This study focuses on the ERCOT electricity market in Texas and demonstrates how climate change is already driving large increases in electricity demand and total electricity costs. Results show that, compared to a 1950-1980 baseline climate, electricity demand was 2.0 GW (4.1%) higher because of the extreme temperatures of that year — climate change contributed 45% 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 $7.7B in 2023 compared to the baseline climate, $295 per ERCOT customer, with most of this increase occurring during the summer. Climate change contributed about 28% of this ($2.2B, $83 per customer), while short-term variability contributed the other 72%. About two-thirds of this increase is due to price increases triggered when the ERCOT grid reaches its physical limits, either in generation or transmission capacity. 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 climate change on the cost of electricity in Texas.

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. Cloud feedback-the radiative response of clouds to changes in temperature-is determined by the contributions from various cloud types and exhibits large uncertainty. Here, we use satellite observations to evaluate how the individual cloud feedback components in response to interannual variability are represented in the latest generation of climate models. The total cloud feedback is positive in the observations, mainly driven by changes in the altitude of high clouds, changes in the cloud cover over land, and changes in the reflectivity of high and low clouds over extratropical regions. The climate models were driven by observed sea-surface temperature, sea-ice conditions, and radiative forcing, so they can be directly compared to the observations. We found the models simulate a total cloud feedback that agrees with the observed positive total cloud feedback in general. However, the models consistently overestimate the decrease of cloud amount over land and in oceanic regions of tropical descent and consistently underestimate the increase of high-cloud altitude and the decrease of extratropical high- and low-cloud optical depth. Models that better simulate cloud feedback in response to short-term fluctuations do not perform better in simulating long-term cloud feedback under global warming. Individual short-term cloud feedback components in climate models are assessed by comparing against satellite observations Model biases are mainly driven by tropical marine low-cloud, high-cloud altitude, and extratropical high-cloud optical depth feedbacks Skill in simulating short-term cloud feedbacks is not correlated with skill in simulating long-term cloud feedbacks

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 degrees 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 degrees 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. Land-surface temperature (LST) from ERA-5 Land are on average warmer than GOES-16 and 17 estimates, but for extremely hot conditions, ERA-5 underestimates LSTERA-5 Land does not accurately represent the magnitude and diurnal cycle of surface urban heat island effectGOES-16 and 17 show higher population exposure to extreme LST compared to ERA-5 Land

The use of energy by cryptocurrency mining comes not just with an environmental cost but also an economic one through increases in electricity prices for other consumers. Here we investigate the increase in wholesale price on Texas ERCOT grid due to energy consumption from cryptocurrency mining. For every GW of cryptocurrency mining load on the grid, we find that the wholesale price of electricity on the ERCOT grid increases by 2 per Cent. Given that todays cryptocurrency mining load on the ERCOT grid is around 1 GW, it suggests that wholesale prices have already risen this amount. There are 27 GW of mining load waiting to be hooked up to the ERCOT grid. If cryptocurrency mining increases rapidly, the price of energy in Texas could skyrocket.

Mortality due to extreme temperatures is one of the most important impacts of climate change. In this analysis, we use historic mortality and temperature data from 106 cities in the United States to develop a model that predicts deaths attributable to temperature. With this model and projections of future temperature from climate models, we estimate temperature-related deaths in the United States due to climate change, changing demographics, and adaptation. We find that temperature-related deaths increase rapidly as the climate warms, but this is mainly due to an expanding and aging population. For global average warming below 3°C above pre-industrial levels, we find that climate change slightly reduces temperature-related mortality in the U.S. because the reduction of cold-related mortality exceeds the increase in heat-related deaths. Above 3°C warming, whether the increase in heat-related deaths exceeds the decrease in cold-related deaths depends on the level of adaptation, emphasizing the need for our society to effectively adapt to climate change that we do not avoid. Most of the reduction in mortality is occurring in the Southern U.S. This region is already well adapted to hot temperatures and the reduction of cold-related mortality drives overall lower mortality. Cities in the Northern U.S. are not well adapted to high temperatures, so the increase in heat-related mortality exceeds the reduction in cold-related mortality. Thus, while the total number of climate-related mortality may not change much, climate change will shift mortality to higher latitudes.

