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Towards the end of June 2021, temperature records were broken by several degrees Celsius in several cities in the Pacific Northwest areas of the US and Canada, leading to spikes in sudden deaths and sharp increases in emergency calls and hospital visits for heat-related illnesses. Here we present a multi-model, multi-method attribution analysis to investigate the extent to which human-induced climate change has influenced the probability and intensity of extreme heat waves in this region. Based on observations, modelling and a classical statistical approach, the occurrence of a heat wave defined as the maximum daily temperature (TXx) observed in the area 45-52N, 119-123W, was found to be virtually impossible without human-caused climate change. The observed temperatures were so extreme that they lay far outside the range of historical temperature observations. This makes it hard to state with confidence how rare the event was. Using a statistical analysis that assumes that the heat wave is part of the same distribution as previous heat waves in this region led to a first-order estimation of the event frequency of the order of once in 1000 years under current climate conditions. Using this assumption and combining the results from the analysis of climate models and weather observations, we found that such a heat wave event would be at least 150 times less common without human-induced climate change. Also, this heat wave was about 2 C hotter than a 1-in-1000-year heat wave would have been in 1850-1900, when global mean temperatures were 1.2 C cooler than today. Looking into the future, in a world with 2 C of global warming (0.8 C warmer than today), a 1000-year event would be another degree hotter. Our results provide a strong warning: our rapidly warming climate is bringing us into uncharted territory with significant consequences for health, well-being and livelihoods. Adaptation and mitigation are urgently needed to prepare societies for a very different future.

Abstract: Global Climate Models (GCMs) exhibit substantial biases in their simulation of tropical climate. One particularly problematic bias exists in GCMs' simulation of the tropical rainband known as the Intertropical Convergence Zone (ITCZ). Much of the precipitation on Earth falls within the ITCZ, which plays a key role in setting Earth's temperature by affecting global energy transports, and partially dictates dynamics of the largest interannual mode of climate variability: The El Niño‐Southern Oscillation (ENSO). Most GCMs fail to simulate the mean state of the ITCZ correctly, often exhibiting a “double ITCZ bias,” with rainbands both north and south rather than just north of the equator. These tropical mean state biases limit confidence in climate models' simulation of projected future and paleoclimate states, and reduce the utility of these models for understanding present climate dynamics. Adjusting GCM parameterizations of cloud processes and atmospheric convection can reduce tropical biases, as can artificially correcting sea surface temperatures through modifications to air‐sea fluxes (i.e., “flux adjustment”). Here, we argue that a significant portion of these rainfall and circulation biases are rooted in orographic height being biased low due to assumptions made in fitting observed orography onto GCM grids. We demonstrate that making different, and physically defensible, assumptions that raise the orographic height significantly improves model simulation of climatological features such as the ITCZ and North American rainfall as well as the simulation of ENSO. These findings suggest a simple, physically based, and computationally inexpensive method that can improve climate models and projections of future climate.

A key question for infectious disease dynamics is the impact of the climate on future burden. Here, we evaluate the climate drivers of respiratory syncytial virus (RSV), an important determinant of disease in young children. We combine a dataset of county-level observations from the US with state-level observations from Mexico, spanning much of the global range of climatological conditions. Using a combination of nonlinear epidemic models with statistical techniques, we find consistent patterns of climate drivers at a continental scale explaining latitudinal differences in the dynamics and timing of local epidemics. Strikingly, estimated effects of precipitation and humidity on transmission mirror prior results for influenza. We couple our model with projections for future climate, to show that temperature-driven increases to humidity may lead to a northward shift in the dynamic patterns observed and that the likelihood of severe outbreaks of RSV hinges on projections for extreme rainfall.

