Over the twenty-first century, the frequency of extreme-heat events for major cities in heavily air conditioned California is projected to increase rapidly. Extreme heat is defined here as the temperature threshold for the 90th-percentile excedence probability (T90) of the local warmest summer days under the current climate. Climate projections from three atmosphere–ocean general circulation models, with a range of low to midhigh temperature sensitivity forced by the Special Report on Emission Scenarios higher, middle, and lower emission scenarios, indicate that these increases in temperature extremes and variance are projected to exceed the rate of increase in mean temperature. Overall, projected increases in extreme heat under the higher A1fi emission scenario by 2070–99 tend to be 20%–30% higher than those projected under the lower B1 emission scenario. Increases range from approximately 2 times the present-day number of days for inland California cities (e.g., Sacramento and Fresno), up to 4 times for previously temperate coastal cities (e.g., Los Angeles and San Diego), implying that present-day “heat wave” conditions may dominate summer months—and patterns of electricity demand—in the future. When the projected extreme heat and observed relationships between high temperature and electricity demand for California are mapped onto current availability, maintaining technology and population constant for demand-side calculations, a potential for electricity deficits as high as 17% during T90 peak electricity demand periods is found. Similar increases in extreme-heat days are likely for other southwestern U.S. urban locations, as well as for large cities in developing nations with rapidly increasing electricity demands. In light of the electricity response to recent extreme-heat events, such as the July 2006 heat waves in California, Missouri, and New York, these results suggest that future increases in peak electricity demand will challenge current transmission and supply methods as well as future planned supply capacities when population and income growth are taken into account.

In this study, we investigated the effects of water stress on the growth and yield of summer maize (Zea mays L.) over four phenological stages: Seedling, jointing, heading, and grain-filling. Water stress treatments were applied during each of these four stages in a water-controlled field in the Guanzhong Plain, China between 2013 and 2016. We found that severe water stress during the seedling stage had a greater effect on the growth and development of maize than stress applied during the other three stages. Water stress led to lower leaf area index (LAI) and biomass owing to reduced intercepted photosynthetically active radiation (IPAR) and radiation-use efficiency (RUE). These effects extended to the reproductive stage and eventually reduced the unit kernel weight and yield. In addition, the chlorophyll content in the leaf remained lower, even though irrigation was applied partially or fully after the seedling stage. Severe and prolonged water stress in maize plants during the seedling stage may damage the structure of the photosynthetic membrane, resulting in lower chlorophyll content, and therefore RUE, than those in the plants that did not experience water stress at the seedling stage. Maize plants with such damage did not show a meaningful recovery even when irrigation levels during the rest of the growth period were the same as those applied to the plants not subjected to water stress. The results of our field experiments suggest that an unrecoverable yield loss could occur if summer maize were exposed to severe and extended water stress events during the seedling stage.

A state-of-the-art regional model, the Weather Research and Forecasting (WRF) model (Skamarock et al., 2008) coupled with a chemistry component (Chem) (Grell et al., 2005), is coupled with the snow, ice, and aerosol radiative (SNICAR) model that includes the most sophisticated representation of snow metamorphism processes available for climate study. The coupled model is used to simulate black carbon (BC) and dust concentrations and their radiative forcing in seasonal snow over North China in January–February of 2010, with extensive field measurements used to evaluate the model performance. In general, the model simulated spatial variability of BC and dust mass concentrations in the top snow layer (hereafter BCS and DSTS, respectively) are consistent with observations. The model generally moderately underestimates BCS in the clean regions but significantly overestimates BCS in some polluted regions. Most model results fall within the uncertainty ranges of observations. The simulated BCS and DSTS are highest with > 5000 ng g⁻¹ and up to 5 mg g⁻¹, respectively, over the source regions and reduce to < 50 ng g⁻¹ and < 1 μg g⁻¹, respectively, in the remote regions. BCS and DSTS introduce a similar magnitude of radiative warming (~ 10 W m⁻²) in the snowpack, which is comparable to the magnitude of surface radiative cooling due to BC and dust in the atmosphere. This study represents an effort in using a regional modeling framework to simulate BC and dust and their direct radiative forcing in snowpack. Although a variety of observational data sets have been used to attribute model biases, some uncertainties in the results remain, which highlights the need for more observations, particularly concurrent measurements of atmospheric and snow aerosols and the deposition fluxes of aerosols, in future campaigns.