We explore the extent to which internal variability can reconcile discrepancies between observed and simulated warming in the upper tropical troposphere. We compare all extant radiosonde‐based ...estimates for the period 1958–2014 to simulations from the Coupled Model Intercomparison Project phase 5 multimodel ensemble and the 100 realization Max Planck Institute large ensemble. We consider annual mean temperatures and all available 30‐and 15‐year trends. Most observed trends fall within the ensemble spread for most of the record, and trends calculated over 15‐year periods show better agreement than 30‐year trends, with generally larger discrepancies for the older observational products. The simulated amplification of surface warming aloft in the troposphere is consistent with observations, and the linear correlation between surface and simultaneous tropospheric warming trends decreases with trend length. We conclude that trend differences between observations and simulations of tropical tropospheric temperatures are dominated by observational uncertainty and chaotic internal variability rather than by systematic errors in model performance.
Key Points
Radiosonde observations show good agreement with a single‐model 100 member ensemble of simulations for the majority of observational record
Largest trend differences for amplification of tropospheric over surface warming aloft for 30‐year trends occur in second half of the record
Differences in observed and simulated tropical tropospheric temperature trends arise from observational uncertainty and internal variability
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
We use a decadal prediction system with the Coupled Model Intercomparison Project Phase 6 version of the coupled Max Planck Institute Earth System Model to predict the probability of occurrence for ...extremely warm summers in the Northern Hemisphere. An assimilation run with Max Planck Institute Earth System Model shows a robust response of summer temperature extremes in northern Europe and northeast Asia to North Atlantic sea surface temperature via a circumglobal Rossby wavetrain. When the North Atlantic is warm, warm summer temperature extremes occur with a probability of 20% and 24% in northern Europe and northeast Asia, respectively. In a cold North Atlantic phase, these probabilities are 0% and 8%. A similar difference in probability of occurrence is found in the initialized climate predictions. Consequently, the likelihood of a warm summer temperature extreme occurring in the examined regions in the next 10 years can be inferred from predictions of North Atlantic temperature.
Plain Language Summary
Extremely warm summers can have a substantial impact on society. Trustworthy predictions of such events several years ahead could help in anticipating the extremes and managing their impacts. In this study, we show that the probability with which a warm summer temperature extreme occurs in northern Europe and northeast Asia can be linked to the surface temperature of the North Atlantic ocean. We further show that North Atlantic ocean surface temperature and the connection between ocean temperature and extreme summer temperature can be predicted. As a result, the probability for extremely warm summers to occur in northern Europe and northeast Asia in the next 10 years can be predicted.
Key Points
Extremely warm summers in northern Europe and northeast Asia occur more frequently when the North Atlantic is warm than when it is cold
A set of initialized CMIP6 decadal hindcasts predicts this dependence of summer temperature extremes on North Atlantic temperature
The likelihood of extremely warm summers to occur in the next 10 years can be inferred from predictions of North Atlantic ocean temperature
Abstract
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.
Plain Language Summary
Climate model simulations that start from different initial states and differ only due to the chaos in the climate system are used extensively to quantify the forced climate response, variability intrinsic to the climate system, and their change under global warming. Here, we present a new version of the Max Planck Institute Grand Ensemble (MPI‐GE CMIP6) that is run as part of the latest generation of climate models. This single‐model ensemble currently consists of 30 realizations for the historical period 1850–2014 and for five scenarios of possible future climates until 2100. The power of MPI‐GE CMIP6 goes beyond its predecessor by not only providing monthly mean but also 3‐hourly to daily model output, the full range of future scenarios including the two highly policy‐relevant scenarios that were designed to match the Paris Agreement global warming limits of 1.5 and 2°C, and the opportunity to compare the low‐resolution ensemble to simulations of the same model version with higher horizontal resolution. In this paper, we describe the new ensemble and demonstrate with application examples how to use its power.
