Complex Spatial Variability Research Articles

Estimating Biogeochemical Rates Using a Computationally Efficient Lagrangian Approach

Nutrient concentrations in many estuaries have increased over the past century due to increases in wastewater discharge and increased agricultural intensity, contributing to multiple environmental problems. Numerous biogeochemical and physical processes in estuaries influence nutrient concentrations during transport, resulting in complex spatial and temporal variability and challenges identifying predominant processes and their rates. Mechanistic models which require these rates to quantify biogeochemical processes become complex and difficult to calibrate as the number of processes and parameters grows, owing to the high dimensionality of the parameter space and the computational cost of simultaneously modeling the transport and transformations of constituents. We developed a modeling approach that decouples transport from transformations, enabling fast, data-driven exploration of the parameter space. The approach extracted information including water age, cumulative exposure to specific habitats, and mean water depth exposure from a hydrodynamic model. Using this information, a biogeochemical model was implemented to predict ammonium and nitrate concentrations in a Lagrangian frame. The model performed each simulation in milliseconds on a laptop computer, allowing the fitting of rate parameters for key transformations by optimization. The optimization used fixed station nitrate observations and the model was then validated against high-resolution mapping observations of ammonium and nitrate. The results suggest that the observed spatial and temporal variation can be largely represented with five transformation processes and their associated rates. Dissolved inorganic nitrogen (DIN) losses occurred only in shallow vegetated areas in the model, highlighting that biogeochemical processes in these areas should be included in DIN models.

Mapping sub-surface distribution of soil organic carbon stocks in South Africa's arid and semi-arid landscapes: Implications for land management and climate change mitigation

Soil organic carbon (SOC) stocks are critical for land management strategies and climate change mitigation. However, understanding SOC distribution in South Africa's arid and semi-arid regions remains a challenge due to data limitations, and the complex spatial and sub-surface variability in SOC stocks driven by desertification and land degradation. Thus, to support soil and land-use management practices as well as advance climate change mitigation efforts, there is an urgent need to provide more precise SOC stock estimates within South Africa's arid and semi-arid regions. Hence, this study adopted remote-sensing approaches to determine the spatial sub-surface distribution of SOC stocks and the influence of environmental co-variates at four soil depths (i.e., 0-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm). Using two regression-based algorithms, i.e., Extreme Gradient Boosting (XGBoost) and Random Forest (RF), the study found the former (RMSE values ranging from 7.12 t/ha to 29.55 t/ha) to be a superior predictor of SOC in comparison to the latter (RMSE values ranging from 7.36 t/ha to 31.10 t/ha). Nonetheless, both models achieved satisfactory accuracy (R2 ≥ 0.52) for regional-scale SOC predictions at the studied soil depths. Thereafter, using a variable importance analysis, the study demonstrated the influence of climatic variables like rainfall and temperature on SOC stocks at different depths. Furthermore, the study revealed significant spatial variability in SOC stocks, and an increase in SOC stocks with soil depth. Overall, these findings enhance the understanding of SOC dynamics in South Africa's arid and semi-arid landscapes and emphasizes the importance of considering site specific topo-climatic characteristics for sustainable land management and climate change mitigation. Furthermore, the study offers valuable insights into sub-surface SOC distribution, crucial for informing carbon sequestration strategies, guiding land management practices, and informing environmental policies within arid and semi-arid environments.

The Spatio-Temporal Dynamics of Water Resources (Rainfall and Snow) in the Sierra Nevada Mountain Range (Southern Spain)

This paper describes the use of a unique spatio-temporally resolved precipitation and temperature dataset to assess the spatio-temporal dynamics of water resources over a period of almost seven decades across the Sierra Nevada mountain range, which is the most southern Alpine environment in Europe. The altitude and geographical location of this isolated alpine environment makes it a good detector of climate change. The data were generated by applying geostatistical co-kriging to significant instrumental precipitation and temperature (minimum, maximum and mean) datasets. The correlation between precipitation and altitude was not particularly high and the statistical analysis yielded some surprising results in the form of mean annual precipitation maps and yearly precipitation time series. These results confirm the importance of orographic precipitation in the Sierra Nevada mountain range and show a decrease in mean annual precipitation of 33 mm per decade. Seasonality, however, has remained constant throughout the period of the study. The results show that previous studies have overestimated the altitudinal precipitation gradient in the Sierra Nevada and reveal its complex spatial variability. In addition, the results show a clear correspondence between the mean annual precipitation and the NAO index and, to a much lesser extent, the WeMO index. With respect to temperature, there is a high correlation between minimum temperature and altitude (coefficient of correlation = −0.84) and between maximum temperature and altitude (coefficient of correlation = −0.9). Thus, our spatial temperature maps were very similar to topographic maps, but the temporal trend was complex, with negative (decreasing) and positive (increasing) trends. A dynamic model of snowfall can be obtained by using the degree-day methodology. These results should be considered when checking the local performance of climatological models.

