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El meta-análisis es un método ampliamente utilizado para sintetizar los datos de diferentes estudios. Sin embargo, a menudo estudiantes, profesionales e investigadores carecemos de conocimientos prácticos para hacer e interpretar un meta-análisis. Esta guía presenta una variedad de herramientas para realizar meta-análisis de correlaciones en R, mediante el uso de ejemplos reales. Incluye desde análisis simples y su interpretación, hasta el análisis de moderadores (meta-regresión), usando los paquetes metafor (Viechtbauer, 2010) y metaviz (Kossmeier et al., 2020). También incluye explicaciones para la transformación de coeficientes r de Pearson a z de Fisher (y viceversa), creación de gráficos de bosque (forest plots) y gráficos de embudo (funnel plots), análisis de heterogeneidad y diagnósticos de influencia, así como estrategias para detectar posibles sesgos de publicación utilizando el paquete weightr (Coburn & Vevea, 2019), y para determinar el poder estadístico de un meta-análisis utilizando metameta (Quintana, 2022). Con esta guía, los lectores podrán adquirir las habilidades necesarias para realizar meta-análisis de correlaciones de manera efectiva en R y obtener resultados confiables.

We present a novel 3-D warping technique for the estimation of 4-D seismic time-shift. This unsupervised method provides a diffeomorphic 3-D time shift field that includes uncertainties, therefore, it does not need prior time-shift data to be trained. This results in a widely applicable method in time-lapse seismic data analysis that is not implicitly biased by supervised time-shifts from other methods. We explore the generalization of the method to unseen data both in the same geological setting and in a different field, where the generalization error stays constant and within an acceptable range across test cases. We further explore upsampling of the warp field from a smaller network to decrease computational cost and see some deterioration of the warp field quality as a result. This method provides an accurate 3-D seismic registration method, where the heavy computation can be preexecuted and the inference of the network taking seconds on consumer hardware.

Meta-analysis upweights studies reporting lower standard errors and hence more precision. But in empirical practice, notably in observational research, precision is not given to the researcher. Precision must be estimated, and thus can be p-hacked to achieve statistical significance. Simulations show that a modest dose of spurious precision creates a formidable problem for inverse-variance weighting and bias-correction methods based on the funnel plot. Selection models fail to solve the problem, and the simple mean can beat sophisticated estimators. Cures to publication bias may become worse than the disease. We introduce an approach that surmounts spuriousness: the Meta-Analysis Instrumental Variable Estimator (MAIVE), which employs inverse sample size as an instrument for reported variance.

Increasing evidence suggests that the reproducibility and replicability of scientific findings is threatened by researchers employing questionable research practices (QRPs) in order to achieve statistically significant results. Numerous metrics have been developed to determine replication success but it has not yet been investigated how well those metrics perform in the presence of QRPs. This paper aims to compare the performance of different metrics quantifying replication success in the presence of four types of QRPs: cherry picking of outcomes, questionable interim analyses, questionable inclusion of covariates, and questionable subgroup analyses. Our results show that the metric based on the version of the sceptical p-value that is recalibrated in terms of effect size performs better in maintaining low values of overall type-I error rate, but often requires larger replication sample sizes compared to metrics based on significance, the controlled version of the sceptical p-value, meta-analysis or Bayes factors, especially when severe QRPsare employed.

Resolving the “replication crisis” is a top priority of the scientific community now. Numerous proposals have been made; still, there lacks not only an established solution but even an agreement on whether there exists a “crisis” or not. Here, by questioning the philosophical foundations of our study designs and analyses, I trace back the “crisis” to reductionist ontologies and methodologies ingrained in the modern statistical methods which have dominated biological, medical, psychological, and social sciences for a century. The crisis is not our inability to “reproduce” results but that, uncritical of our statistical methods and the experimental designs they have inculcated, we expect to be able to “reproduce” results despite neglecting almost all individual-level and contextual variables of complex processes.