Responses of tropical cyclones (TCs) to CO2 doubling are explored using coupled global climate models (GCMs) with increasingly refined atmospheric/land horizontal grids (~ 200 km, ~ 50 km and ~ 25 km). The three models exhibit similar changes in background climate fields thought to regulate TC activity, such as relative sea surface temperature (SST), potential intensity, and wind shear. However, global TC frequency decreases substantially in the 50 km model, while the 25 km model shows no significant change. The ~ 25 km model also has a substantial and spatially-ubiquitous increase of Category 3–4–5 hurricanes. Idealized perturbation experiments are performed to understand the TC response. Each model’s transient fully-coupled 2 × CO2 TC activity response is largely recovered by “time-slice” experiments using time-invariant SST perturbations added to each model’s own SST climatology. The TC response to SST forcing depends on each model’s background climatological SST biases: removing these biases leads to a global TC intensity increase in the ~ 50 km model, and a global TC frequency increase in the ~ 25 km model, in response to CO2-induced warming patterns and CO2 doubling. Isolated CO2 doubling leads to a significant TC frequency decrease, while isolated uniform SST warming leads to a significant global TC frequency increase; the ~ 25 km model has a greater tendency for frequency increase. Global TC frequency responds to both (1) changes in TC “seeds”, which increase due to warming (more so in the ~ 25 km model) and decrease due to higher CO2 concentrations, and (2) less efficient development of these“seeds” into TCs, largely due to the nonlinear relation between temperature and saturation specific humidity.

Tropical cyclone intensification processes are explored in six high-resolution climate models. The analysis framework employs process-oriented diagnostics that focus on how convection, moisture, clouds, and related processes are coupled. These diagnostics include budgets of column moist static energy and the spatial variance of column moist static energy, where the column integral is performed between fixed pressure levels. The latter allows for the quantification of the different feedback processes responsible for the amplification of moist static energy anomalies associated with the organization of convection and cyclone spinup, including surface flux feedbacks and cloud-radiative feedbacks. Tropical cyclones (TCs) are tracked in the climate model simulations and the analysis is applied along the individual tracks and composited over many TCs. Two methods of compositing are employed: a composite over all TC snapshots in a given intensity range, and a composite over all TC snapshots at the same stage in the TC life cycle (same time relative to the time of lifetime maximum intensity for each storm). The radiative feedback contributes to TC development in all models, especially in storms of weaker intensity or earlier stages of development. Notably, the surface flux feedback is stronger in models that simulate more intense TCs. This indicates that the representation of the interaction between spatially varying surface fluxes and the developing TC is responsible for at least part of the intermodel spread in TC simulation.

Prior global climate model (GCM) experiments have shown that the Tibetan Plateau and related orography play a significant role in enhancing the Indian Monsoon, particularly during its onset, and the East Asian monsoon. However, these experiments have been largely performed with atmosphere-only, lower-resolution GCMs that neglect the influence of atmosphere–ocean coupling, and do not resolve tropical cyclones (TCs). Here we explore the influence of Asian orography on tropical circulations in a Geophysical Fluid Dynamics Laboratory GCM at two different atmosphere/land resolutions (∼ 50 and 200 km), and with or without atmosphere–ocean coupling. Atmosphere–ocean coupling is found to play a significant role in the precipitation response due to the Asian orography, enhancing the precipitation increase over the Western North Pacific (hereafter WNP), and drying the Arabian Sea. In these same regions, the higher resolution model, which resolves TCs up to category 3, suggests that Asian orography has a significant influence on TCs, increasing TC frequency in the WNP, and decreasing it in the Arabian Sea. However, in contrast to precipitation, this TC response does not appear to be strongly affected by the atmosphere–ocean coupling. Connections between the direct atmospheric circulation response to Asian orography, ocean circulation changes, and these various effects on precipitation and tropical cyclones are analyzed and discussed.

The temporal structure of heat waves having substantial human impact varies widely, with many featuring a compound structure of hot days interspersed with cooler breaks. In contrast, many heat wave definitions employed by meteorologists include a continuous threshold-exceedance duration criterion. This study examines the hazard of these diverse sequences of extreme heat in the present, and their change with global warming. We define compound heat waves to include those periods with additional hot days following short breaks in heat wave duration. We apply these definitions to analyze daily temperature data from observations, NOAA Geophysical Fluid Dynamics Laboratory global climate model simulations of the past and projected climate, and synthetically generated time series. We demonstrate that compound heat waves will constitute a greater proportion of heat wave hazard as the climate warms and suggest an explanation for this phenomenon. This result implies that in order to limit heat-related mortality and morbidity with global warming, there is a need to consider added vulnerability caused by the compounding of heat waves.