Efficient 3D real-time adaptive AUV sampling of a river plume front

The coastal environment faces multiple challenges due to climate change and human activities. Sustainable marine resource management necessitates knowledge, and development of efficient ocean sampling approaches is increasingly important for understanding the ocean processes. Currents, winds, and freshwater runoff make ocean variables such as salinity very heterogeneous, and standard statistical models can be unreasonable for describing such complex environments. We employ a class of Gaussian Markov random fields that learns complex spatial dependencies and variability from numerical ocean model data. The suggested model further benefits from fast computations using sparse matrices, and this facilitates real-time model updating and adaptive sampling routines on an autonomous underwater vehicle. To justify our approach, we compare its performance in a simulation experiment with a similar approach using a more standard statistical model. We show that our suggested modeling framework outperforms the current state of the art for modeling such spatial fields. Then, the approach is tested in a field experiment using two autonomous underwater vehicles for characterizing the three-dimensional fresh-/saltwater front in the sea outside Trondheim, Norway. One vehicle is running an adaptive path planning algorithm while the other runs a preprogrammed path. The objective of adaptive sampling is to reduce the variance of the excursion set to classify freshwater and more saline fjord water masses. Results show that the adaptive strategy conducts effective sampling of the frontal region of the river plume.

High-resolution grids of daily air temperature for Peru - the new PISCOt v1.2 dataset

Gridded high-resolution climate datasets are increasingly important for a wide range of modelling applications. Here we present PISCOt (v1.2), a novel high spatial resolution (0.01°) dataset of daily air temperature for entire Peru (1981–2020). The dataset development involves four main steps: (i) quality control; (ii) gap-filling; (iii) homogenisation of weather stations, and (iv) spatial interpolation using additional data, a revised calculation sequence and an enhanced version control. This improved methodological framework enables capturing complex spatial variability of maximum and minimum air temperature at a more accurate scale compared to other existing datasets (e.g. PISCOt v1.1, ERA5-Land, TerraClimate, CHIRTS). PISCOt performs well with mean absolute errors of 1.4 °C and 1.2 °C for maximum and minimum air temperature, respectively. For the first time, PISCOt v1.2 adequately captures complex climatology at high spatiotemporal resolution and therefore provides a substantial improvement for numerous applications at local-regional level. This is particularly useful in view of data scarcity and urgently needed model-based decision making for climate change, water balance and ecosystem assessment studies in Peru.

County-Scale Spatial Distribution of Soil Nutrients and Driving Factors in Semiarid Loess Plateau Farmland, China

Characterized by a topography of thousands of ravines, the Loess Plateau has highly complex spatial variability in terms of soil nutrients. Therefore, it is of considerable importance to study the soil nutrient spatial distribution, driving factors of precise fertilizer management, and the strategic use of soil nutrient resources. In 2017, 242 soil samples were taken from the semiarid Anding district farming region in northern China. The spatial variability and factors influencing soil nutrients were studied using statistical and geostatistical methods. The results showed that the mean soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), available potassium (AK), and pH values were averaged at 12.64 g·kg−1, 0.84 g·kg−1, 23.20 mg·kg−1, 188.87 mg·kg−1, and 8.60, respectively. The nugget-to-sill ratios for the semi-variograms of SOM, TN, AP, and AK varied from 25.84 to 49.93%, while the coefficients of variation varied from 24.53 to 69.44%, revealing that all four indicators exhibited considerable variability, and that the samples’ geographical variability was produced by a combination of random and structural factors. Overall increasing trends were exhibited from the middle to the northeast and southwest in the distributions of SOM, TN, and AP. The spatial distribution of AK displayed the opposite trend, increasing from the southwest to north and southeast. The texture of the tillage layer was the main factor directly affecting SOM, and explained 8% of its variation. The distribution of TN was mainly influenced by the irrigation method and water source type. AP and AK contents differed significantly between the two parent materials, three textures, and three topography types at the level of p < 0.01. In conclusion, the regional soil fertility was poor, spatial heterogeneity was moderate, and influencing factors were complex, highlighting the need to adopt precise fertilization management and adopting land management measures according to the actual influencing factors of each nutrient, thereby contributing to the enhancement of regional fertility.

