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Global warming and the associated rise in extreme temperatures substantially increase the chance of concurrent droughts and heat waves. The 2014 California drought is an archetype of an event characterized by not only low precipitation but also extreme high temperatures. From the raging wildfires, to record low storage levels and snowpack conditions, the impacts of this event can be felt throughout California. Wintertime water shortages worry decision-makers the most because it is the season to build up water supplies for the rest of the year. Here we show that the traditional univariate risk assessment methods based on precipitation condition may substantially underestimate the risk of extreme events such as the 2014 California drought because of ignoring the effects of temperature. We argue that a multivariate viewpoint is necessary for assessing risk of extreme events, especially in a warming climate. This study discusses a methodology for assessing the risk of concurrent extremes such as droughts and extreme temperatures.

No abstract

It has been proposed that chemically reactive lipids released during lipid peroxidation convert low density lipoprotein (LDL), the major carrier of plasma cholesterol, to an abnormal form and that receptor-mediated clearance of this altered LDL produces cholesteryl ester deposition in macrophage-derived foam cells of atheroma. Immuno-cytochemical analyses now reveal the presence of protein modified by malondialdehyde, a peroxidative end product, which colocalizes with the extracellular deposition of apolipoprotein B-100 protein of LDL in atheroma from Watanabe heritable hyperlipidemic rabbits. These findings provide direct evidence for the existence in vivo of protein modified by a physiological product of lipid peroxidation within arterial lesions.

Extreme climatic events are growing more severe and frequent, calling into question how prepared our infrastructure is to deal with these changes. Current infrastructure design is primarily based on precipitation Intensity-Duration-Frequency (IDF) curves with the so-called stationary assumption, meaning extremes will not vary significantly over time. However, climate change is expected to alter climatic extremes, a concept termed nonstationarity. Here we show that given nonstationarity, current IDF curves can substantially underestimate precipitation extremes and thus, they may not be suitable for infrastructure design in a changing climate. We show that a stationary climate assumption may lead to underestimation of extreme precipitation by as much as 60%, which increases the flood risk and failure risk in infrastructure systems. We then present a generalized framework for estimating nonstationary IDF curves and their uncertainties using Bayesian inference. The methodology can potentially be integrated in future design concepts.

The sensitivity of California precipitation to El Niño intensity is investigated by applying a multimodel ensemble of historical climate simulations to estimate how November–April precipitation probability distributions vary across three categorizations of El Niño intensity. Weak and moderate El Niño events fail to appreciably alter wet or dry risks across northern and central California, though odds for wet conditions increase across southern California during moderate El Niño. Significant increases in wet probabilities occur during strong El Niño events across the entire state. In California's main northern watershed regions, simulations indicate an 85% chance of greater than normal precipitation and a 50% probability of at least 125% of normal. Our results indicate that both the statewide average and the spatial distribution of California precipitation are sensitive to El Niño intensity. Forecasts of El Niño intensity would thus contribute to improved situational awareness for California water planning and related water resource impacts.

No abstract

Time series of U.S. daily heavy precipitation (95th percentile) are analyzed to determine factors responsible for regionality and seasonality in their 1979–2013 trends. For annual conditions, contiguous U.S. trends have been characterized by increases in precipitation associated with heavy daily events across the northern United States and decreases across the southern United States. Diagnosis of climate simulations (CCSM4 and CAM4) reveals that the evolution of observed sea surface temperatures (SSTs) was a more important factor influencing these trends than boundary condition changes linked to external radiative forcing alone. Since 1979, the latter induces widespread, but mostly weak, increases in precipitation associated with heavy daily events. The former induces a meridional pattern of northern U.S. increases and southern U.S. decreases as observed, the magnitude of which closely aligns with observed changes, especially over the south and far west. Analysis of model ensemble spread reveals that appreciable 35-yr trends in heavy daily precipitation can occur in the absence of forcing, thereby limiting detection of the weak anthropogenic influence at regional scales. Analysis of the seasonality in heavy daily precipitation trends supports physical arguments that their changes during 1979–2013 have been intimately linked to internal decadal ocean variability and less so to human-induced climate change. Most of the southern U.S. decrease has occurred during the cold season that has been dynamically driven by an atmospheric circulation reminiscent of teleconnections linked to cold tropical eastern Pacific SSTs. Most of the northeastern U.S. increase has been a warm season phenomenon, the immediate cause for which remains unresolved.