When the Earth warms, the high latitudes often warm more than the low latitudes, a phenomenon commonly known as high latitude amplification. Although high latitude amplification has been observed by both climate data and models, the trajectory of high latitude amplification in our future changing climate is uncertain. Pacific-wide reconstructions of sea surface temperature variability from past climates are important for establishing the historical records of high latitude amplification. Multiple extratropical temperature records have been established for the past 10 million years (Myr). However, it is debated whether the warmest end member, the Western Pacific Warm Pool (WPWP), warmed during the late Miocene (~12 to 5 million years ago, Ma) and Pliocene (5 to 3 Ma). Here we present new multi-proxy, multi-site paleotemperature records from the WPWP. These results, based on lipid biomarkers and foraminiferal Mg/Ca, unequivocally show warmer temperatures in the past, and a secular cooling over the last 10 Myr. We combine these new data, along with the previously established paleotemperature records, to reveal a persistent pattern of change in the Pacific described by a high latitude amplification factor of ~1.7, which does not seem to be affected by the major climate changes over the past 10 Myr. The evolution of spatial temperature gradients in the Pacific is also evident in climate model output and instrumental observations covering the last 160 years, and thus appears to be a robust and predictable feature of the climate system. These results therefore confirm that climate models can capture the major features of past climate change, providing increased confidence in their predictions of future patterns that are likely to be similar to those reconstructed here.

The equilibrium climate sensitivity estimated from different sources is inconsistent due to its dependence on the surface warming pattern. Cloud feedbacks have been identified as the major contributor to this so-called pattern effect. We find a large unforced pattern effect in CERES data, with cloud feedback estimated from two consecutive 125-month periods (March 2000-July 2010 and August 2010-December 2020) changing from -0.45 +/- 0.85 to +1.2 +/- 0.78 W/m(2)/K. When comparing to models, 27% of consecutive 10-year segments in CMIP6 control runs have differences similar to the observations. We also compare the spatial patterns in the CERES data to those in climate models and find they are similar, with the East Pacific playing a key role. This suggests that the impact of the unforced pattern effect can be significant and that models are capable of reproducing its global-average magnitude.

The Electric Reliability Council of Texas (ERCOT) manages the electric power across most of Texas. They make short-term assessments of electricity demand based on historical weather over the last decade or two, thereby ignoring the effects of climate change and the possibility of weather variability outside of the recent historical range. In this paper, we develop an empirical methodology to predict the impact of weather on energy demand. We use that with a large ensemble of climate model runs to construct a probability distribution of power demand on the ERCOT grid for summer and winter 2021. We find that the ERCOT grid is running with no safety margin, particularly during summer. We estimate a 5% chance that maximum power demand would be within 4.3 and 7.9 GW of ERCOT’s estimate of best-case available resources during summer and winter 2021, respectively, and a 20% chance it would be within 7.1 and 17 GW. With such small margins, the unexpected reductions in available power can lead to shortages on the grid. This problem is partially hidden by the fact that ERCOTs seasonal assessments, based entirely on historical weather, are too low. Prior to the 2021 winter blackout, ERCOT forecasted an extreme peak load of 67 GW. In reality, we estimate hourly peak demand was 82 GW, 22% above ERCOT’s most extreme forecast and about equal to the best-case available power. Given the high stakes, ERCOT should develop probabilistic estimates using modern scientific tools to predict the range of power demand more accurately.

Our planet’s energy balance is sensitive to spatial inhomogeneities in sea surface temperature and sea ice changes, but this is typically ignored in climate projections. Here, we show the energy budget during recent decades can be closed by combining changes in effective radiative forcing, linear radiative damping and this pattern effect. The pattern effect is of comparable magnitude but opposite sign to Earth’s net energy imbalance in the 2000s, indicating its importance when predicting the future climate on the basis of observations. After the pattern effect is accounted for, the best-estimate value of committed global warming at present-day forcing rises from 1.31 K (0.99–2.33 K, 5th–95th percentile) to over 2 K, and committed warming in 2100 with constant long-lived forcing increases from 1.32 K (0.94–2.03 K) to over 1.5 K, although the magnitude is sensitive to sea surface temperature dataset. Further constraints on the pattern effect are needed to reduce climate projection uncertainty. Earth’s energy budget depends on the global sea surface temperature pattern, which is currently counteracting warming more strongly than expected in the future. Including this pattern effect in projections causes committed warming with present-day forcing to exceed the Paris goals, implying less leeway than anticipated.

Abstract. This study investigates the impact of global warming on heat and humidity extremes by analyzing 6-hourly output from 28 members of the Max Planck Institute Grand Ensemble driven by forcing from a 1 %/year CO2 increase. We find that unforced variability drives large changes in regional exposure to extremes in different ensemble members, and these variations are mostly associated with ENSO variability. However, while the unforced variability of the climate can alter the occurrence of extremes regionally, variability within the ensemble decreases significantly as one looks at larger regions or at a global population perspective. This means that, for metrics of extreme heat and humidity analyzed here, forced variability of the climate is more important than the unforced variability at global scales. Lastly, we found that most heat wave metrics will increase significantly between 1.5 °C and 2.0 °C, and that low GDP regions shows significant higher risks of facing extreme heat events compared to high GDP regions. Considering the limited economic adaptability of population to heat extremes, this reinforces the idea that the most severe impacts of climate change may fall mostly on those least capable to adapt.