Long-term trends and wave climate variability in the South Atlantic Ocean: The influence of climate indices

Linking wave climate variability and trends with climate indices is important to better understand and predict large-scale patterns of wave variability down to wave conditions at the coast. This study investigates such links in the South Atlantic Ocean using 72 years of ERA5 wave hindcast. Different wave parameters are computed, including storm wave statistics, and are further analyzed in terms of long-term trends and interannual changes. Our results indicate that, over the last decades, wave height has been significantly increasing across the entire domain, while extreme events statistics are also increasing, although with more complex spatial variability. The variations of these wave properties are primarily correlated, from low to high latitudes, with the Atlantic Multidecadal Oscillation (AMO), Tropical Southern Atlantic Index (TSA) and Southern Annular Mode (SAM), with different preferred timescales. We think that better understanding and predicting the evolution of these climate indices, including under climate change, will be critical to anticipate coastal hazards in this region.

Remote Sensing Data for Digital Soil Mapping in French Research—A Review

Soils are at the crossroads of many existential issues that humanity is currently facing. Soils are a finite resource that is under threat, mainly due to human pressure. There is an urgent need to map and monitor them at field, regional, and global scales in order to improve their management and prevent their degradation. This remains a challenge due to the high and often complex spatial variability inherent to soils. Over the last four decades, major research efforts in the field of pedometrics have led to the development of methods allowing to capture the complex nature of soils. As a result, digital soil mapping (DSM) approaches have been developed for quantifying soils in space and time. DSM and monitoring have become operational thanks to the harmonization of soil databases, advances in spatial modeling and machine learning, and the increasing availability of spatiotemporal covariates, including the exponential increase in freely available remote sensing (RS) data. The latter boosted research in DSM, allowing the mapping of soils at high resolution and assessing the changes through time. We present a review of the main contributions and developments of French (inter)national research, which has a long history in both RS and DSM. Thanks to the French SPOT satellite constellation that started in the early 1980s, the French RS and soil research communities have pioneered DSM using remote sensing. This review describes the data, tools, and methods using RS imagery to support the spatial predictions of a wide range of soil properties and discusses their pros and cons. The review demonstrates that RS data are frequently used in soil mapping (i) by considering them as a substitute for analytical measurements, or (ii) by considering them as covariates related to the controlling factors of soil formation and evolution. It further highlights the great potential of RS imagery to improve DSM, and provides an overview of the main challenges and prospects related to digital soil mapping and future sensors. This opens up broad prospects for the use of RS for DSM and natural resource monitoring.

Spatial Prediction and Mapping of Soil Water Content by TPE-GBDT Model in Chinese Coastal Delta Farmland with Sentinel-2 Remote Sensing Data

Soil water content is an important indicator used to maintain the ecological balance of farmland. The efficient spatial prediction of soil water content is crucial for ensuring crop growth and food production. To this end, 104 farmland soil samples were collected in the Yellow River Delta (YRD) in China, and the soil water content was determined using the drying method. A gradient boosting decision tree (GBDT) model based on a tree-structured Parzen estimator (TPE) hyperparametric optimization was developed, and then the soil water content was predicted and mapped based on the soil texture and vegetation index from Sentinel-2 remote sensing images. The results of statistical analysis showed that the soil water content had a high coefficient of variation (55.30%), a non-normal distribution, and complex spatial variability. Compared with other models, the TPE-GBDT model had the highest prediction accuracy (RMSE = 6.02% and R2 = 0.71), and its mapping results showed that the areas with high soil water content were distributed on both sides of the river and near the estuary. Furthermore, the results of Shapley additive explanation (SHAP) analysis showed that the soil texture (PC2 and PC5), modified normalized difference vegetation index (MNDVI), and Sentinel-2 red edge position (S2REP) index provided important contributions to the spatial prediction of soil water content. We found that the hydraulic physical properties of soil texture and the vegetation characteristics (such as vegetation coverage, root action, and transpiration) are the key factors affecting the spatial migration and heterogeneity of the soil water content in the study area. The above results show that the TPE algorithm can quickly capture the hyperparameters that are most suitable for the GBDT model, so that the GBDT model can ensure prediction accuracy, reduce the loss function with less training data, and accurately learn of the nonlinear relationship between soil water content and environmental factors. This paper proposes a machine learning method for hyperparameter optimization that shows considerable potential to predict the spatial heterogeneity of soil water content, which can effectively support regional farmland soil and water conservation and high-quality agricultural development.

