutions and influence matrices is analyzed. Numerical examples are presented to demonstrate the efficiency, accuracy, and potential of the boundary element method with fast multipole expansion to solve large-scale problems, i.e., with tens of thousands of degrees of freedom.

CMIP6 Models Underestimate Arctic Sea Ice Loss during the Early Twentieth-Century Warming, despite Simulating Large Low-Frequency Sea Ice Variability

Abstract The variability of Arctic sea ice extent (SIE) on interannual and multidecadal time scales is examined in 29 models with historical forcing participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) and in twentieth-century sea ice reconstructions. Results show that during the historical period with low external forcing (1850–1919), CMIP6 models display relatively good agreement in their representation of interannual sea ice variability (IVSIE) but exhibit pronounced intermodel spread in multidecadal sea ice variability (MVSIE), which is overestimated with respect to sea ice reconstructions and is dominated by model uncertainty in sea ice simulation in the subpolar North Atlantic. We find that this is associated with differences in models’ sensitivity to Northern Hemispheric sea surface temperatures (SSTs). Additionally, we show that while CMIP6 models are generally capable of simulating multidecadal changes in Arctic sea ice from the mid-twentieth century to present day, they tend to underestimate the observed sea ice decline during the early twentieth-century warming (ETCW; 1915–45). These results suggest the need for an improved characterization of the sea ice response to multidecadal climate variability in order to address the sources of model bias and reduce the uncertainty in future projections arising from intermodel spread. Significance Statement The credibility of Arctic sea ice predictions depends on whether climate models are capable of reproducing changes in the past climate, including patterns of sea ice variability which can mask or amplify the response to global warming. This study aims to better understand how latest-generation global climate models simulate interannual and multidecadal variability of Arctic sea ice relative to available observations. We find that models differ in their representation of multidecadal sea ice variability, which is overall larger than in observations. Additionally, models underestimate the sea ice decline during the period of observed warming between 1915 and 1945. Our results suggest that, to achieve better predictions of Arctic sea ice, the realism of low-frequency sea ice variability in models should be improved.

Toward Understanding Parametric Controls on Runoff Sensitivity to Climate in the Community Land Model: A Case Study Over the Colorado River Headwaters

AbstractCrucial to the assessment of future water security is how the land model component of Earth System Models partition precipitation into evapotranspiration and runoff, and the sensitivity of this partitioning to climate. This sensitivity is not explicitly constrained in land models nor the model parameters important for this sensitivity identified. Here, we seek to understand parametric controls on runoff sensitivity to precipitation and temperature in a state‐of‐the‐science land model, the Community Land Model version 5 (CLM5). Process‐parameter interactions underlying these two climate sensitivities are investigated using the sophisticated variance‐based sensitivity analysis. This analysis focuses on three snow‐dominated basins in the Colorado River headwaters region, a prominent exemplar where land models display a wide disparity in runoff sensitivities. Runoff sensitivities are dominated by indirect or interaction effects between a few parameters of subsurface, snow, and plant processes. A focus on only one kind of parameters would therefore limit the ability to constrain the others. Surface runoff exhibits strong sensitivity to parameters of snow and subsurface processes. Constraining snow simulations would require explicit representation of the spatial variability across large elevation gradients. Subsurface runoff and soil evaporation exhibit very similar sensitivities. Model calibration against the subsurface runoff flux would therefore constrain soil evaporation. The push toward a mechanistic treatment of processes in CLM5 have dampened the sensitivity of parameters compared to earlier model versions. A focus on the sensitive parameters and processes identified here can help characterize and reduce uncertainty in water resource sensitivity to climate change.

Inclusion of genital, sexual, and gender diversity in human reproductive teaching: impact on student experience and recommendations for tertiary educators.

Western societal norms have long been constrained by binary and exclusionary perspectives on matters such as infertility, contraception, sexual health, sexuality, and gender. These viewpoints have shaped research and knowledge frameworks for decades and led to an inaccurate and incomplete reproductive biology curriculum. To combat these deficiencies in reproductive systems-related education, our teaching team undertook a gradual transformation of unit content from 2018 to 2023, aiming to better reflect real diversity in human reproductive biology. This initiative involved intentional modifications, including clear use of pronoun self-identification by staff. We addressed the historical lack of representation of genital variation and helped students interrogate oversimplified reproductive biology binaries. A novel assignment was also introduced, prompting students to apply reproductive physiology knowledge to propose innovative assisted reproductive technology solutions for diverse demographics. The collective impact of these innovations had a positive effect on student learning. With improved lecture content and inclusive language, the proportion of inclusive group assignment topics chosen by students more than doubled in 2021. By 2022, coinciding with assessment topic changes, the percentage of inclusive assignments topics surpassed 50%. Further development of laboratory activities on intersex genital variation and genital modification raised further understanding of genital, sexual, gender, and cultural diversity. While implementing these changes posed challenges, pushing both staff and students out of their comfort zones at times, collaboration with relevant organizations and individuals with lived experience of queer identity proved integral. Ultimately, these relatively simple adjustments had a substantial impact on student experiences and appreciation for diversity.NEW & NOTEWORTHY We outline the teaching innovations that we have implemented to improve inclusion of diversity in reproductive biology and physiology contexts. This includes improved representation of genital, sexual, and gender diversity considerations in the curriculum. There is a critical need for these innovations as how we teach fundamentally shapes the understanding of our future medical and health professionals and researchers and thus influences the quality of future medical care and research.