This study evaluates the performance of Coupled Model Intercomparison Project (CMIP) phase 5 and phase 6 models by comparing feedbacks in models to those inferred from observations. Overall, we find no systematic disagreements between the feedbacks in the model ensembles and feedbacks inferred from observations, although there is a wide range in the ability of individual models to reproduce the observations. In particular, 40 of 52 models have best estimates that fall within the uncertainty of the observed total feedback. We quantify two sources of uncertainty in the model ensembles: (1) the structural difference, due to the differences in model parameterizations, and (2) the unforced pattern effect, due to unforced variability, and find that both are important when comparing to an 18-year observational data set. We perform the comparison using two energy balance frameworks: the traditional energy balance framework, in which it is assumed that changes in energy balance are controlled by changes in global average surface temperatures, and an alternative framework that assumes the changes in energy balance are controlled by tropical atmospheric temperatures. We find that the alternative framework provides a more robust way of comparing the models to observations, with both smaller structural differences and smaller unforced pattern effect. However, when considering the relation of feedbacks in response to interannual variability and long-term warming, the traditional framework has advantages. There are no great differences between the CMIP5 and CMIP6 ensembles’ ability to reproduce the observed feedbacks.

We describe our Solar Aerosol and Gas Experiment (SAGE) III/ISS cloud detection algorithm and observations. We identify three types of clouds: visible cirrus (extinction coefficient > 3 x 10−2 km−1), subvisible cirrus (extinction coefficient between 3 x 10−2 km−1 and 10−3 km−1), and very low extinction cloud‐aerosol mixtures (extinction coefficient between 10−3 km−1and 10−4 km−1). Visible cirrus cannot be quantitatively measured by SAGE because of its high extinction, but we infer the presence of cirrus through the termination of the SAGE vertical scan. We then assume that cirrus layers extend 0.5 km below the scan termination height. SAGE cirrus cloud fraction estimated in this way is in qualitative agreement with Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements. Analyzing 2018–2020 SAGE III/ISS data, we find that visible cirrus and subvisible cirrus have nearly equal abundance in the tropical upper troposphere and the average cloud fraction is about 25%. At 16 km, the highest concentration of visible cirrus and subvisible cirrus is over the Tropical West Pacific, central Africa, and central South America during boreal winter. Latitudinal gaps in zonal mean cloud fraction are aligned with descending branch of the residual mean circulation. Using tropopause relative coordinates, we show that maximum cloud‐top heights are consistently restricted to a narrow region near the tropopause. The aerosol extinction fields also show the latitudinal gaps coincident with the descending residual circulation. We identify four anomalous aerosol extinction periods that can be tentatively assigned to significant volcanic or fire events.

We use our forward domain filling trajectory model to explore the impact of tropical convection on stratospheric water vapor (H 2 O) and tropical tropopause layer cloud fraction (TTLCF). Our model results are compared to winter 2008/2009 TTLCF derived from Cloud‐Aerosol Lidar with Orthogonal Polarization and lower stratospheric H 2 O observations from the Microwave Limb Sounder. Convection alters the in situ water vapor by driving the air toward ice saturation relative humidity. If the air is subsaturated, then convection hydrates the air through the evaporation of ice, but if the air is supersaturated, then convective ice crystals grow and precipitate, dehydrating the air. On average, there are a large number of both hydrating and dehydrating convective events in the upper troposphere, but hydrating events exceed dehydrating events. Explicitly adding convection produces a less than 2% increase in global stratospheric water vapor during the period analyzed here. Tropical tropopause temperature is the primary control of stratospheric water vapor, and unless convection extends above the tropopause, it has little direct impact. Less than 1% of the model parcels encounter convection above the analyzed cold‐point tropopause. Convection, on the other hand, has a large impact on TTLCF. The model TTLCF doubles when convection is included, and this sensitivity has implications for the future climate‐related changes, given that tropical convective frequency and convective altitudes may change.

Recently, Anderson et al. (2012, https://doi.org/10.1126/science.1222978, 2017, https://doi. org/10.1073/pnas.1619318114) and Anderson and Clapp (2018, https://doi.org/10.1039/C7CP08331A) proposed that summertime convectively injected water vapor over North America could lead to stratospheric ozone depletion through halogenic catalytic reactions. Such ozone loss would reduce the ozone column and increase erythemal daily dose (EDD). Using 10 years of observations over the North American monsoon region from the Aura Ozone Monitoring Instrument, we find that the column ozone and EDD has a ~0.8–0.9 spatial correlation with lower stratospheric water vapor measured by the Aura Microwave Limb Sounder. We show that this correlation appears to be due to the elevation of the monsoonal tropopause and associated monsoonal convection. The increase in tropopause altitude reduces the ozone column and increases EDD. We see no apparent evidence of substantial heterogeneous chemical ozone loss in lower stratospheric ozone coincident with the stratospheric monsoonal water vapor enhancement.

