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Several recent widespread temperature extremes across the United States (U.S.) have been associated with power outages, disrupting access to electricity at times that are critical for the health and well-being of communities. Building resilience to such extremes in our energy infrastructure needs a comprehensive understanding of their spatial and temporal characteristics. In this study, we systematically quantify the frequency, extent, duration, and intensity of widespread temperature extremes and their associated energy demand in the six North American Electric Reliability Corporation regions using ERA5 reanalysis data. We show that every region has experienced hot or cold extremes that affected nearly their entire extent and such events were associated with substantially higher energy demand, resulting in simultaneous stress across the entire electric gird. The western U.S. experienced significant increases in the frequency (123%), extent (32%), duration (55%) and intensity (29%) of hot extremes and Texas experienced significant increases in the frequency (132%) of hot extremes. The frequency of cold extremes has decreased across most regions without substantial changes in other characteristics. Using power outage data, we show that recent widespread extremes in nearly every region have coincided with power outages, and such outages account for between 12%–52% of all weather-related outages in the past decade depending on the region. Importantly, we find that solar potential is significantly higher during widespread hot extremes in all six regions and during widespread cold extremes in five of the six regions. Further, wind potential is significantly higher during widespread hot or cold extremes in at least three regions. Our findings indicate that increased solar and wind capacity could be leveraged to meet the higher demand for energy during such widespread extremes, improving the resilience and reliability of our energy systems in addition to limiting carbon emissions.

 

The impact of extreme heat on crop yields is an increasingly pressing issue given anthropogenic climate warming. However, some of the physical mechanisms involved in these impacts remain unclear, impeding adaptation-relevant insight and reliable projections of future climate impacts on crops. Here, using a multiple regression model based on observational data, we show that while extreme dry heat steeply reduced U.S. corn and soy yields, humid heat extremes had insignificant impacts and even boosted yields in some areas, despite having comparably high dry-bulb temperatures as their dry heat counterparts. This result suggests that conflating dry and humid heat extremes may lead to underestimated crop yield sensitivities to extreme dry heat. Rainfall tends to precede humid but not dry heat extremes, suggesting that multivariate weather sequences play a role in these crop responses. Our results provide evidence that extreme heat in recent years primarily affected yields by inducing moisture stress, and that the conflation of humid and dry heat extremes may lead to inaccuracy in projecting crop yield responses to warming and changing humidity.

 

US maize and soy production have increased rapidly since the mid-20th century. While global warming has raised temperatures in most regions over this time period, trends in extreme heat have been smaller over US croplands, reducing crop-damaging high temperatures and benefiting maize and soy yields. Here we show that agricultural intensification has created a crop-climate feedback in which increased crop production cools local climate, further raising crop yields. We find that maize and soy production trends have driven cooling effects approximately as large as greenhouse gas induced warming trends in extreme heat over the central US and substantially reduced them over the southern US, benefiting crops in all regions. This reduced warming has boosted maize and soy yields by 3.3 (2.7–3.9; 13.7%–20.0%) and 0.6 (0.4–0.7; 7.5%–13.7%) bu/ac/decade, respectively, between 1981 and 2019. Our results suggest that if maize and soy production growth were to stagnate, the ability of the crop-climate feedback to mask warming would fade, exposing US crops to more harmful heat extremes.

 

Intensive crop growth can modify regional climate by partitioning energy to latent heating through transpiration, cooling growing season temperatures. Recent work shows that cooling associated with agriculture can dampen anthropogenic warming over breadbasket regions. However, it is unknown whether climate models reproduce crop influences on regional climate, and thus the future risk of extreme climate events over global breadbasket regions. We show that models overestimate growing season temperatures and underestimate evapotranspiration (ET) over global croplands, and that these differences increase with cropped area. We trace this warm and dry difference through each model's representation of the surface energy budget, showing that model differences in transpiration, leaf area index, and the ratio of transpiration to total ET drive the overall effect. While the implications of these model deficiencies for future projections are uncertain, they point to the importance of improving representations of crop‐climate processes to better assess breadbasket vulnerability to climate change.

 

Abstract The frequency of heat waves (defined as daily temperature exceeding the local 90th percentile for at least three consecutive days) during summer in the United States is examined for daily maximum and minimum temperature and maximum apparent temperature, in recent observations and in 10 CMIP5 models for recent past and future. The annual average percentage of days participating in a heat wave varied between approximately 2% and 10% in observations and in the model’s historical simulations during 1979–2005. Applying today’s temperature thresholds to future projections, heat-wave frequencies rise to more than 20% by 2035–40. However, given the models’ slight overestimation of frequencies and positive trend rates during 1979–2005, these projected heat-wave frequencies should be regarded cautiously. The models’ overestimations may be associated with their higher daily autocorrelation than is found in observations. Heat-wave frequencies defined using apparent temperature, reflecting both temperature and atmospheric moisture, are projected to increase at a slightly (and statistically significantly) faster rate than for temperature alone. Analyses show little or no changes in the day-to-day variability or persistence (autocorrelation) of extreme temperature between recent past and future, indicating that the future heat-wave frequency will be due predominantly to increases in standardized (using historical period statistics) mean temperature and moisture content, adjusted by the local climatological daily autocorrelation. Using nonparametric methods, the average level and spatial pattern of future heat-wave frequency is shown to be approximately predictable on the basis of only projected mean temperature increases and local autocorrelation. These model-projected changes, even if only approximate, would impact infrastructure, ecology, and human well-being. 

Nonlinear increases in warm season temperatures are projected for many regions, a phenomenon we show to be associated with relative surface drying. However, negative human health impacts are physiologically linked to combinations of high temperatures and high humidity. Since the amplified warming and drying are concurrent, the net effect on humid-heat, as measured by the wet bulb temperature (T_W), is uncertain. We demonstrate that globally, on the hottest days of the year, the positive effect of amplified warming onT_Wis counterbalanced by a larger negative effect resulting from drying. As a result, the largest increases inT_WandT_xdo not occur on the same days. Compared to a world with linear temperature change, the drying associated with nonlinear warming dampens mid-latitudeT_Wincreases by up to 0.5 °C, and also dampens the rise in frequency of dangerous humid-heat (T_W > 27 °C) by up to 5 d per year in parts of North America and Europe. Our results highlight the opposing interactions among temperature and humidity changes and their effects onT_W, and point to the importance of constraining uncertainty in hydrological and warm season humidity changes to best position the management of future humid-heat risks.

 

Extreme heat research has largely focused on dry‐heat, while humid‐heat that poses a substantial threat to human‐health remains relatively understudied. Using hourly high‐resolution ERA5 reanalysis and HadISD station data, we provide the first spatially comprehensive, global‐scale characterization of the magnitude, seasonal timing, and frequency of dry‐ and wet‐bulb temperature extremes and their trends. While the peak dry‐ and humid‐heat extreme occurrences often coincide, their timing differs in climatologically wet regions. Since 1979, dry‐ and humid‐heat extremes have become more frequent over most land regions, with the greatest increases in the tropics and Arctic. Humid‐heat extremes have increased disproportionately over populated regions (∼5.0 days per‐person per‐decade) relative to global land‐areas (∼3.6 days per‐unit‐land‐area per‐decade) and population exposure to humid‐heat has increased at a faster rate than to dry‐heat. Our study highlights the need for a multivariate approach to understand and mitigate future harm from heat stress in a warming world.