Invited Session I: Spatial Extremes
Friday, October 5, 8:30 a.m. – 10:30 a.m.
Session Chair: Dave Higdon
Calibration of numerical model output using nonparametric spatial density functions
J. Zhou, M. Fuentes, and Howard Chang.
There is a growing interest in quantifying the health impacts of climate change. Those studies routinely use climate model output as future exposure projections. Uncertainty quantification, usually in the form of sensitivity analysis, has focused predominantly on the variability arising from different emission scenarios or multi-model ensembles.
The objective comparison of mean and variances of modeled climatic variables with the ones obtained from observed field data is the common approach for assessment of computer model performance. One drawback of this strategy is that it fails to calibrate properly the tails of the modeled temperature distribution, and improving the ability of these numerical models to characterize extremely high temperature events is of critical interest to understand the potential impact of climate change on human health.
In this work we introduce an innovative framework to assess climate model performance, not only based on the two first moments of models and field data, but on their entire distribution. Our methodology also down-scales the gridded climate model output to the point-level for projecting future exposure over a specific geographical region. This approach is motivated by the need to better characterize the tails of future temperature distribution where the greatest health impacts are likely to occur. We apply the methodology to calibrate temperature projections from a regional climate model for the period 2041 to 2050. Accounting for calibration uncertainty, we calculate the number of excess deaths attributed to heat waves and future temperatures in the southern region of the U.S.
Statistical modeling of extreme value behavior in North American tree-ring density series
Elizabeth Mannshardt, Peter F. Craigmile, Martin P. Tingley
Many analyses of the paleoclimate record include conclusions about extremes, with a focus on the unprecedented nature of recent climate events. While the use of extreme value theory is becoming common in the analysis of the instrumental climate record, applications of this framework to the spatio-temporal analysis of paleoclimate records remain limited. This article develops a Bayesian hierarchical model to investigate spatially varying trends and dependencies in the parameters characterizing the distribution of extremes of a proxy data set, and applies it to the site-wise decadal maxima and minima of a gridded network of temperature sensitive tree ring density time series over northern North America.
The statistical analysis reveals significant spatial associations in the temporal trends of the location parameters of the generalized extreme value distributions: maxima are increasing as a function of time, with stronger increases in the north and east of North America; minima are significantly increasing in the west, possibly decreasing in the east, and exhibit no changes in the center of the region. Results indicate that the distribution varies as a function of both space and time, with tree ring density maxima becoming more extreme as a function of time and minima having diverging temporal trends, by spatial location. Results of this proxy-only analysis are a first step towards directly reconstructing extremal climate behavior, as opposed to mean climate behavior, by linking extremes in the proxy record to extremes in the instrumental record.
Attribution of Extreme Climatic Events
Richard Smith
Much of the current concern about climate change is focused around
questions of whether extreme events are becoming more frequent and/or
more severe as a result of human-induced global warming. More
specifically, when extreme events have arisen, such as the western
European heatwave of 2003, the Russian heatwave of 2010, or the Texas
heatwave of 2011, the question is asked to what extent the extreme
event may be "attributed" to anthropogenic forcing factors. One way to
answer this question is to run climate models under two scenarios, one
including all the anthropogenic forcing factors (in particular,
greenhouse gases) while the other is run only including the natural
forcings (e.g. solar fluctuations) or control runs with no forcings at
all. Based on the climate model runs, probabilities of the extreme
event of interest may be computed under both scenarios, say P1 for the
anthropogenic scenario and P0 for the natural or control scenario. We
can then compute a risk ratio P1/P0 or, equivalently, the fraction of
attributable risk, defined as 1-P0/P1. The difficulty with this simple
idea, however, is that different climate models produce very different
estimates (from each other, and from the observational
data). Therefore, a Bayesian Hierarchical Model is constructed to
synthesize the parameter estimates under either scenario. Results will
be shown based on the 2003 European heatwave. This is joint work with
Michael Wehner (Lawrence Berkeley Lab).
Bayesian spatial extreme value analysis to assess the changing risk of concurrent high temperatures across large portions of European cropland
Ben Shaby and Brian Reich
There is strong evidence that extremely high temperatures are detrimental to the yield and quality of many economically and socially critical crops. Fortunately, the most deleterious conditions for agriculture occur rarely. We wish to assess the risk of the catastrophic scenario in which large areas of croplands experience extreme heat stress during the same growing season. Applying a hierarchical Bayesian spatial extreme value model that allows the distribution of extreme temperatures to change in time both marginally and in spatial coherence, we examine whether the risk of widespread extremely high temperatures across acricultural land in Europe has increased over the last century.