Technical note: Unsupervised classification of ozone profiles in UKESM1

Abstract. The vertical distribution of ozone in the atmosphere, which features complex spatial and temporal variability set by a balance of production, loss, and advection, is relevant for both surface air pollution and climate via its role in radiative forcing. At present, the way in which regions of coherent ozone structure are defined relies on somewhat arbitrarily drawn boundaries. Here we consider a more general, data-driven method for defining coherent regimes of ozone structure. We apply an unsupervised classification technique called Gaussian mixture modeling (GMM), which represents the underlying distribution of ozone profiles as a linear combination of multi-dimensional Gaussian functions. In doing so, GMM identifies coherent groups or subpopulations of the ozone profile distribution. As a proof-of-concept study, we apply GMM to ozone profiles from three subsets of the UKESM1 coupled climate model runs carried out for CMIP6: specifically, the seasonal mean of a historical subset (2009–2014) and two subsets from two different future climate projections (i.e., SSP1-2.6 and SSP5-8.5). Despite not being given any spatiotemporal information, GMM identifies several spatially coherent regions of ozone structure. Using a combination of statistical guidance and post hoc judgment, we select a six-class representation of global ozone, consisting of two tropical classes and four mid-to-high-latitude classes. The tropical classes feature a relatively high-altitude tropopause, while the higher-latitude classes feature a lower-altitude tropopause and low values of tropospheric ozone, as expected based on broad patterns observed in the atmosphere. Both of the future projections feature lower ozone concentrations at 850 hPa than the historical benchmark, with signatures of ozone hole recovery. We find that the area occupied by the tropical classes is expanded in both future projections, which are most prominent during austral summer. Our results suggest that GMM may be a useful method for identifying coherent ozone regimes, particularly in the context of model analysis.

Short-term variability of atmospheric helium revealed through a cryo-enrichment method

Abstract. Tropospheric helium variations are tightly linked to CO2 due to the co-emission of He and CO2 from natural-gas burning. Recently, Birner et al. (2022a) showed that the global consumption of natural gas has measurably increased the He content of the atmosphere. Like CO2, He is also predicted to exhibit complex spatial and temporal variability on shorter timescales, but measurements of these short-term variations are lacking. Here, we present the development of an improved gas delivery and purification system for the semi-continuous mass spectrometric measurement of the atmospheric He-to-nitrogen ratio (He/N2). The method replaces the chemical getter used previously by Birner et al. (2021, 2022a) to preconcentrate He in an air stream with a cryogenic trap which can be more simply regenerated by heating and which improves the precision of the measurement to 22 per meg (i.e., 0.022 ‰) in 10 min (1σ). Using this “cryo-enrichment” method, we measured the He/N2 ratios in ambient air at La Jolla (California, USA) over 5 weeks in 2022. During this period, He/N2 was strongly correlated with atmospheric CO2 concentrations, as expected from anthropogenic emissions, with a diurnal cycle of 450–500 per meg (max–min) caused by the sea–land breeze pattern of local winds, which modulates the influence of local pollution sources.

Simulation of Runoff through Improved Precipitation: The Case of Yamzho Yumco Lake in the Tibetan Plateau

