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The Max Planck Institute Grand Ensemble (MPI‐GE) is the largest ensemble of a single comprehensive climate model currently available, with 100 members for the historical simulations (1850–2005) and four forcing scenarios. It is currently the only large ensemble available that includes scenario representative concentration pathway (RCP) 2.6 and a 1% CO2 scenario. These advantages make MPI‐GE a powerful tool. We present an overview of MPI‐GE, its components, and detail the experiments completed. We demonstrate how to separate the forced response from internal variability in a large ensemble. This separation allows the quantification of both the forced signal under climate change and the internal variability to unprecedented precision. We then demonstrate multiple ways to evaluate MPI‐GE and put observations in the context of a large ensemble, including a novel approach for comparing model internal variability with estimated observed variability. Finally, we present four novel analyses, which can only be completed using a large ensemble. First, we address whether temperature and precipitation have a pathway dependence using the forcing scenarios. Second, the forced signal of the highly noisy atmospheric circulation is computed, and different drivers are identified to be important for the North Pacific and North Atlantic regions. Third, we use the ensemble dimension to investigate the time dependency of Atlantic Meridional Overturning Circulation variability changes under global warming. Last, sea level pressure is used as an example to demonstrate how MPI‐GE can be utilized to estimate the ensemble size needed for a given scientific problem and provide insights for future ensemble projects. Key Points The 100‐member MPI‐GE is currently the largest publicly available ensemble of a comprehensive climate model MPI‐GE currently has the most forcing scenarios of all large ensemble projects: RCP2.6, RCP4.5, RCP8.5, and 1% CO2 The power of MPI‐GE is to estimate the forced response and internal variability, including changing variability, to unprecedented precision

Single-model initial-condition large ensembles are powerful tools to quantify the forced response, internal climate variability, and their evolution under global warming. Here, we present the CMIP6 version of the Max Planck Institute Grand Ensemble (MPI-GE CMIP6) with currently 30 realizations for the historical period and five emission scenarios. The power of MPI-GE CMIP6 goes beyond its predecessor ensemble MPI-GE by providing high-frequency output, the full range of emission scenarios including the highly policy-relevant low emission scenarios SSP1-1.9 and SSP1-2.6, and the opportunity to compare the ensemble to complementary high-resolution simulations. First, we describe MPI-GE CMIP6, evaluate it with observations and reanalyzes and compare it to MPI-GE. Then, we demonstrate with six application examples how to use the power of the ensemble to better quantify and understand present and future climate extremes, to inform about uncertainty in approaching Paris Agreement global warming limits, and to combine large ensembles and artificial intelligence. For instance, MPI-GE CMIP6 allows us to show that the recently observed Siberian and Pacific North American heatwaves would only avoid reaching 1–2 years return periods in 2071–2100 with low emission scenarios, that recently observed European precipitation extremes are captured only by complementary high-resolution simulations, and that 3-hourly output projects a decreasing activity of storms in mid-latitude oceans. Further, the ensemble is ideal for estimates of probabilities of crossing global warming limits and the irreducible uncertainty introduced by internal variability, and is sufficiently large to be used for infilling surface temperature observations with artificial intelligence.

Changes in temperature variability affect the frequency and intensity of extreme events, as well as the regional range of temperatures that ecosystems and society need to adapt to. While accurate projections of temperature variability are vital for understanding climate change and its impacts, they remain highly uncertain. We use rank-frequency analysis to evaluate the performance of eleven single model initial-condition large ensembles (SMILEs) against observations in the historical period, and use those that best represent historical regional variability to constrain projections of future temperature variability. Constrained projections from the best-performing SMILEs still show large uncertainties in the intensity and the sign of the variability change for large areas of the globe. Our results highlight poorly modelled regions where observed variability is not well represented such as large parts of Australia, South America, and Africa, particularly in their local summer season, underscoring the need for further modelling improvements over crucial regions. In these regions, the constrained projected change is typically larger than in the unconstrained ensemble, suggesting that in these regions, multi-model mean projections may underestimate future variability change. Constraining projections using best-performing climate models, uncertainties in future temperature variability can be narrowed, yet remain large over poorly modelled regions, highlighting the need for multiple model ensembles to assess future risks.