Forecasting the reproducibility of research findings is one of the key challenges of Metascience. To date, reliable above-chance predictions have mainly been achieved by pooling the subjective ratings of experts through surveys or via prediction markets. Obtaining such data is laborious, however, and unlikely to increase the theoretical understanding of reproducibility.Here we show that empirical measures of K, a principled metric of knowledge, are correlated with reproducibility forecasts made for the Brazilian Reproducibility Initiative (BRI), an ongoing replication of 60 studies that use three common methodologies in the life sciences. For each study, we calculated the K value by dividing the effect size, measured in bits of Shannon entropy explained, by the descriptive complexity of the study’s methodology, calculated as the optimal Shannon encoding of a conceptual graph representing the replication protocol.K values were statistically associated with subjective predictions about studies’ replication probabilities and relative effect sizes. This relation was robust to controlling for study methodology, and was a stronger predictor than other plausible covariates, including the originally reported effect size, subjective ratings of a study’s methodological difficulty, and raters’ self-reported expertise. The superior fit of K may be due to its particular structure and metrics, which are rooted in information theory and may more accurately reflect the implicit calculations made by raters.This is the first evidence that an objective metric hypothesized to quantify scientific knowledge captures subjective judgments about reproducibility. This finding gives the first independent support of K’s underlying assumption that scientific knowledge is a process of information compression, and it may offer a new tool for metaresearch.

1. Estimates of temporal change of biodiversity, and its components loss and gain, are needed at local and geographical scales. However, we lack them because of data in-completeness, heterogeneity, and lack of temporal replication. Hence, we need a tool to integrate heterogeneous data and to account for their incompleteness. 2. We introduce spatiotemporal machine learning interpolation that can estimate cross-scale biodiversity change and its components. The approach naturally captures the expected and complex interactions between scale (grain), geography, data types, and drivers of change. As such it can integrate inventory data from reserves or countries with data from atlases and local survey plots. We present two flavors, both blending tree-based machine learning (random forests, boosted trees) with advances in ecolog-ical scaling: The first combines machine learning with species-area relationships (SAR method), the second with occupancy-area relationships (OAR method). 3. Using simulated data and an empirical example of global mammals and European plants, we show that tree-based machine learning effectively captures temporal biodi-versity change, loss, and gain across a continuum of spatial grains. This can be done despite the lack of time series data (i.e., it does not require temporal replication at sites), temporal biases in the amount of data, and highly uneven sampling area. These estimates can be mapped at any desired spatial resolution. 4. In all, this is a user-friendly and computationally fast approach with minimal require-ments on data format. It can integrate heterogeneous biodiversity data to obtain esti-mates of temporal biodiversity change, loss, and gain, that would otherwise be invisi-ble in the raw data alone.

The most common and well-known meta-regression models work under the assumption that there is only one effect size estimate per study and that the estimates are independent. However, meta-analytic reviews of social science research often include multiple effect size estimates per primary study, leading to dependence in the estimates. Some meta-analyses also include multiple studies conducted by the same lab or investigator, creating another potential source of dependence. An increasingly popular method to handle dependence is robust variance estimation (RVE), but this method can result in inflated Type I error rates when the number of studies is small. Small-sample correction methods for RVE have been shown to control Type I error rates adequately but may be overly conservative, especially for tests of multiple-contrast hypotheses. We evaluated an alternative method for handling dependence, cluster wild bootstrapping, which has been examined in the econometrics literature but not in the context of meta-analysis. Results from two simulation studies indicate that cluster wild bootstrapping maintains adequate Type I error rates and provides more power than extant small sample correction methods, particularly for multiple-contrast hypothesis tests. We recommend using cluster wild bootstrapping to conduct hypothesis tests for meta-analyses with a small number of studies. We have also created an R package that implements such tests.

In this article, we present statacons, an SCons-based build tool for Stata. Because of the integration of Stata and Python in recent versions of Stata, we are able to adapt SCons for Stata workflows without the use of an external shell or extensive configuration. We discuss the usefulness of build tools generally, provide examples of the use of statacons in Stata workflows, present key elements of the syntax of statacons, and discuss extensions, alternatives, and limitations. We provide recommendations for collaborative workflows and, at the end of the article, installation instructions.