Alpine lakes on the Tibetan Plateau have significantly changed under a changing climate over past decades. However, the changing patterns of the inflow sources of the lakes, i.e., rainfall and the melt water of snow and glaciers, and their response to climate change remain uncertain because obtaining accurate precipitation and melt water discharge is difficult due to the complex topography, spatial variability, and scarce stations of the alpine area. A distributed hydrological model, J2000, was employed in this study to simulate runoff component variations of the Yamzho Yumco Lake glaciated basin during 1974–2019. Except for observed daily runoff from two tributaries, a High Asia Refined (HAR) high-resolution reanalysis of precipitation data was combined with field precipitation gradient observation and snow cover area validation, all performed simultaneously to reduce the uncertainty of inflow components in the model. Results showed that the average runoff into the lake during 1974–2019 was 5.5 ± 1.4 × 108 m3/10a, whereas rainfall runoff, glacier melt runoff, snowmelt runoff, and baseflow contributed to 54.6%, 10.8%, 1.8%, and 32.7% of total runoff in mean, respectively. Seasonal runoff in spring, summer, autumn, and winter accounted for 6.7%, 60.6%, 23.9% and 8.8% of annual total runoff, respectively. In glacial areas, the reduction in total runoff after removing the precipitation trend was 1.4 times than that of temperature, and in non-glacial areas, the reduction in total runoff after removing the precipitation trend was 1.6 times than the increase in total runoff after removing the temperature trend. The proportion of rainfall runoff increased at a rate of 1.0%/10a, whereas the proportion of melt runoff decreased at a rate of 0.07%/10a during the study period.

Constraining depositional evolution and reservoir compartmentalization in a mixed carbonate-siliciclastic lacustrine system: The Yacoraite formation, Salta Group, NW Argentina

The mixed carbonate-siliciclastic Yacoraite Formation, Salta Group in northern Argentina has been interest of recent studies searching for potential analogues for the South Atlantic pre-sal carbonates. Microbial lacustrine carbonates are important reservoir systems and their characteristics and stratigraphic packaging are a major factor impacting compartmentalization. This study provides sedimentological and facies analyses combined with petrographical, isotope geochemical and petrophysical analyses of lacustrine clastics and carbonates using an example from the Yacoraite Formation (Salta Group) in northern Argentina (Tres Cruces sub-basin).The Yacoraite Formation records the evolution of a lacustrine system responding to basin scale tectonic processes and climatic conditions and is interpreted as a microbially-related mixed carbonate clastic lacustrine system comprising 8 main Facies types, deposited in either a perennial or an ephemeral system. The most dominant architectural elements are: Sandstones with interbedded shales (F1), Shales with interbedded sandstones and siltstones (F2), Thick-bedded oolitic grainstone (F5), Thin-bedded oolitic grainstone-packstone (F6) and Stromatolite boundstone (F8). These facies types allow to differentiate two types of lacustrine environments: Perennial (Stage I) vs Ephemeral (Stage II) intercalated with episodes of continental plains deposition, arranged in three depositional sequences (Yacoraite I, II and III), each recording either transgressive or a regressive-transgressive trend.Carbonate and clastics are arranged in cycles during the Perennial l Stage suggesting that the depositional system responded in patterns attributable to reciprocal sedimentation, whereas, during the Ephemeral Stage 2, spatial coexistence of clastic and carbonate seem to be the case rather that cyclic vertical superposition.Thick-bedded oolitic grainstone and Stromatolites (F5 and F8 respectively) of the perennial lacustrine system show laterally continuous and thick beds with the best reservoir properties. Furthermore, the Sandstones with interbedded shales (F1) are characterized by intermediate values of porosity and permeability, which would not result in permeability barriers. On the other hand, Thin-bedded oolitic grainstone-packstone (F6) of the ephemeral lacustrine system are characterized by a high degree of heterogeneity due to their limited areal distribution and their complex spatial variability with Shales with interbedded sandstones and siltstones (F2).

Remote sensing scene image classification model based on multi-scale features and attention mechanism

Remote sensing scene classification has received more and more attention as important fundamental research in recent years. However, the redundant background information and complex spatial scale variability of remote sensing scene images make the existing convolutional neural network models, which mainly concentrate on global features, perform poorly. To effectively alleviate these problems, we proposed an MSRes-SplitNet model based on multiscale features and attention mechanisms for remote sensing scene image classification. First, MSRes blocks are constructed for the extraction of multi-scale features. Then, the multi-channel local features are fused by the Split-Attention block. Finally, the global and local feature information is aggregated by convolution, thus obtaining multi-scale features while alleviating the small-sample learning problem. Experiments are conducted on three publicly available datasets and compared with other state-of-the-art methods, showing that the proposed method MSRes-SplitNet has better performance while effectively reducing a large number of parameters.