We investigate the response of stratospheric water vapor (SWV) to different forcing agents within the Precipitation Driver and Response Model Intercomparison Project (PDRMIP) framework. For each model and forcing agent, we break down the SWV response into a slow response, which is coupled to surface temperature changes, and a fast response, which is the response to external forcing but before the sea surface temperatures have responded. Our results show that, for most climate perturbations, the slow SWV response dominates the fast response. The slow SWV response exhibits a similar sensitivity to surface temperature across all climate perturbations. Specifically, the sensitivity is 0.35 ppmv K−1 in the tropical lower stratosphere (TLS), 2.1 ppmv K−1 in the northern hemispheric lowermost stratosphere (LMS), and 0.97 ppmv K−1 in the southern hemispheric LMS. In the TLS, the fast SWV response only dominates the slow SWV response when the forcing agent radiatively heats the cold-point region – for example, black carbon, which directly heats the atmosphere by absorbing solar radiation. The fast SWV response in the TLS is primarily controlled by the fast adjustment of cold-point temperature across all climate perturbations. This control becomes weaker at higher altitudes in the tropics and altitudes below 150 hPa in the LMS.

The goal of this investigation is to understand the mechanism behind the observed high relative humidity with respect to ice (RHi) in the tropical region between ~14 km (150 hPa) and the tropopause, often referred to as the tropical tropopause layer (TTL). As shown by satellite, aircraft, and balloon observations, high (>80%) RHi regions are widespread within the TTL. Regions with the highest RHi are colocated with extensive cirrus. During boreal winter, the TTL RHi is highest over the Tropical Western Pacific (TWP) with a weaker maximum over South America and Africa. In the winter, TTL temperatures are coldest and upward motion is the greatest in the TWP. It is this upward motion, driving humid air into the colder upper troposphere that produces the persistent high RHi and cirrus formation. Back trajectory calculations show that comparable adiabatic and diabatic processes contribute to this upward motion. We construct a bulk model of TWP TTL water vapor transport that includes cloud nucleation and ice microphysics that quantifies how upward motion drives the persistent high RHi in the TTL region. We find that atmospheric waves triggering cloud formation regulate the RHi and that convection dehydrates the TTL. Our forward domain‐filling trajectory model is used to more precisely simulate the TTL spatial and vertical distribution of RHi. The observed RHi distribution is reproduced by the model, and we show that convection increases RHi below the base of the TTL with little impact on the RHi in the TTL region.

The transient climate response (TCR), defined to be the warming in near‐surface air temperature after 70 years of a 1% per year increase in CO 2 , can be estimated from observed warming over the nineteenth and twentieth centuries. Such analyses yield lower values than TCR estimated from global climate models (GCMs). This disagreement has been used to suggest that GCMs' climate may be too sensitive to increases in CO 2 . Here we critically evaluate the methodology of the comparison using a large ensemble of a fully coupled GCM simulating the historical period, 1850–2005. We find that TCR estimated from model simulations of the historical period can be much lower than the model's true TCR, replicating the disagreement seen between observations and GCM estimates of TCR. This suggests that the disagreement could be explained entirely by the methodology of the comparison and undercuts the suggestions that GCMs overestimate TCR.

This study investigates potential biases between equilibrium climate sensitivity inferred from warming over the historical period (ECShist) and the climate system's true ECS (ECStrue). This paper focuses on two factors that could contribute to differences between these quantities. First is the impact of internal variability over the historical period: our historical climate record is just one of an infinity of possible trajectories, and these different trajectories can generate ECShist values 0.3 K below to 0.5 K above (5%-95% confidence interval) the average ECShist. Because this spread is due to unforced variability, I refer to this as the unforced pattern effect. This unforced pattern effect in the model analyzed here is traced to unforced variability in loss of sea ice, which affects the albedo feedback, and to unforced variability in warming of the troposphere, which affects the shortwave cloud feedback. There is also a forced pattern effect that causes ECShist to depart from ECStrue due to differences between today's transient pattern of warming and the pattern of warming at 2xCO(2) equilibrium. Changes in the pattern of warming lead to a strengthening low-cloud feedback as equilibrium is approached in regions where surface warming is delayed: the Southern Ocean, eastern Pacific, and North Atlantic near Greenland. This forced pattern effect causes ECShist to be on average 0.2 K lower than ECStrue (similar to 8%). The net effect of these two pattern effects together can produce an estimate of ECShist as much as 0.5 K below ECStrue.