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This study focuses on the Electric Reliability Council of Texas (ERCOT) electricity market in Texas and demonstrates how the increase in temperature due to climate change is already driving large increases in electricity demand and total electricity costs. Results show that, compared to a 1950–80 baseline climate, electricity demand in 2023 was 1.9 GW (3.9%) higher because of the extreme temperatures of that year—climate change contributed 47% of this increase, with the rest coming from short-term climate variability. As demand increases, so does the price per unit of electricity, so consumers are hit double: They must buy more electricity, and each unit of electricity costs more. Using data from the wholesale market, we estimate that the total cost of electricity (the combination of higher demand and higher per unit prices) increased by $7.6B in 2023 compared to the baseline climate, $290 per ERCOT customer, with most of this increase occurring during the summer. Climate change contributed about 29% of this ($2.2B, $83 per customer), while short-term variability contributed the rest. About two-thirds of this increase is due to price increases triggered when the ERCOT grid becomes constrained. Investments in increasing the power supply or the ability to transmit it across the state, or reducing demand (e.g., demand response), could substantially reduce the impact of increasing temperature on the cost of electricity in Texas. Significance StatementQuantifying the impacts of warmer temperatures due to climate change on society is a key goal of the climate science community. In this paper, we develop a methodology for calculating the cost of increased temperatures on electricity consumption. We show that climate change is driving up the costs of electricity in Texas. Compared to the climate of the mid-twentieth century, electricity demand was 4.1% higher in 2023, with climate change responsible for about half of this increase. This increased the total cost of electricity by $7.6 billion, $290 per person. Climate change contributed about 29% of this extra cost, representing a significant burden on the poorest in our society. 

Abstract We compare high‐resolution land‐surface temperature (LST) estimates from the GOES‐16/17 (GOES) satellites to ERA‐5 Land (ERA‐5) reanalysis data across nine large US cities. We quantify the offset and find that ERA‐5 generally overestimates LST compared to GOES by 1.63°C. However, this overestimation is less pronounced in urban areas, underscoring the limitations of ERA‐5 in capturing the LST gradient between urban and non‐urban areas. We then examine three quantities: Surface Urban Heat Island Intensity (SUHII), extreme LST events, and LST exposure by population. We find that ERA‐5 does not accurately represent the diurnal variation and magnitude of SUHII in GOES. Furthermore, while ERA‐5 was on average too warm, ERA‐5 underestimates extreme heat by an average of 2.40°C. Our analysis reveals higher population exposure to high LST in the GOES data set across the cities studied. This discrepancy is especially pronounced when estimating the population fraction that are most exposed to heat. 

Neighborhood-scale planning plays a crucial role in addressing climate change through adaptation strategies, particularly concerning thermal environmental factors such as temperature and humidity. While various numerical models estimate local thermal environments, their complexity limits broader applications in performance-oriented neighborhood planning. To bridge this gap, the Urban Weather Generator (UWG), a simplified model based on energy conservation principles, was developed to meet the needs of neighborhood planners. Although UWG has been utilized in cities like Singapore, Basel, and Toulouse, further validation is needed for hot and humid conditions such as those in the Gulf Coast of the US. This study evaluates the performance of the UWG model in the Houston area as a first step toward estimating thermal environments at a fine neighborhood scale. We conducted a comprehensive sensitivity analysis of key model input parameters and neighborhood size, revealing that urban building density is the most significant variable, while neighborhood size has minimal impact on model accuracy, indicating potential scale flexibility. Multiple reference weather stations in suburban/rural areas were used, with outputs compared to actual observations in a representative urban site in Houston. Over 11 months of observations across multiple years, the Sugar Land weather station and Wharton weather station were identified as the optimal choices for simulating neighborhood-scale weather in Houston’s urban area. 