Recent studies have revealed strong trends in humid heat, including the nearing of human physiological limits in some regions. Understanding of past extremes and their meaningfulness for contextualizing future possibilities, especially in the near‐term, is limited by the absence of a global analysis focused on the most extreme humid‐heat‐anomaly events. Here we identify record‐setting humid‐heat days for 216 global regions and assess the likelihood of these records being broken even under present‐day climate forcing. We use several reanalyses as a historical catalogue, and large climate‐model ensembles to represent other statistically plausible events. Unlike the spatial pattern of large temperature anomalies, we find that humid‐heat anomalies are most intense, and most seasonally and interannually concentrated, in the deep tropics and arid subtropics. Many top events have attracted little if any prior attention. The eastern United States is especially susceptible to record‐breaking humid heat due to modest current records (>1% inferred annual exceedance probability) contrasting with numerous simulated large‐anomaly days. Australia and eastern China are also prone to locally exceptional episodes, with >40% of ensemble members simulating events exceeding the ERA5‐based distribution maximum. Model biases for key characteristics, together with the observed record‐setting day affecting its estimated return period by >2.5x in half of regions, underline several valuable aspects of a joint observation/model perspective on humid heat. This approach aids in evaluating the plausibility of as‐yet‐unseen extremes; identifying regions of concern that might otherwise be overlooked and underprepared; and gauging regionally specific correlations between event magnitudes and societal impacts. Plain Language Summary Surprisingly little is known about what the most extreme humid heat ever observed looks like on a regional basis. Understanding these upper limits of past experience would aid in ascertaining how as‐yet‐unseen extremes might affect human and natural systems. Using climate models and observations, we identify areas with the most intense humid heat versus ones with more modest records, and connect those patterns to the El Niño‐Southern Oscillation and other drivers. Our two‐pronged statistical approach highlights the eastern US and much of Australia as particularly susceptible to having their current records broken—in other words, they have a high statistical likelihood of event magnitudes beyond those experienced in recent decades. We also evaluate climate models against reanalyses and identify systematic model biases in humid‐heat magnitude, duration, and propensity for temporal clustering. Our results draw attention to, and aid in quantifying, impactful record‐breaking humid heat, especially in the tropics and subtropics where adaptation efforts should especially be focused. Key Points The largest and most‐clustered humid‐heat anomalies occur in low latitudes, and have not been well captured by in situ networks Model ensembles can identify regions prone to large anomalies, while reanalyses can identify those with modest current record maxima The single record‐setting day often affects its estimated return period by a factor of 3 or more, so good data on extremes is crucial

Societally relevant weather impacts typically result from compound events, which are rare combinations of weather and climate drivers. Focussing on four event types arising from different combinations of climate variables across space and time, here we illustrate that robust analyses of compound events — such as frequency and uncertainty analysis under present-day and future conditions, event attribution to climate change, and exploration of low-probability-high-impact events — require data with very large sample size. In particular, the required sample is much larger than that needed for analyses of univariate extremes. We demonstrate that Single Model Initial-condition Large Ensemble (SMILE) simulations from multiple climate models, which provide hundreds to thousands of years of weather conditions, are crucial for advancing our assessments of compound events and constructing robust model projections. Combining SMILEs with an improved physical understanding of compound events will ultimately provide practitioners and stakeholders with the best available information on climate risks. The authors show that robust analyses of high-impact compound weather and climate events require many samples. Thus, they argue that large ensemble climate model simulations should be used to provide the best available information on climate risks.

Climate‐related extreme events impose a heavy toll on humankind, and many will likely become more frequent in the future. The compound (joint) occurrence of different climate‐related hazards and impacts can further exacerbate the detrimental consequences for society. By analyzing postprocessed data from the Inter‐Sectoral Impact Model Intercomparison Project, we provide a global mapping of future changes in the compound occurrence of six categories of hazards or impacts related to climate extremes. These are: river floods, droughts, heatwaves, wildfires, tropical cyclone‐induced winds and crop failures. In line with the existing literature, we find sharp increases in the occurrence of many individual hazards and impacts, notably heatwaves and wildfires. Under a medium‐high emission scenario, many regions worldwide transition from chiefly experiencing a given category of hazard or impact in isolation to routinely experiencing compound hazard or impact occurrences. A similarly striking change is projected for the future recurrence of compound hazards or impacts, with many locations experiencing specific compound occurrences at least once a year for several years, or even decades, in a row. In the absence of effective global climate mitigation actions, we may thus witness a qualitative regime shift from a world dominated by individual climate‐related hazards and impacts to one where compound occurrences become the norm. Plain Language Summary Climate‐related extreme events often result in large and negative societal impacts, and many such events are likely to become more frequent in the future. The joint occurrence of different climate‐related extreme events can lead to even larger impacts than those of extremes occurring in isolation. In the absence of effective global climate mitigation to minimize the ongoing climatic change, we find that many regions worldwide transition from chiefly experiencing extreme events in isolation to routinely experiencing the joint occurrence of different climate‐related extreme events. Such joint occurrences may repeatedly affect the same region for several years, or even decades, in a row. Key Points Under a medium‐high emission scenario, compound hazard or impact occurrences will become the norm in many regions Under such scenario, many regions will experience specific compound occurrences for several years, or even decades, in a row In the absence of global climate mitigation, we project a regime shift to a world dominated by compound hazards or impacts