A Compact, Coherent Representation of Stellar Surface Variation in the Spectral Domain

Time-varying inhomogeneities on stellar surfaces constitute one of the largest sources of radial velocity (RV) error for planet detection and characterization. We show that stellar variations, because they manifest on coherent, rotating surfaces, give rise to changes that are complex but usably compact and coherent in the spectral domain. Methods for disentangling stellar signals in RV measurements benefit from modeling the full domain of spectral pixels. We simulate spectra of spotted stars using starry and construct a simple spectrum projection space that is sensitive to the orientation and size of stellar surface features. Regressing measured RVs in this projection space reduces RV scatter by 60%–80% while preserving planet shifts. We note that stellar surface variability signals do not manifest in spectral changes that are purely orthogonal to a Doppler shift or exclusively asymmetric in line profiles; enforcing orthogonality or focusing exclusively on asymmetric features will not make use of all the information present in the spectra. We conclude with a discussion of existing and possible implementations on real data based on the presented compact, coherent framework for stellar signal mitigation.

BOATSv2: new ecological and economic features improve simulations of high seas catch and effort

Abstract. Climate change and industrial fishing are having profound effects on marine ecosystems. Numerical models of fish communities and their interaction with fishing can help assess the biogeochemical and socioeconomic dynamics of this coupled human–natural system and how it is changing. However, existing models have significant biases and do not include many processes known to be relevant. Here we describe an updated version of the BiOeconomic mArine Trophic Size-spectrum (BOATS) model for global fish and fishery studies. The model incorporates new ecological and economic features designed to ameliorate prior biases. Recent improvements include reduction of fish growth rates in iron-limited high-nutrient low-chlorophyll regions and the ability to simulate fishery management. Features added to BOATS here for the first time include (1) a separation of pelagic and demersal fish communities to provide an expanded representation of ecological diversity and (2) spatial variation of fishing costs and catchability for more realistic fishing effort dynamics. We also introduce a new set of observational diagnostics designed to evaluate the model beyond the boundary of large marine ecosystems (66 commonly adopted coastal ocean ecoregions). Following a multi-step parameter selection procedure, the updated BOATSv2 model shows comparable performance to the original model in coastal ecosystems, accurately simulating catch, biomass, and fishing effort, and markedly improves the representation of fisheries in the high seas, correcting for excessive high seas and deep-sea catches in the previous version. Improvements mainly stem from separating pelagic and demersal energy pathways, complemented by spatially variable catchability of pelagic fish and depth- and distance-dependent fishing costs. The updated model code is available for simulating both historical and future scenarios.

Increasing model resolution improves but overestimates global mid-depth circulation simulation

Increasing the spatial resolution in climate models has significantly improved the simulation of global upper-layer ocean circulation. However, the ability of high-resolution models to accurately reproduce mid-depth circulation, in terms of strength and direction, still remains uncertain. An analysis of 17 climate models with varying resolutions reveals that both low and high-resolution models depict weaker current speeds compared with observations. High-resolution models demonstrate improved simulations of current speed and flow direction, except in the Southern Ocean. The performance of high-resolution models in regions with strong currents is generally better than in regions with weak flows. Dynamically, increasing the model resolution enhances the representation of temporal variations in mid-depth circulation by effectively capturing mesoscale processes. However, this also results in an overestimation of their intensity by approximately 65% on average across the global ocean.

Introducing the MESMER-M-TPv0.1.0 module: spatially explicit Earth system model emulation for monthly precipitation and temperature

Abstract. Emulators of Earth system models (ESMs) are statistical models that approximate selected outputs of ESMs. Owing to their runtime efficiency, emulators are especially useful when large amounts of data are required, for example, for in-depth exploration of the emission space, for investigating high-impact low-probability events, or for estimating uncertainties and variability. This paper introduces an emulation framework that allows us to emulate gridded monthly mean precipitation fields using gridded monthly mean temperature fields as forcing. The emulator is designed as an extension of the Modular Earth System Model Emulator (MESMER) framework, and its core relies on the concepts of generalised linear models (GLMs). Precipitation at each (land) grid point and for each month is approximated as a multiplicative model with two factors. The first factor entails the temperature-driven precipitation response and is assumed to follow a gamma distribution with a logarithmic link function. The second factor is the residual variability in the precipitation field, which is assumed to be independent of temperature but may still possess spatial precipitation correlations. Therefore, the monthly residual field is decomposed into independent principal components and subsequently approximated and sampled using a kernel density estimation with a Gaussian kernel. The emulation framework is tested and validated using 24 ESMs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). For each ESM, we train on a single-ensemble member across scenarios and evaluate the emulator performance using simulations with historical and Shared Socioeconomic Pathways (SSP5-8.5) forcing. We show that the framework captures grid-point-specific precipitation characteristics, such as variability, trend, and temporal auto-correlations. In addition, we find that emulated spatial (cross-variable) characteristics are consistent with those of ESMs. The framework is also able to capture compound hot–dry and cold–wet extremes, although it systematically underestimates their occurrence probabilities. The emulation of spatially explicit coherent monthly temperature and precipitation time series is a major step towards a computationally efficient representation of impact-relevant variables of the climate system.