Projections of future tropical cyclone frequency are uncertain, ranging from a slight increase to a considerable decrease according to climate models. Estimation of how much the Earth's surface temperature warms in response to greenhouse gas increase, quantified by effective climate sensitivity, is also uncertain. These two uncertainties have historically been studied independently as they concern different scales: One quantifies the extreme weather and the other the mean climate. Here, we show that these two uncertainties are not independent and are both influenced by the response of tropical clouds to warming. Across climate models, we show an anticorrelation between shortwave cloud radiative feedback and changes in the frequency of seed vortices, a prevalent type of tropical cyclone precursors. We further show an anticorrelation between effective climate sensitivity and tropical cyclone frequency changes, suggesting that global tropical cyclone frequency tends to decrease more substantially in models with larger temperature increase. [ABSTRACT FROM AUTHOR]

Future changes in the North American monsoon, a circulation system that brings abundant summer rains to vast areas of the North American Southwest, could have significant consequences for regional water resources. How this monsoon will change with increasing greenhouse gases, however, remains unclear, not least because coarse horizontal resolution and systematic sea-surface temperature biases limit the reliability of its numerical model simulations. Here we investigate the monsoon response to increased atmospheric carbon dioxide (CO 2) concentrations using a 50-km-resolution global climate model which features a realistic representation of the monsoon climatology and its synoptic-scale variability. It is found that the monsoon response to CO 2 doubling is sensitive to sea-surface temperature biases. When minimizing these biases, the model projects a robust reduction in monsoonal precipitation over the southwestern United States, contrasting with previous multi-model assessments. Most of this precipitation decline can be attributed to increased atmospheric stability, and hence weakened convection, caused by uniform sea-surface warming. These results suggest improved adaptation measures, particularly water resource planning, will be required to cope with projected reductions in monsoon rainfall in the American Southwest.

The interaction between clouds and radiation is a key process within the climate system, and assessing the impacts of that interaction provides valuable insights into both the present-day climate and future projections. Many modeling experiments have been designed over the years to probe the impact of the cloud radiative effect (CRE) on the climate, including those that seek to disrupt the mean CRE effect and those that only disrupt the covariance of the CRE with the circulation. Seven such experimental designs have been added to the Energy Exascale Earth System Model version 1 (E3SMv1) of the US Department of Energy. These experiments include both the first and second iterations of the Clouds On/Off Klimate Intercomparison Experiment (COOKIE) experimental design, as well as the cloud-locking method. This paper documents the code changes necessary to implement such experiments and also provides detailed instructions for how to run them. Analyses across experiment types provide valuable insights and confirm the findings of prior studies, including the role of cloud radiative heating toward intensifying the monsoon, intensifying rain rates, and poleward expansion of the general circulation owing to cloud feedbacks. [ABSTRACT FROM AUTHOR]

Arid extratropical Asia (AEA) is bisected at the wetter Tian Shan (a northern offshoot of the Tibetan Plateau) into east and west deserts, each with unique climatological characteristics. The east deserts (~35°- 55°N, ~75°-115°E) have a summer precipitation maximum, and the west deserts (~35°-55°N, ~45°-75°E) have a winter-spring precipitation maximum. A new high-resolution (50 km atmosphere-land) global coupled climate model is run with the Tian Shan removed to determine whether these mountains are responsible for the climatological east-west differentiation of AEA. Multicentennial simulations for the Control and NoTianshan runs highlight statistically significant effects of the Tian Shan. Overall, the Tian Shan are found to enhance the precipitation seasonality gradient across AEA, mostly through altering the east deserts. The Tian Shan dramatically change the precipitation seasonality of the Taklimakan Desert directly to its east (the driest part of AEA) by blocking west winter precipitation, enhancing subsidence over this region, and increasing east summer precipitation. The Tian Shan increase east summer precipitation through two mechanisms: 1) orographic precipitation, which is greatest on the eastern edge of the Tian Shan in summer, and 2) remote enhancement of the East Asian summer monsoon through alteration of the larger-scale seasonal mean atmospheric circulation. The decrease in east winter precipitation also generates remote warming of the Altai and Kunlun Shan, mountains northeast and southeast of the Tian Shan, respectively, due to reduction of snow cover and corresponding albedo decrease.

Although a theory of the climatology of tropical cyclone formation remains elusive, high-resolution climate models can now simulate many aspects of tropical cyclone climate.