Resolution Dependence in Modeling Extreme Weather Events

At Argonne National Laboratory we have developed a high performance regional climate modeling simulation capability based on the NCAR MM5v3.4. The regional climate simulation system at Argonne currently includes a Java-based interface to allow rapid selection and generation of initial and boundary conditions, a high-performance version of MM5v3.4 modified for long climate simulations on our 512-processor Beowulf cluster (Chiba City), an interactive Web-based analysis tool to facilitate analysis and collaboration via the Web, and an enhanced version of the CAVE5d software capable of working with large climate data sets. In this paper we describe the application of this modeling system to investigate the role of model resolution in predicting extreme events such as the ''Hurricane Huron'' event of 11-15 September 1996. We have performed a series of ''Hurricane Huron'' experiments at 80, 40, 20, and 10 km grid resolution over an identical spatiotemporal domain. We conclude that increasing model resolution leads to dramatic changes in the vertical structure of the simulated atmosphere producing significantly different representations of rainfall and other parameters critical to the assessment of impacts of climate change

Methods of Tail Dependence Estimation

Characterization and quantification of climate extremes and their dependencies are fundamental to the studying of natural hazards. This chapter reviews various parametric and nonparametric tail dependence coefficient estimators. The tail dependence coefficient describes the dependence (degree of association) between concurrent extremes at different locations. Accurate and reliable knowledge of the spatial characteristics of extremes can help improve the existing methods of modeling the occurrence probabilities of extreme events. This chapter will review these methods and use two case studies to demonstrate the application of tail dependence analysis

The dependence of precipitation and its footprint on atmospheric temperature in idealized extratropical cyclones

Flood hazard is a function of the magnitude and spatial pattern of precipitation accumulation. The sensitivity of precipitation to atmospheric temperature is investigated for idealized extratropical cyclones, enabling us to examine the footprint of extreme precipitation (surface area where accumulated precipitation exceeds high thresholds) and the accumulation in different-sized catchment areas. The mean precipitation increases with temperature, with the mean increase at 5.40%/∘C. The 99.9th percentile of accumulated precipitation increases at 12.7%/∘C for 1 h and 9.38%/∘C for 24 h, both greater than Clausius-Clapeyron scaling. The footprint of extreme precipitation grows considerably with temperature, with the relative increase generally greater for longer durations. The sensitivity of the footprint of extreme precipitation is generally super Clausius-Clapeyron. The surface area of all precipitation shrinks with increasing temperature. Greater relative changes in the number of catchment areas exceeding extreme total precipitation are found when the domain is divided into larger rather than smaller catchment areas. This indicates that fluvial flooding may increase faster than pluvial flooding from extratropical cyclones in a warming world. When the catchment areas are ranked in order of total precipitation, the 99.9th percentile is found to increase slightly above Clausius-Clapeyron expectations for all of the catchment sizes, from 9 km2 to 22,500 km2. This is surprising for larger catchment areas given the change in mean precipitation. We propose that this is due to spatially concentrated changes in extreme precipitation in the occluded fron

Forecasted attribution of the human influence on Hurricane Florence.

Changes in extreme weather, such as tropical cyclones, are one of the most serious ways society experiences the impact of climate change. Advance forecasted conditional attribution statements, using a numerical model, were made about the anthropogenic climate change influence on an individual tropical cyclone, Hurricane Florence. Mean total overland rainfall amounts associated with the forecasted storm's core were increased by 4.9 ± 4.6% with local maximum amounts experiencing increases of 3.8 ± 5.7% due to climate change. A slight increase in the forecasted storm size of 1 to 2% was also attributed. This work reviews our forecasted attribution statement with the benefit of hindsight, demonstrating credibility of advance attribution statements for tropical cyclones

Spatial--temporal mesoscale modeling of rainfall intensity using gage and radar data

Gridded estimated rainfall intensity values at very high spatial and temporal resolution levels are needed as main inputs for weather prediction models to obtain accurate precipitation forecasts, and to verify the performance of precipitation forecast models. These gridded rainfall fields are also the main driver for hydrological models that forecast flash floods, and they are essential for disaster prediction associated with heavy rain. Rainfall information can be obtained from rain gages that provide relatively accurate estimates of the actual rainfall values at point-referenced locations, but they do not characterize well enough the spatial and temporal structure of the rainfall fields. Doppler radar data offer better spatial and temporal coverage, but Doppler radar measures effective radar reflectivity ($Ze$) rather than rainfall rate ($R$). Thus, rainfall estimates from radar data suffer from various uncertainties due to their measuring principle and the conversion from $Ze$ to $R$. We introduce a framework to combine radar reflectivity and gage data, by writing the different sources of rainfall information in terms of an underlying unobservable spatial temporal process with the true rainfall values. We use spatial logistic regression to model the probability of rain for both sources of data in terms of the latent true rainfall process. We characterize the different sources of bias and error in the gage and radar data and we estimate the true rainfall intensity with its posterior predictive distribution, conditioning on the observed data.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS166 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Climate Change and Sea Level Rise Projections for Boston

While the broad outlines of how climate change would impact Boston have been known for some time, it is only recently that we have developed a more definitive understanding of what lies ahead. That understanding was advanced considerably with the publication of Climate Change and Sea Level Rise Projections for Boston by the Boston Research Advisory Group (BRAG).The BRAG report is the first major product of "Climate Ready Boston," a project led by the City of Boston in partnership with the Green Ribbon Commission and funded in part by the Barr Foundation. The BRAG team includes 20 leading experts from the region's major universities on subjects ranging from sea level rise to temperature extremes. University of Massachusetts Boston professors Ellen Douglas and Paul Kirshen headed the research.The BRAG report validates earlier studies, concluding Boston will get hotter, wetter, and saltier in the decades ahead (see figures below). But the group has produced a much more definitive set of projections than existed previously, especially for the problem of sea level rise. BRAG also concluded that some of the effects of climate change will come sooner than expected, accelerating the urgency of planning and action