InSAR and numerical modelling for tailings dam monitoring – the Cadia failure case study

In this paper, ground deformation measurements from synthetic aperture radar interferometry (InSAR) and ground-based prism monitoring are compared to finite-element (FE) simulation results for a recent tailings dam collapse. The InSAR monitoring demonstrated the complex spatial and temporal variability of the tailings dam deformation that is not captured in traditional, point-based monitoring approaches. Potentially anomalous deformation behaviour could have been detected from InSAR 1 year preceding the dam failure. A two-dimensional coupled-consolidation analysis was used to predict the dam behaviour during construction stages. The FE modelling results were in broad agreement with the deformation measurement sources, both in terms of magnitude and trends preceding failure. The results, however, deviate significantly immediately before failure, following the construction of the buttresses. Moreover, the FE modelling is sensitive to parameter uncertainties, such as spatial variability of foundation soil properties. The FE results revealed that deformation in both the upstream and downstream parts of the dam are sensitive to changing foundation properties. However, this impact is potentially much more significant on the downstream compared to the upstream parts of the dam, which are dominated by the behaviour of the tailings. This study highlights the efficacy and complementarity of geotechnical and remote sensing techniques for the monitoring of tailings dams.

Quantitative Precipitation Estimation Model Integrating Meteorological and Geographical Factors at Multiple Spatial Scales

Heavy precipitation tends to cause mountain torrents, urban waterlogging and other disasters. It poses a serious threat to people’s life and property safety. Therefore, real-time quantitative precipitation estimation is especially important to keep track of precipitation changes and reduce negative impacts. However, high-resolution and high-accuracy quantitative precipitation estimation is a challenging task due to the complex spatial and temporal variability of microphysics in precipitation processes. Previous studies have focused only on small-scale radar reflectivity factors above rain gauges and did not pay enough attention to the contribution of covariates to model performance. Meteorological and geographical factors play an important role in rain process, so these factors are taken into account during our research. In this study, a quantitative precipitation estimation model that can employ multi-scale radar reflectivity factors and fuse meteorological and geographical factors is proposed to further improve precipitation accuracy. In addition, we propose the muti-scale self-attention (MS-SA) module that can further utilize information at multiple spatial scales to improve the accurate precipitation estimation. The proposed model reduced the root mean square error of precipitation estimation by 83.8% compared to the conventional Z-R relationship that correlates the rainfall and radar reflectivity factors, i.e., Z=aRb, and by 43.7, 24.6, and 22.7% compared to the back propagation neural network (BPNN), convolutional neural network (CNN), and convolutional neural network with the addition of meteorological factors and geographical factors as covariates in the proposed model, respectively. Therefore, we can conclude that multi-scale radar reflectivity factors fused with meteorological and geographical factors can produce more accurate precipitation estimation.

Plio-Pleistocene Perth Basin water temperatures and Leeuwin Current dynamics (Indian Ocean) derived from oxygen and clumped-isotope paleothermometry

Abstract. The Pliocene sedimentary record provides a window into Earth's climate dynamics under warmer-than-present boundary conditions. However, the Pliocene cannot be considered a stable warm climate that constitutes a solid baseline for middle-of-the-road future climate projections. The increasing availability of time-continuous sedimentary archives (e.g., marine sediment cores) reveals complex temporal and spatial patterns of Pliocene ocean and climate variability on astronomical timescales. The Perth Basin is particularly interesting in that respect because it remains unclear if and how the Leeuwin Current sustained the comparably wet Pliocene climate in Western Australia, as well as how it influenced Southern Hemisphere paleoclimate variability. To constrain Leeuwin Current dynamics in time and space, this project obtained eight clumped-isotope Δ47 paleotemperatures and constructed a new orbitally resolved planktonic foraminifera (Trilobatus sacculifer) stable isotope record (δ18O) for the Plio-Pleistocene (4–2 Ma) interval of International Ocean Discovery Program (IODP) Site U1459. These new data complement an existing TEX86 record from the same site and similar planktonic isotope records from the Northern Carnarvon Basin (Ocean Drilling Program (ODP) Site 763 and IODP Site U1463). The comparison of TEX86 and Δ47 paleothermometers reveals that TEX86 likely reflects sea surface temperatures (SSTs) with a seasonal warm bias (23.8–28.9 ∘C), whereas T. sacculifer Δ47 calcification temperatures probably echo mixed-layer temperatures at the studied Site U1459 (18.9–23.2 ∘C). The isotopic δ18O gradient along a 19–29∘ S latitudinal transect, between 3.9 and 2.2 Ma, displays large variability, ranging between 0.5 ‰ and 2.0 ‰. We use the latitudinal δ18O gradient as a proxy for Leeuwin Current strength, with an inverse relationship between both. The new results challenge the interpretation that suggested a tectonic event in the Indonesian Throughflow as the cause for the rapid steepening of the isotopic gradient (0.9 ‰ to 1.5 ‰) around 3.7 Ma. The tectonic interpretation appears obsolete as it is now clear that the 3.7 Ma steepening of the isotopic gradient is intermittent, with flat latitudinal gradients (∼0.5 ‰) restored in the latest Pliocene (2.9–2.6 Ma). Still, the new analysis affirms that a combination of astronomical forcing of wind patterns and eustatic sea level controlled Leeuwin Current intensity. On secular timescales, a period of relatively weak Leeuwin Current is observed between 3.7 and 3.1 Ma. Notably, this interval is marked by cooler conditions throughout the Southern Hemisphere. In conclusion, the intensity of the Leeuwin Current and the latitudinal position of the subtropical front are both long-range effects of the same forcing: heat transport through the Indonesian Throughflow (ITF) valve and its propagation through Indian Ocean poleward heat transport. The common ITF forcing explains the observed coherence of Southern Hemisphere ocean and climate records.