Different ways of counting heat‐related deaths (HRD) can give you very different numbers. This study examines HRD in Texas using three different definitions: The Optimal Temperature Method (OTM) estimates mortality based on deviations from a community's optimal temperature, capturing the effects of both moderate and extreme heat exposure. This method finds that 2.2% of summertime Texas mortality were heat related over the period 2010–2023. The Extreme Heat Method (XHM) counts deaths associated with extreme temperatures; we find that temperatures exceeding the 95th percentile were responsible for 0.5% of summertime Texas mortality over this same period. This means that moderate heat is responsible for 77% of HRD, with extreme temperatures responsible for the rest. The Excess Death Method (EDM) approach quantifies the mortality burden as the increase in mortality compared to what would have occurred with the climate of a baseline period; we find that summertime Texas mortality over this period was 1.7% higher than with the climate of the mid‐20th century. When comparing these estimates to official HRD values from the State of Texas, which are based on an official determination that heat contributed to the death, we find that official State HRD numbers appear to substantially underreport HRD, attributing just 0.3% of summertime deaths to heat, with the XHM method being the closest. 

Extreme heat poses significant environmental and health risks. These risks often disproportionately affect marginalized and disenfranchised communities. Neighborhood-scale planning is essential for addressing climate change through adaptation strategies. Currently, there is a lack of long-term weather data at the neighborhood level, limiting the ability to analyze localized weather trends and compare variations across areas. This gap persists due to the challenges of collecting fine-scale local data, including significant time demands, limited resources, and potential privacy concerns. To bridge this gap, this project employs the Urban Weather Generator model to generate neighborhood-scale temperature and relative humidity data for the city of Houston. The neighborhood size is defined using a 500-by-500-m grid. Based on the comprehensive analysis of heat stress variation across these neighborhoods, we identify areas with notably higher or lower heat stress duration. This dataset is intended to inform targeted interventions in microclimate design and personal-level heat adaptation. 

The IPCC’s Special Report on Climate Change and Cities shows how cities must adapt to climate risks. Urban planners need to create solutions that fit each city’s needs, enhancing urban adaptability and resilience in the context of increasing climate-related risks. Sustainable urban planning, increased citizen awareness, and resilient infrastructure design are crucial in mitigating the growing impacts of climate change on human settlements. Addressing these challenges requires the integration of perspectives from diverse disciplines, including the natural sciences, social sciences, and engineering fields. This article draws on insights from a collaborative effort among experts in these areas, promoting a more coordinated and interdisciplinary approach. By bridging this expertise, we aim to advance resilience practices and awareness, fostering effective urban climate solutions in Texas and beyond. 

The rapidly intensifying effects of climate change on urban settlements demand that cities move to the forefront of resilience planning. Climate extremes, from heatwaves to flooding, are increasingly testing the adaptability limits of urban systems and the vulnerability of their populations. Recognizing the unique position of cities, the IPCC’s seventh assessment cycle has prioritized urban areas in its upcoming Special Report on Climate Change and Cities. The IPCC report underscores the potential of cities to act as agents of climate adaptation and provides a framework for cities to build climate-resilient systems. Cities are positioned to pioneer practical, integrative solutions that blend climate sciences with urban planning, establishing frameworks that align economic growth, health equity, environmental sustainability, social justice, and effective governance. This opinion piece explores how cities, by positioning themselves as hubs for innovation, policy reform, and community collaboration, can transform climate vulnerabilities into opportunities for community resilience and sustainability, especially by becoming more-than-human cities, setting examples on the global stage. 

This study quantifies the contribution of individual cloud feedbacks to the total short‐term cloud feedback in satellite observations over the period 2002–2014 and evaluates how they are represented in climate models. The observed positive total cloud feedback is primarily due to positive high‐cloud altitude, extratropical high‐ and low‐cloud optical depth, and land cloud amount feedbacks partially offset by negative tropical marine low‐cloud feedback. Seventeen models from the Atmosphere Model Intercomparison Project of the sixth Coupled Model Intercomparison Project are analyzed. The models generally reproduce the observed moderate positive short‐term cloud feedback. However, compared to satellite estimates, the models are systematically high‐biased in tropical marine low‐cloud and land cloud amount feedbacks and systematically low‐biased in high‐cloud altitude and extratropical high‐ and low‐cloud optical depth feedbacks. Errors in modeled short‐term cloud feedback components identified in this analysis highlight the need for improvements in model simulations of the response of high clouds and tropical marine low clouds. Our results suggest that skill in simulating interannual cloud feedback components may not indicate skill in simulating long‐term cloud feedback components.