Large-scale environmental variables known to be linked to the formation of tropical cyclones have previously been used to develop empirical indices as proxies for assessing cyclone frequency from large-scale analyses or model simulations. Here the authors examine the ability of two recent indices, the genesis potential (GP) and the genesis potential index, to reproduce observed North Atlantic cyclone annual frequency variations and trends. These skillfully estimate the mean seasonal variation of observed cyclones, but they struggle with reproducing interannual frequency variability and change. Examination of the independent contributions by the four terms that make up the indices finds that potential intensity and shear have significant skill, while moisture and vorticity either do not contribute to or degrade the indices’ capacity to reproduce observed interannual variability. It is also found that for assessing basinwide cyclone frequency, averaging indices over the whole basin is less skillful than its application to the general area off the coast of Africa broadly covering the main development region (MDR). These results point to a revised index, the cyclone genesis index (CGI), which comprises only potential intensity and vertical shear. Application of the CGI averaged over the MDR demonstrates high and significant skill at reproducing interannual variations and trends in all-basin cyclones across both reanalyses. The CGI also provides a more accurate reproduction of seasonal variations than the original GP. Future work applying the CGI to other tropical cyclone basins and to the downscaling of relatively course climate simulations is briefly addressed.
[1] Although information about climate change and its implications is becoming increasingly available to water utility managers, additional tools are needed to translate this information into secondary products useful for local assessments. The anticipated intensification of the hydrologic cycle makes quantifying changes to hydrologic extremes, as well as associated water quality effects, of particular concern. To this end, this paper focuses on using extreme value statistics to describe maximum monthly flow distributions at a given site, where the nonstationarity is derived from concurrent climate information. From these statistics, flow quantiles are reconstructed over the historic record and then projected to 2100. This paper extends this analysis to an associated source water quality impact, whereby the corresponding risk of exceeding a water quality threshold is examined. The approach is applied to a drinking water source in the Pacific Northwest United States that has experienced elevated turbidity values correlated with high streamflow. Results demonstrate that based on climate change information from the most recent Intergovernmental Panel on Climate Change assessment report, the variability and magnitude of extreme streamflows substantially increase over the 21st century. Consequently, the likelihood of a turbidity exceedance increases, as do the associated relative costs. The framework is general and could be applied to estimate extreme streamflow under climate change at other locations, with straightforward extensions to other water quality variables that depend on extreme hydroclimate.
[1] Though climate forecasts offer substantial promise for improving water resource oversight, additional tools are needed to translate these forecasts into water-quality-based products that can be useful to water utility managers. To this end, a generalized approach is developed that uses seasonal forecasts to predict the likelihood of exceeding a prescribed water quality limit. Because many water quality standards are based on thresholds, this study utilizes a logistic regression technique, which employs nonparametric or "local" estimation that can capture nonlinear features in the data. The approach is applied to a drinking water source in the Pacific Northwest United States that has experienced elevated turbidity values that are correlated with streamflow. The main steps of the approach are to (1) obtain a seasonal probabilistic precipitation forecast, (2) generate streamflow scenarios conditional on the precipitation forecast, (3) use a local logistic regression to compute the turbidity threshold exceedance probabilities, and (4) quantify the likelihood of turbidity exceedance corresponding to the seasonal climate forecast. Results demonstrate that forecasts offer a slight improvement over climatology, but that representative forecasts are conservative and result in only a small shift in total exceedance likelihood. Synthetic forecasts are included to show the sensitivity of the total exceedance likelihood. The technique is general and could be applied to other water quality variables that depend on climate or hydroclimate.
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