Environmetrics最新文献

Samuels, R., N. Carmon, B. Konomi, J. Hobbs, A. Braverman, D. Young, and J. J. Song. 2025. “Estimation of Impact Ranges for Functional Valued Predictors.” Environmetrics 36, no. 5: e70024. https://doi.org/10.1002/env.70024.

In the version of this article initially published, the name of the 3rd author was spelled incorrectly. The correct name is Bledar Konomi, and the spelling error has been updated in the original.

We apologize for this error.

Many environmental processes, such as rainfall, wind, or snowfall, are inherently spatial, and the modeling of extremes has to take into account that feature. In addition, such processes may be associated with a nonextremal feature, for example, wind speed and direction or extreme snowfall and time of occurrence in a year. This article proposes a Bayesian hierarchical model with a conditional independence assumption that aims at modeling simultaneously spatial extremes and an angular component. The proposed model relies on the extreme value theory as well as recent developments for handling directional statistics over a continuous domain. Working within a Bayesian setting, a Gibbs sampler is introduced whose performances are analysed through a simulation study. The paper ends with an application to extreme wind speed in France. Results show that extreme wind events in France are mainly coming from the West, apart from the Mediterranean part of France and the Alps.

Causally-induced asymmetry reflects the principle that an event qualifies as a cause only if its absence would prevent the occurrence of the effect. Thus, uncovering causal effects becomes a matter of comparing a well-defined score in both directions. Motivated by studying causal effects at extreme levels of a multivariate random vector, we propose to construct a model-agnostic causal score relying solely on the assumption of the existence of a max-domain of attraction. Based on a representation of a generalised Pareto random vector, we construct the causal score as the Wasserstein distance between the margins and a well-specified random variable. The proposed methodology is illustrated on a simulated dataset of different characteristics of catchments in Switzerland: discharge, precipitation, snowmelt, temperature, and evapotranspiration.

The block maxima approach, which consists of dividing a series of observations into equal-sized blocks to extract the block maxima, is commonly used for identifying and modeling extreme events using the generalized extreme value (GEV) distribution. In the analysis of coastal wave surge levels, the underlying data that generate the block maxima typically have missing observations. Consequently, the observed block maxima may not correspond to the true block maxima, yielding biased estimates of the GEV distribution parameters. Various parametric modeling procedures are proposed to account for the presence of missing observations under a block maxima framework. The performance of these estimators is compared through an extensive simulation study and illustrated by an analysis of extreme wave surges in Atlantic Canada.

The June 1991 Mt. Pinatubo eruption resulted in a massive increase of sulfate aerosols in the atmosphere, absorbing radiation and leading to global changes in surface and stratospheric temperatures. A volcanic eruption of this magnitude serves as a natural analog for stratospheric aerosol injection, a proposed solar radiation modification method to combat a warming climate. The impacts of such an event are multifaceted and region-specific. Our goal is to characterize the multivariate and dynamic nature of the atmospheric impacts following the Mt. Pinatubo eruption. We developed a multivariate space-time dynamic linear model to understand the full extent of the spatially- and temporally-varying impacts. Specifically, spatial variation is modeled using a flexible set of basis functions for which the basis coefficients are allowed to vary in time through a vector autoregressive (VAR) structure. This novel model is cast in a Dynamic Linear Model (DLM) framework and estimated via a customized MCMC approach. We demonstrate how the model quantifies the relationships between key atmospheric parameters prior to and following the Mt. Pinatubo eruption with reanalysis data from MERRA-2 and highlight when such a model is advantageous over univariate models.

We study the heterogeneity in climate change patterns between hemispheres using a cointegrated vector autoregression (CVAR) derived from an energy balance model. We provide new estimates of the responses of hemispheric climate conditions to shocks in radiative forcing, indicating stronger responses of surface temperature in the Northern than in the Southern Hemisphere, and similar responses of ocean heat content. The difference in equilibrium climate sensitivity between hemispheres is estimated to be around 1.2°C and statistically significant. We also use the model to make projections of the inter-hemispheric difference in temperature anomalies, conditional on the scenarios of forcing considered by the Intergovernmental Panel on Climate Change. The projections range from 0.5°C to 2.1°C in 2100, depending on the scenario. Stochastic forecasts based on the estimated CVAR model are used to assess the probability of alternative scenarios. Possible economic implications of asymmetries are discussed.