Prediction of precipitation changes caused by global climate change is a practical and scientific problem of high complexity. To advance, we look at the record of all available rain gauges in Colombia and at the CHIRPS database to estimate trends in essential variables describing precipitation, including HY-INT, an index of the hydrologic cycle’s intensity. Most of the gauges and cells do not show significant trends. Moreover, the signs of the statistically significant trends are opposite between the two datasets. Satisfactory explanation for the discrepancy remains open. Among the CHIRPS database’s statistically significant trends, the western regions (Pacific and Andes) tend to a more intense hydrologic cycle, increasing both intensity and mean dry spell length, whereas for the northern and eastern regions (Caribbean, Orinoco, and Amazon), the tendencies are opposite. This dipole in trends suggests different mechanisms: ENSO affects western Colombia more directly, whereas rainfall in the eastern regions depends more on the Atlantic Ocean, Caribbean Sea, and Amazon basin dynamics. Nevertheless, there is countrywide accord among gauges and cells with significant increasing trends for annual precipitation. Overall, these observations constitute essential evidence of the need for developing a more satisfactory theory of climate change effects on tropical precipitation.

The aim of this work is to present two different approaches for modeling a photovoltaic panel (PV) fully illuminated. Generally, the physic’s model (White box) takes into account the physic, electronic and energetic behavior of different compound of the system’s model, as function of the solar irradiance and operating temperature. The statistician model (Black box) using the Design of Experiment method considers a physical system as a black box with various inputs (factors) and outputs (responses). Each approach has specific advantages, they are complementary.

Geometric Measures of Variation about the mean is a measure that uses the geometric averaging technique to average the deviations from the mean. From previous studies, it has been determined that the measure is more precise in estimating the average variation about the mean than the existing measures of variation about the mean. Given that the technique is a newly introduced technique of estimating the average variation about the mean, the actual sample estimator for the measure is still unknown, as a result, the study aimed at determining the unbiased estimator for the population geometric measure. The study used a mathematical estimation technique to determinethe unbiased estimator among the existing possible estimators as it assumed a simple random sampling without replacement technique. The study determined that the unbiased estimator of the population estimator was the sample estimator which did not allow one degree of freedom.

Kenya is one of the countries in the world with a good quantity of wind. This makes the country to work ontechnologies that can help in harnessing the wind with a vision of achieving a total capacity of 2GW of wind energy by 2030.The objective of this research is to find the best three-parameter wind speed distribution for examining wind speed using the maximum likelihood fitting technique. To achieve the objective, the study used hourly wind speed data collected for a period of three years (2016 – 2018) from five sites within Narok County. The study examines the best distributions that the data fits and then conducted a suitability test of the distributions using the Kolmogorov-Smirnov test. The distribution parameters were fitted using maximum likelihood technique and model comparison test conducted using Akaike’s Information Criterion (AIC) and the Bayesian Information Criterion (BIC) values with the decision rule that the best distribution relies on the distribution with the smaller AIC and BIC values. The research showed that the best distribution is the gamma distribution with the shape parameter of 2.071773, scale parameter of 1.120855, and threshold parameter of 0.1174. A conclusion that gamma distribution is the best three-parameter distribution for examining the Narok country wind speed data

Measures of dispersion are important statistical tool used to illustrate the distribution of datasets. These measureshave allowed researchers to define the distribution of various datasets especially the measures of dispersion from the mean.Researchers and mathematicians have been able to develop measures of dispersion from the mean such as mean deviation, variance and standard deviation. However, these measures have been determined not to be perfect, for example, variance give average of squared deviation which differ in unit of measurement as the initial dataset, mean deviation gives bigger average deviation than the actual average deviation because it violates the algebraic laws governing absolute numbers, while standarddeviation is affected by outliers and skewed datasets. As a result, there was a need to develop a more efficient measure of variation from the mean that would overcome these weaknesses. The aim of this paper was to model a geometric measure of variation about the population mean which could overcome the weaknesses of the existing measures of variation about the population mean. The study was able to formulate the geometric measure of variation about the population mean that obeyedthe algebraic laws behind absolute numbers, which was capable of further algebraic manipulations as it could be used further to estimate the average variation about the mean for weighted datasets, probability mass functions and probability density functions. Lastly, the measure was not affected by outliers and skewed datasets. This shows that the formulated measure was capable of solving the weaknesses of the existing measures of variation about the mean