We use the 100-member Max Planck Institute Grand Ensemble (MPI-GE) to disentangle the contributions from colocated dynamic atmospheric conditions and local thermodynamic effects of moisture limitation as drivers of variability in European summer heat extremes. Using a novel extreme event definition, we find that heat extremes with respect to the evolving mean climate increase by 70% under a moderate warming scenario during the twenty-first century. With a multiple regression approach, we find that the dynamical mechanisms representing blocking and anticyclonic conditions are the main driver of variability in extreme European summer temperatures, both in past and future climates. By contrast, local thermodynamic drivers play a secondary role in explaining the total variability in extreme temperatures. We also find that considering both dynamical and thermodynamical sources of variability simultaneously is crucial. Assessing only one type of drivers leads to an overestimation of their effect on extreme temperatures, particularly when considering only thermodynamical drivers. Lastly, we find that although most past and future heat extremes occur under favorable dynamical atmospheric conditions; this occurs 10–40% less frequently over Central Europe in the twenty-first century. By contrast, heat extremes over Central Europe occur 40% more frequently under concurrent extreme moisture limitation in the twenty-first Century. Our findings highlight a new type of neutral-atmosphere, moisture-driven heat extremes, and confirm that the increase in European heat extremes and associated variability increase are dominated by the local thermodynamic effect of moisture limitation.

Increases in climate hazards and their impacts mark one of the major challenges of climate change. Situations in which hazards occur close enough to one another to result in amplified impacts, because systems are insufficiently resilient or because hazards themselves are made more severe, are of special concern. We consider projected changes in such compounding hazards using the Max Planck Institute Grand Ensemble under a moderate (RCP4.5) emissions scenario, which produces warming of about 2.25 °C between pre-industrial (1851–1880) and 2100. We find that extreme heat events occurring on three or more consecutive days increase in frequency by 100%–300%, and consecutive extreme precipitation events increase in most regions, nearly doubling for some. The chance of concurrent heat and drought leading to simultaneous maize failures in three or more breadbasket regions approximately doubles, while interannual wet-dry oscillations become at least 20% more likely across much of the subtropics. Our results highlight the importance of taking compounding climate extremes into account when looking at possible tipping points of socio-environmental systems.

In recent years, European summer heatwaves have strongly intensified due to rising anthropogenic emissions. While European summer heatwaves will continue to intensify due to the warming of summer temperatures, the effects of the changes in internal variability under global warming remain unknown. Employing five single-model initial-condition large ensembles, we find that the forced changes in internal variability are projected to intensify central and northern European summer heatwaves. Central and northern Europe will experience frequent moisture limitations, enhancing land-atmosphere feedback and increasing heatwave intensity and variability. In contrast, the forced changes in internal variability will contribute to weakening southern European summer heatwaves. Southern Europe is projected to face a more stable moisture-depleted environment that reduces extreme temperature variability and heatwave intensity. Our findings imply that while adaptation to increasing mean temperatures in southern Europe should suffice to reduce the vulnerability to increasing EuSHW intensity, in central and northern Europe adaptation to increased temperature variability will also be needed. Projected changes in internal climate variability are expected to amplify heatwave intensity in central and northern Europe—making these events more severe and less predictable—while dampening the trend in southern Europe.

Extreme heat and drought typical of an end-of-century climate could soon occur over Europe, and repeatedly. Despite the European climate being potentially prone to multi-year successive extremes due to the influence of the North Atlantic variability, it remains unclear how the likelihood of successive extremes changes under warming, how early they could reach end-of-century levels, and how this is affected by internal climate variability. Using the Max Planck Institute Grand Ensemble, we find that even under moderate warming, end-of-century heat and drought levels virtually impossible 20 years ago reach 1-in-10 likelihoods as early as the 2030s. By 2050–2074, two successive years of single or compound end-of-century extremes, unprecedented to date, exceed 1-in-10 likelihoods; while Europe-wide 5-year megadroughts become plausible. Whole decades of end-of-century heat stress could start by 2040, by 2020 for drought, and with a warm North Atlantic, end-of-century decades starting as early as 2030 become twice as likely.