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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.

 

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.

 

Mortality due to extreme temperatures is one of the most worrying 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. Southern U.S. cities are 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 in the U.S. to higher latitudes.

 

The Electric Reliability Council of Texas (ERCOT) manages the electric power across most of Texas. They make short-term assessments of electricity demand on the basis of historical weather over the last two decades, thereby ignoring the effects of climate change and the possibility of weather variability outside the recent historical range. In this paper, we develop an empirical method 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 most severe weather events will use 100% of available power—if anything goes wrong, as it did during the 2021 winter, there will not be sufficient available power. More quantitatively, 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. The shortage of power on the ERCOT grid is partially hidden by the fact that ERCOTs seasonal assessments, which are based entirely on historical weather, are too low. Prior to the 2021 winter blackout, ERCOT forecast 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.

 

Abstract. This study investigates the impact of global warming on heat and humidityextremes by analyzing 6 h output from 28 members of the Max PlanckInstitute Grand Ensemble driven by forcing from a 1 % yr−1 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 El Niño–Southern Oscillation (ENSO) variability. However, while the unforced variability in 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 in 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 and 2.0 ∘C, and that low gross domestic product (GDP) regions show significantly higher risks of facing extreme heat events compared to high GDP regions. Considering the limited economic adaptability of the population to heat extremes, this reinforces the idea that the most severe impacts of climate change may fall mostly on those least capable of adapting. 

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/m²/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.

 

This study investigates potential biases between equilibrium climate sensitivity inferred from warming over the historical period (ECS_hist) and the climate system’s true ECS (ECS_true). 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 ECS_histvalues 0.3 K below to 0.5 K above (5%–95% confidence interval) the average ECS_hist. 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 ECS_histto depart from ECS_truedue to differences between today’s transient pattern of warming and the pattern of warming at 2×CO₂equilibrium. 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 ECS_histto be on average 0.2 K lower than ECS_true(~8%). The net effect of these two pattern effects together can produce an estimate of ECS_histas much as 0.5 K below ECS_true.

 

Abstract. Our climate is constrained by the balance between solar energy absorbed by the Earth and terrestrial energy radiated tospace. This energy balance has been widely used to infer equilibrium climate sensitivity (ECS) from observations of20th-century warming. Such estimates yield lower values than other methods, and these have been influential in pushing downthe consensus ECS range in recent assessments. Here we test the method using a 100-member ensemble of the Max Planck Institute Earth System Model(MPI-ESM1.1) simulations of the period 1850–2005 with known forcing. We calculate ECS in each ensemble member usingenergy balance, yielding values ranging from 2.1 to 3.9K. The spread in the ensemble is related to the centralassumption in the energy budget framework: that global average surface temperature anomalies are indicative of anomaliesin outgoing energy (either of terrestrial origin or reflected solar energy). We find thatthis assumption is not well supportedover the historical temperature record in the model ensemble or more recent satellite observations. We find that framingenergy balance in terms of 500hPa tropical temperature better describes the planet's energy balance.