Measures of dispersion are important statistical tool used to illustrate the distribution of datasets. These measureshave allowed researchers to define the distribution of various datasets especially the measures of dispersion from the mean. Researchers and mathematicians have been able to develop measures of dispersion from the mean such as mean deviation, variance and standard deviation. However, these measures have been determined not to be perfect, for example, variance giveaverage of squared deviation which differ in unit of measurement as the initial dataset, mean deviation gives bigger average deviation than the actual average deviation because it violates the algebraic laws governing absolute numbers, while standard deviation is affected by outliers and skewed datasets. As a result, there was a need to develop a more efficient measure of variation from the mean that would overcome these weaknesses. The aim of the paper was to estimate the average variation about the population mean using geometric measure of variation. The study was able to use the geometric measure of variation to estimate the average variation about the population mean for un-weighted datasets, weighted datasets, probability mass and probability density functions with finite intervals, however, the function faces serious integration problems when estimating the average deviation for probability density functions as a result of complexity in the integrations by parts involved and alsointegration on infinite intervals. Despite the challenge on probability density functions, the study was able to establish that the geometric measure of variation was able to overcome the challenges faced by the existing measures of variation about the population mean.

Whether in meta-analysis or single experiments, selecting results based on statistical significance leads to overestimated effect sizes, impeding falsification. We critique a quantitative synthesis that used significance to score and select previously published effects for situation awareness-performance associations (Endsley, 2019). How much does selection using statistical significance quantitatively impact results in a meta-analytic context? We evaluate and compare results using significance-filtered effects versus analyses with all effects as-reported. Endsley reported high predictiveness scores and large positive mean correlations but used atypical methods: the hypothesis was used to select papers and effects. Papers were assigned the maximum predictiveness scores if they contained at-least-one significant effect, yet most papers reported multiple effects, and the number of non-significant effects did not impact the score. Thus, the predictiveness score was rarely less than the maximum. In addition, only significant effects were included in Endsley’s quantitative synthesis. Filtering excluded half of all reported effects, with guaranteed minimum effect sizes based on sample size. Results for filtered compared to as-reported effects clearly diverged. Compared to the mean of as-reported effects, the filtered mean was overestimated by 56%. Furthermore, 92% (or 222 out of 241) of the as-reported effects were below the mean of filtered effects. We conclude that outcome-dependent selection of effects is circular, predetermining results and running contrary to the purpose of meta-analysis. Instead of using significance to score and filter effects, meta-analyses should follow established research practices.

Statisticians play a key role in almost all scientific research. But are they also the key to solving the reproducibility crisis? Heidi Seibold, Alethea Charlton, Anne-Laure Boulesteix and Sabine Hoffmann urge statisticians to take an active role in promoting more replicable and more credible science.

In prevention science and related fields, large meta-analyses are common, and these analyses often involve dependent effect size estimates. Robust variance estimation (RVE) methods provide a way to include all dependent effect sizes in a single meta-regression model, even when the exact form of the dependence is unknown. RVE uses a working model of the dependence structure, but the two currently available working models are limited to each describing a single type of dependence. Drawing on flexible tools from multilevel and multivariate meta-analysis, this paper describes an expanded range of working models, along with accompanying estimation methods, which offer potential benefits in terms of better capturing the types of data structures that occur in practice and, under some circumstances, improving the efficiency of meta-regression estimates. We describe how the methods can be implemented using existing software (the ‘metafor’ and ‘clubSandwich’ packages for R), illustrate the proposed approach in a meta-analysis of randomized trials on the effects of brief alcohol interventions for adolescents and young adults, and report findings from a simulation study evaluating the performance of the new methods.

