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Understanding dependence structure among extreme values plays an important role in risk assessment in environmental studies. In this work, we propose the χ network and the annual extremal network for exploring the extremal dependence structure of environmental processes. A χ network is constructed by connecting pairs whose estimated upper tail dependence coefficient, χ ^ , exceeds a prescribed threshold. We develop an initial χ network estimator, and we use a spatial block bootstrap to assess both the bias and variance of our estimator. We then develop a method to correct the bias of the initial estimator by incorporating the spatial structure in χ . In addition to the χ network, which assesses spatial extremal dependence over an extended period of time, we further introduce an annual extremal network to explore the year-to-year temporal variation of extremal connections. We illustrate the χ and the annual extremal networks by analyzing the hurricane season maximum precipitation at the US Gulf Coast and surrounding area. Analysis suggests there exists long distance extremal dependence for precipitation extremes in the study region and the strength of the extremal dependence may depend on some regional scale meteorological conditions, for example, sea surface temperature. [ABSTRACT FROM AUTHOR]

Precipitation extremes are expected to increase in a warming climate, which may have serious societal impacts. This study uses two initial condition ensembles conducted with the Community Earth System Model (CESM) to investigate potential changes in extreme precipitation under two climate change scenarios over the contiguous United States. We fit non-stationary generalized extreme value (GEV) models to annual maximum daily precipitation simulated from a 30-member ensemble under the RCP8.5 scenario and a 15-member ensemble under the RCP4.5 scenario. We then compare impacts using the 1 % annual exceedance probability (AEP) level, which is the amount of daily rainfall with only a 1 % chance of being exceeded in a given year. Under RCP8.5 between 2005 and 2080, the 1 % AEP level is projected to increase by 17 % on average across the U.S., and up to 36 % for some grid cells. Compared to RCP8.5 in the year 2080, RCP4.5 is projected to reduce the 1 % AEP level by 7 % on average, with reductions as large as 18 % for some grid cells. We also investigate a pattern scaling approach in which we produce predictive GEV distributions of annual precipitation maxima under RCP4.5 given only global mean temperatures for this scenario. We compare results from this less computationally intensive method to those obtained from our GEV model fitted directly to the CESM RCP4.5 output and find that pattern scaling produces reasonable projections. [ABSTRACT FROM AUTHOR]

The goal of this work is to characterize the extreme precipitation simulated by a regional climate model (RCM) over its spatial domain. For this purpose, we develop a Bayesian hierarchical model. Since extreme value analyses typically only use data considered to be extreme, the hierarchical approach is particularly useful as it sensibly pools the limited data from neighboring locations. We simultaneously model the data from both a control and future run of the RCM which allows for easy inference about projected change. Additionally, this hierarchical model is the first to spatially model the shape parameter which characterizes the nature of the distribution’s tail. Our hierarchical model shows that for the winter season, the RCM indicates a general increase in 100-year precipitation return levels for most of the study region. For the summer season, the RCM surprisingly indicates a significant decrease in the 100-year precipitation return level. [ABSTRACT FROM AUTHOR]

In his paper in Climate Monitor, TML Wigley uses basic probability arguments to illustrate how a slowly changing climate could potentially affect the frequency of extreme events. In the time since the paper appeared, there has been increased interest in assessing how weather extremes may be altered by climate change. Much of the work has been conducted using extreme value analysis, which is the branch of statistics developed specifically to characterize extreme events. This commentary discusses the advantages of an EVA approach and reviews some EVA techniques that have been used to describe climate change’s potential impact on extreme phenomena. Additionally, this commentary illustrates basic EVA techniques in an analysis of temperatures for central England. In parallel to Wigley’s analysis, a time-varying EVA analysis is compared to a stationary one, and furthermore, the trend from the EVA analysis is compared to the trend in means. [ABSTRACT FROM AUTHOR]

Noting a strong imperative to understand precipitation extremes, and that considerable uncertainty affects observational data sets, this paper compares the representation of extremes in a number of widely used daily gridded products, derived from rain gauge data, satellite retrieval and reanalysis for the conterminous United States. Analysis is based upon the concept of "tail dependence" arising in multivariate extreme value theory, and we infer the level of temporal dependence in the joint tail of the precipitation probability distribution for pairwise comparisons of products. In this way, we consider the range of products more like an ensemble and examine the relationships between members, and do not attempt to define, or compare products to, some ground truth. Linear correlation between products is also computed. Considerable discrepancy between groups of products, both annually and seasonally, is linked to source data and complex terrain. In particular, products based on rain gauge data showed remarkable similarity, but differed considerably, showing almost total loss of extremal dependence during DJF in mountainous regions, when compared with satellite products. Additionally, simulated re-forecasts revealed reasonable temporal agreement with large scale generated extremes. The diversity and extent of discrepancies identified across all products raises important questions about their use, and we urge caution, particularly for products derived from satellite data. [ABSTRACT FROM AUTHOR]

Heat waves take a major toll on human populations, with negative impacts on the economy, agriculture, and human health. As a result, there is great interest in studying the changes over time in the probability and magnitude of heat waves. In this paper we propose a hierarchical Bayesian model for serially-dependent extreme temperatures. We assume the marginal temperature distribution follows the generalized Pareto distribution (GPD) above a location-specific threshold, and capture dependence between subsequent days using a transformed max-stable process. Our model allows both the parameters in the marginal GPD and the temporal dependence function to change over time. This allows Bayesian inference on the change in likelihood of a heat wave. We apply this methodology to daily high temperatures in nine cities in the western US for 1979-2010. Our analysis reveals increases in the probability of a heat wave in several US cities. This article has supplementary material online. [ABSTRACT FROM AUTHOR]

Uncertainty in return level estimates for rare events, like the intensity of large rainfall events, makes it difficult to develop strategies to mitigate related hazards, like flooding. Latent spatial extremes models reduce the uncertainty by exploiting spatial dependence in statistical characteristics of extreme events to borrow strength across locations. However, these estimates can have poor properties due to model misspecification: Many latent spatial extremes models do not account for extremal dependence, which is spatial dependence in the extreme events themselves. We improve estimates from latent spatial extremes models that make conditional independence assumptions by proposing a weighted likelihood that uses the extremal coefficient to incorporate information about extremal dependence during estimation. This approach differs from, and is simpler than, directly modeling the spatial extremal dependence; for example, by fitting a max-stable process, which is challenging to fit to real, large datasets. We adopt a hierarchical Bayesian framework for inference, use simulation to show the weighted model provides improved estimates of high quantiles, and apply our model to improve return level estimates for Colorado rainfall events with 1% annual exceedance probability. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]