Key Points
The Max Planck Institute Grand Ensemble in its CMIP6 version (MPI‐GE CMIP6) is a 30‐member initial‐condition large ensemble with up to 3‐hourly model output and five emission scenarios
The ensemble is specifically suited to investigate climate extremes and Paris Agreement global warming limits
MPI‐GE CMIP6 adequately represents heat extremes, while precipitation extremes are captured by complementary high‐resolution simulations
Humid heat presents a major societal challenge through its impacts on human health, energy demand, and economic productivity, underlined by the projected emergence of conditions beyond human ...tolerance. However, systematic assessment of what drives the most extreme humid heat worldwide has been lacking. Here, we investigate factors determining the location and magnitude of humid‐heat extremes, framing our analysis around the four regions with the highest values: the southern Persian Gulf, north‐central Pakistan, eastern South Asia, and the western Amazon. We find that strong boundary‐layer moisture fluxes, together with stability that inhibits moist convection, explain well the timing and location of near‐surface humid‐heat extremes. These favorable conditions are achieved through regionally distinct factors, including shallow sea breezes in the Persian Gulf and large‐scale subsidence in eastern South Asia. Our results demonstrate some of the principal controls on the most intense humid heat, both globally and for particular regions and heat events.
Plain Language Summary
Combinations of high temperatures and high humidity are a primary threat of climate change because they can be not only uncomfortable but deadly, and are growing rapidly in frequency and intensity. In this study, we present a general theory for where the most extreme such events occur and describe the precise meteorological conditions that favor them. We are therefore able to state with new confidence the reasons why extreme humid heat events take place where they do, and why. Our results also highlight the regional distinctions in the factors (such as winds or ocean temperatures) that contribute to these events, increasing the strength of the evidence that some regions are much more sensitive to certain factors than others—information that can be used to guide better forecasts as well as climate‐change projections.
Key Points
A systematic assessment of humid‐heat hotspots reveals the dual importance of vertical stability and low‐level moisture sources
These two conditions are achieved through markedly different meteorological processes and in markedly different geographical contexts
Understanding the most extreme humid heat requires applying global principles to detailed regional analyses across space and time scales
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.
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.
Abstract
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.
We use the 100-member Grand Ensemble with the climate model MPI-ESM to evaluate the controllability of mean and extreme European summer temperatures with the global mean temperature targets in the ...Paris Agreement. We find that European summer temperatures at 2 °C of global warming are on average 1 °C higher than at 1.5 °C of global warming with respect to pre-industrial levels. In a 2 °C warmer world, one out of every two European summer months would be warmer than ever observed in our current climate. Daily maximum temperature anomalies for extreme events with return periods of up to 500 years reach return levels of 7 °C at 2 °C of global warming and 5.5 °C at 1.5 °C of global warming. The largest differences in return levels for shorter return periods of 20 years are over southern Europe, where we find the highest mean temperature increase. In contrast, for events with return periods of over 100 years these differences are largest over central Europe, where we find the largest changes in temperature variability. However, due to the large effect of internal variability, only four out of every ten summer months in a 2 °C warmer world present mean temperatures that could be distinguishable from those in a 1.5 °C world. The distinguishability between the two climates is largest over southern Europe, while decreasing to around 10% distinguishable months over eastern Europe. Furthermore, we find that 10% of the most extreme and severe summer maximum temperatures in a 2 °C world could be avoided by limiting global warming to 1.5 °C.
Abstract
Northeast Asia experienced unprecedented abrupt warming in the 1990s since the last century. Based on a robust time series and rank frequency evaluation, the Max Planck Institute for ...Meteorology Grand Ensembles of CMIP5 (MPI-GE5), CMIP6 (MPI-GE6), EC-Earth3 and IPSL-CM6A-LR were identified as the models that best simulate the external forcing and internal variability in observations and represent observations most adequately. The negative-to-positive phase transition of the Atlantic multidecadal variability (AMV), combined with the external forcing, can explain 88% 60%−111% of the 1990s warming. With prescribed anthropogenic emissions in the near future, a phase shift in the AMV to +2 (-2) standard deviation will amplify (weaken) the warming over Northeast Asia by 37% 29%−49% (19% 15%−25%). This highlights the importance of natural climate variability in Northeast Asia’s government decision-making and risk management, and emphasizes that only climate models with an adequate representation of forced warming can quantify these contributions correctly.
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