We analysed tide gauges at Dublin port and its environs over the period 1938-2018. With three different tide gauges in the vicinity of the main Dublin port gauge, we merged the data sets and validated them against each other. The recordings of all four tide gauges were found to be in good agreement between 2003-2015, though this was markedly less so from 2016 to the present. We estimate the sea level rise in Dublin port for this period at 10 mm yr-1. The rate over the longer period of 1938-2015 was 1.67 mm yr-1 which is in good agreement with the global average. We found that the rate of sea level rise in Dublin is cyclic with some extreme upward and downward trends. However, starting around 1980, Dublin has seen significantly higher rates that have been always positive since 1996. Furthermore, our analysis of the tide gauges indicates an increase in sea level variability since 1980. Both decadal rates and continuous in time rates are calculated and provided in this paper.

The North Atlantic Oscillation (NAO) is the major atmospheric mode that controls winter European climate variability because its strength and phase determine regional temperature, precipitation and storm tracks. The NAO spatial structure and associated climatic impacts over Europe are not stationary making it crucial to understanding its past evolution in order to improve the predictability of future scenarios. In this regard, there has been a dramatic increase in the number of studies aimed at reconstructing past NAO variability, but the information related to decadal-scale NAO evolution beyond the last millennium is scarce and inconclusive. We present a new 2,000-year multi-annual, proxy-based reconstruction of local NAO impact, with associated uncertainties, obtained by a Bayesian approach. This new local NAO reconstruction is obtained from a mountain lacustrine sedimentary archive of the Iberian Peninsula. This geographical area is not included in previous NAO reconstructions despite being a widely used region for instrumental-based NAO measurements. We assess the main external forcings (i.e., volcanic eruptions and solar activity) on NAO variability which, on a decadal scale, show that a low number of sunspots correlate to low NAO values. By comparison with other previously published NAO reconstructions in our analyses we can test the stationarity of the solar influence on the NAO signal across a latitudinal gradient based on the position of the employed archives for each NAO reconstruction. Inconclusive results on the volcanic forcing on NAO variability over decadal time-scales indicates the need for further studies. Moreover, we highlight the potential role of other North Atlantic modes of variability (i.e., East Atlantic pattern) on the non-stationary behaviour of the NAO throughout the Common Era, likely via solar forcing., This work was financed by the Spanish Ministry of Economy and Competitiveness through the PaleoNAO (CGL2010-15767/BTE), RapidNAO (CGL2013-40608-R), and PaleoModes (CGL2016-75281-C2-1-R) projects and a PhD JAE grant (BOE 03/02/2011) for G.S.L. from the Spanish National Research Council (CSIC). A.H. is supported by a Beatriu de Pinós – Marie Curie Cofund programme fellowship (2016 BP 00023). A.G. is grateful for her Ramón y Cajal contract (RYC-2012-11024) and the MINECO grant VOLCLIMA (CGL2015-72629-EXP). A.P.’s work was supported by a Science Foundation Ireland Career Development Award grant 17/CDA/4695 and an SFI centre grant 12/RC/2289_P2. R.M.T. was supported by the Portuguese FCT project: HOLMDRIVE—North Atlantic Atmospheric Patterns Influence on Western Iberia Climate: From the Late Glacial to the Present (PTDC/CTA-GEO/29029/2017). This contribution is also part of the POLARCSIC activities.