Metrics for 3D Object Pointing and Manipulation in Virtual Reality: The Introduction and Validation of a Novel Approach in Measuring Human Performance

Assessing the performance of human movements during teleoperation and virtual reality (VR) is a challenging problem, particularly in 3D space, due to complex settings. Despite the presence of a multitude of measurements, a compelling standardized 3D metric is yet missing, aggravating interstudy comparability. Hence, evaluating human performance in virtual environments (VEs) is a long-standing research goal, and a performance metric that combines two or more measurements under one formulation remains largely unexplored, particularly in higher dimensions. The absence of such a metric is primarily attributed to the discrepancies between pointing and manipulation, the complex spatial variables in 3D, and the combination of translational and rotational movements.

Tidal dynamics in palaeo‐seas in response to changes in physiography, tidal forcing and bed shear stress

AbstractSimulating hydrodynamic conditions in palaeo‐ocean basins is needed to better understand the effects of tidal forcing on the sedimentary record. When combined with sedimentary analyses, hydrodynamic modelling can help inform complex temporal and spatial variability in the sediment distribution of tide‐dominated palaeo‐ocean basins. Herein, palaeotidal modelling of the epicontinental Upper Jurassic (160 Ma, lower Oxfordian) Sundance and Curtis seas of North America reveals possible regional‐scale variations in tidal dynamics in response to changes in ocean tidal forcing, physiographic configuration and bottom drag coefficient. A numerical model forced with an M2 tidal constituent at the open boundary shows that the magnitude and location of tidal amplification, and the variability in current velocity and bed shear stress in the basin, were controlled by palaeophysiography. Numerical results obtained using a depth of 600 m at the ocean boundary of the system enable the prediction of major facies trends observed in the lower Curtis Formation. The simulation results also highlight that certain palaeophysiographic configurations can either permit or prevent tidal resonance, leading to an overall amplification or dampening of tides across the basin. Furthermore, some palaeophysiographic configurations generated additional tidal harmonics in specific parts of the basins. Consequently, similar sedimentary successions can emerge from a variety of relative sea‐level scenarios, and a variety of sedimentary successions may be deposited in different parts of the basin in any given relative sea‐level scenario. These results suggest that the interpretation of sedimentary successions deposited in strongly tide‐influenced basins should consider changes in tidal dynamics in response to changing sea level and basin physiography.

Stochastic solutions for time-fractional heat equations with complex spatial variables

We deal with complex spatial diffusion equations with time-fractional derivative and study their stochastic solutions. In particular, we complexify the integral operator solution to the heat-type equation where the time derivative is replaced with the convolution-type generalization of the regularized Caputo derivative. We prove that this operator is solution of a complex time-fractional heat equation with complex spatial variable. This approach leads to a wrapped Brownian motion on a circle time-changed by the inverse of the related subordinator. This time-changed Brownian motion is analyzed and, in particular, some results on its moments, as well as its construction as weak limit of continuous-time random walks, are obtained. The extension of our approach to the higher dimensional case is also provided.