Analyzing of trends in flood magnitude and the timing of the dates of flood occurrences of large river basins across the globe are essential for understanding changes in water availability (high or low flows) and assessing the fidelity of global hydrological models. Our research is motivated by the recent six major consecutive floods in Mahanadi (years: 2001, 2003, 2006, 2008, 2011 and 2013) River Basin (MRB), which is one of the largest peninsular Rivers in India with a catchment area of 14,1589 km2. We examine the altered risk of flooding focusing on changes in the flood regimes and a shift in the timing of floods over the past four decades (1970-2016) using hydrometric observations across the MRB. A framework for identification of flood regime changes is developed using monsoonal maxima peak discharge (MMPD) and peak over threshold (POT) events at 24 stream gauges over the basin. We find a mix of (insignificant) up/downward trends in flood magnitude at Upper MRB (Region I). On the other hand, the middle reaches of the basin (Region II) showed an upward trend in flood magnitude, with a larger number of sites detect significant trends in flood magnitude for the POT events. Further, we find the downward trends in MMPD series at Region I is field significant (at 10% significance level) whereas none of the trends in POT series show field significance. Only a few stations detected abrupt changes in the flood time series, and they are spatially clustered at Region I, whereas Region II showed no evidence of change points. A delayed (or earlier) shift in flood timing is apparent for most of the sites, notwithstanding the mean date of flood occurrence is in August irrespective of the type of flood series. The outcomes of the study contribute to ensuring flood resilience at densely populated large river basins.

We demonstrate levels of skill for forecasts of seasonal-mean wind speed and solar irradiance in Europe, using seasonal forecast systems available from the Copernicus Climate Change Service (C3S). While skill is patchy, there is potential for the development of climate services for the energy sector. Following previous studies, we show that, where there is skill, a simple linear regression-based method using the hindcast and forecast ensemble means provides a straightforward approach for producing calibrated probabilistic seasonal forecasts. This method extends naturally to using a larger-scale feature of the climate, such as the North Atlantic Oscillation, as the climate model predictor, and we show that this provides opportunities to improve the skill in some cases. \ud \ud We further demonstrate that, on seasonal-average and regional (e.g. national) average scales, wind and solar power generation are highly correlated with single climate variables (wind speed and irradiance). The detailed non-linear transformations from meteorological quantities to energy quantities, which are essential for detailed simulation of power system operations, are usually not necessary when forecasting gross wind or solar generation potential at seasonal-mean regional-mean scales.\ud \ud Together, our results demonstrate that where there is skill in seasonal forecasts of wind speed and irradiance, or a correlated larger-scale climate predictor, skilful forecasts of seasonal mean wind and solar power generation can be made based on the climate variable alone, without requiring complex transformations. This greatly simplifies the process of developing a useful seasonal climate service.

The geographic focus of the current study Mariana trench, the deepest point of the Earth located in the west Pacific Ocean. Mariana trench has unique structure and features formed in the complex process of the trench development. There is a range of the environmental factors affecting trench structure and functioning: bathymetry, geography, geology and tectonics. Current research aimed to study interconnections among these determinants. Technically, the research was performed by R programming language, statistical analysis, and QuantumGIS. Methodology includes a range of the statistical methods for data processing, the most important of which is cluster analysis. The results revealed unevenness of the factors affecting trench bathymetric structure, caused by the environmental conditions.

This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models. Application domains include stock market, marketing, E-commerce, corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on advancement of hybrid models as more than 51% of the reviewed articles applied hybrid model. On the other hand, it is found that based on the RMSE accuracy metric, hybrid models had higher prediction accuracy than other algorithms. While it is expected the trends go toward the advancements of deep learning models.

Primitive lavas erupted in the Cascades arc of western North America demonstrate significant patterns of along-arc heterogeneity. Such compositional diversity may be the result of differences in mantle melting processes, subduction geometry, regional tectonics, or compositions of the slab, mantle or overlying lithosphere. Previous authors have partitioned the arc into four geochemically distinct segments in order to assess the importance and relative roles of these potential causes (Schmidt et al., 2008). However, despite the immense amount of data available from the Cascade arc, no previous study has utilized a statistical approach on a comprehensive dataset to address such a fundamental petrologic question. To better characterize the heterogeneity of the entire arc, we compiled >250,000 isotopic, major, and trace element analyses (glass and whole rock) from nearly 13,000 samples. To minimize inherent sampling bias – the effect where well-studied volcanoes heavily weight conclusions – we use a weighted bootstrap Monte Carlo approach in which the probability of a sample being selected to the posterior distribution was inversely proportional to the number of samples within its 0.25° latitude bin. This methodology produces a more uniform and unbiased distribution from which we can assess regional, rather than local, compositional variability in the Cascades arc. Using a multivariate statistical approach, we demonstrate that the four segments designated by Schmidt et al. (2008) are, in fact, statistically distinct. However, using a modified hierarchical clustering mechanism, we objectively divide the arc into six regions which have geochemical differences that are up to 6.3 times more statistically significant than in the previous scheme. Our new, more robust segmentation scheme includes the Garibaldi (49.75-51°N), Baker (48.5-49.75°N), Glacier Peak (47.75-48.5°N), Washington (45.75-47.75°N), Graben (44.25-45.75°N), and South (41.25-44.25°N) Segments. By partitioning the arc into the most statistically distinct segments and calculating unbiased mean compositions for each, we explore the petrogenetic causes for the regional-scale differences in primitive lava compositions. These bootstrapped mean data indicate significant inter-segment differences in fluid-flux signature, mantle fertility, and depth and degree of melting. We suggest that differences in subduction geometry, regional tectonics and mantle heterogeneity are the primary causes for these intra-arc differences. This study demonstrates the value of rigorous statistics and the use of big data in the field of petrology.

A Pluri-Gaussian method is developed for facies variables in three dimensions to model vertical cyclicity related to facies ordering and rhythmicity. Cyclicity is generally characterised by shallowing-upward or deepening-upward sequences and rhythmicity by the repetition of facies at constant intervals along sequences. Both of these aspects are commonly observed in shallow-marine carbonate successions, especially in the vertical direction. A grid-free spectral simulation approach is developed, with a separable covariance allowing a dampened hole-effect to capture rhythmicity in the vertical direction and a different covariance in the lateral plane along strata, as in space-time models. In addition, facies ordering is created by using a spatial shift between two latent Gaussian functions in the Pluri-Gaussian approach. Rapid conditioning to data is performed via Gibbs sampling and kriging using the screening properties of separable covariances. The resulting facies transiograms can show complex patterns of cyclicity and rhythmicity. Finally, a three dimensional case study of shallow-marine carbonate deposits at outcrop shows the applicability of the modelling method.

Sea level rises at an accelerating pace threatening coastal communities all over the world. In this context sea level projections are key tools to help risk mitigation and adaptation. Sea level projections are often made using models of the main contributors to sea level rise (e.g. thermal expansion, glaciers, ice sheets...). To obtain the total sea level these contributions are added, therefore the uncertainty of total sea level depends on the correlation between the uncertainties of the contributors. This fact is important to understand the differences in the uncertainty of sea level projections from different methods. Using two process-based models to project sea level for the 21st century, we show how to model the correlation structure and its time dependence. In these models the correlation primarily arises from uncertainty of future global mean surface temperature that correlates with almost all contributors. Assuming that sea level contributors are independent of each other, an assumption made in many sea level projections, underestimates the uncertainty in sea level projections. As a result, high-end low probability events that are important for decision making are underestimated. The uncertainty in the strength of the dependence between contributors is also explored. New dependence relation between the uncertainty of dynamical processes, and surface mass balance in glaciers and ice sheets are introduced in our model. Total sea level uncertainty is found to be as sensitive to the dependence between contributors as to uncertainty in individual contributors like thermal expansion and Greenland ice sheet.

This paper details team SUTD’s effort when participating in the “Prediction of extremal precipitation” challenge. We propose a framework that combines the generalized Pareto distribution, a bootstrap resampling scheme and inverse distance weights to capture spatial dependence. Our method reduces the quantile loss functions by 55.1% as compared to a naive benchmark, and shows improvement across all months and all stations. The method works well even for stations without training data. Despite being simple, our method ranked fifth in the competition and our scores were very close to those of the winning teams. The framework is scalable and can be implemented easily by practising engineers.