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This paper presents a novel data set of regional climate model simulations over Europe that significantly improves our ability to detect changes in weather extremes under low and moderate levels of global warming. The data set provides a unique and physically consistent data set, as it is derived from a large ensemble of regional climate model simulations. These simulations were driven by two global climate models from the international HAPPI consortium. The set consists of 100 × 10-year simulations and 25 × 10-year simulations, respectively. These large ensembles allow for regional climate change and weather extremes to be investigated with an improved signal-to-noise ratio compared to previous climate simulations. The changes in four climate indices for temperature targets of 1.5 °C and 2.0 °C global warming are quantified: number of days per year with daily mean near-surface apparent temperature of > 28 °C (ATG28); the yearly maximum 5-day sum of precipitation (RX5day); the daily precipitation intensity of the 50-yr return period (RI50yr); and the annual Consecutive Dry Days (CDD). This work shows that even for a small signal in projected global mean temperature, changes of extreme temperature and precipitation indices can be robustly estimated. For temperature related indices changes in percentiles can also be estimated with high confidence. Such data can form the basis for tailor-made climate information that can aid adaptive measures at a policy-relevant scales, indicating potential impacts at low levels of global warming at steps of 0.5 °C. [ABSTRACT FROM AUTHOR]

Climate classifications based on temperature and precipitation measurements are increasingly being used for environmental and climate change studies. Using three classification methods (Köppen, Extended Köppen, and Holdridge) and one observational dataset for present climate (CRU, Climate Research Unit), we show that GCMs have bridged the gap that led to the emergence of RCMs thirty years ago, as GCMs can now provide global climate classifications whose accuracy and precision are comparable to those of regional outputs of the RCMs. Projections of high-resolution GCMs for future climates under the assumptions of three Representative Concentration Pathways (RCP26, RCP45 and RCP85) can therefore be used as a primary source for climate change and global warming studies at high resolution. This paper provides comprehensive, model-derived climate classifications for the entire planet, using RCMs and two GCMs for present and future climate-change scenarios, and discusses how well the models actually represent the climates of the world when compared with reference, ground validation data. It turns out that both GCMs and RCMs appear still limited to provide practical estimates of the world climates even for present climate conditions. The modeling of precipitation remains the Achilles' heel of models and thus of multidimensional indices, which are very sensitive to this variable. The conclusion is that model outputs at regional scale need to be taken with extreme caution without venturing into informing policies presenting potentially large societal impacts. Nonetheless, the role of models as privileged tools to advance our scientific knowledge of the Earth's system remains undisputed. • A systematic analysis of climate classifications with RCMs and two high resolution GCMs is presented. • Three classification methods are used to illustrate the differences between models and observations. • Current high-resolution GCMs can provide climate classifications with the same accuracy as those from RCMs. • Uncertainties in modeling precipitation persist for large parts of the world. [ABSTRACT FROM AUTHOR]

This study investigates present and future European heat wave magnitudes, represented by the Heat Wave Magnitude Index-daily (HWMId), for regional climate models (RCMs) and the driving global climate models (GCMs) over Europe. A subset of the large EURO-CORDEX ensemble is employed to study sources of uncertainties related to the choice of GCMs, RCMs, and their combinations. We initially compare the evaluation runs of the RCMs driven by ERA-interim reanalysis to E-OBS (observation-based estimates), finding that the RCMs can capture most of the observed spatial and temporal features of HWMId. With their higher resolution compared to GCMs, RCMs can reveal spatial features of HWMId associated with small-scale processes (e.g., orographic effects); moreover, RCMs represent large-scale features of HWMId satisfactorily (e.g., by reproducing the general pattern revealed by E-OBS with high values at western coastal regions and low values at the eastern part). Our results indicate a clear added value of the RCMs compared to the driving GCMs. Forced with the emission scenario RCP8.5, all the GCM and RCM simulations consistently project a rise in HWMId at an exponential rate. However, the climate change signals projected by the GCMs are generally attenuated when downscaled by the RCMs, with the spatial pattern also altered. The uncertainty in a simulated future change of heat wave magnitudes following global warming can be attributed almost equally to the difference in model physics (as represented by different RCMs) and to the driving data associated with different GCMs. Regarding the uncertainty associated with RCM choice, a major factor is the different representation of the orographic effects. No consistent spatial pattern in the ensemble spread associated with different GCMs is observed between the RCMs, suggesting GCM uncertainties are transformed by RCMs in a complex manner due to the nonlinear nature of model dynamics and physics. In summary, our results support the use of dynamical downscaling for deriving regional climate realization regarding heat wave magnitudes. [ABSTRACT FROM AUTHOR]

A comprehensive analysis of the representation of winter and summer Northern Hemisphere atmospheric blocking in global climate simulations in both present and future climate is presented. Three generations of climate models are considered: CMIP3 (2007), CMIP5 (2012), and CMIP6 (2019). All models show common and extended underestimation of blocking frequencies, but a reduction of the negative biases in successive model generations is observed. However, in some specific regions and seasons such as the winter European sector, even CMIP6 models are not yet able to achieve the observed blocking frequency. For future decades the vast majority of models simulate a decrease of blocking frequency in both winter and summer, with the exception of summer blocking over the Urals and winter blocking over western North America. Winter predicted decreases may be even larger than currently estimated considering that models with larger blocking frequencies, and hence generally smaller errors, show larger reduction. Nonetheless, trends computed over the historical period are weak and often contrast with observations: this is particularly worrisome for summer Greenland blocking where models and observations significantly disagree. Finally, the intensity of global warming is related to blocking changes: wintertime European and North Pacific blocking are expected to decrease following larger global mean temperatures, while Ural summer blocking is expected to increase. [ABSTRACT FROM AUTHOR]

The equilibrium climate sensitivity, that is, the global‐mean surface‐air temperature change in response to a doubling of the carbon dioxide concentration is a widely used metric in climate change studies. Its exact value is rarely known because its estimation requires a long integration time of several thousand years. We propose a method to estimate an accurate value of the equilibrium response from fully coupled climate models at a reasonable computational cost. Using this method, our state‐of‐the‐art climate model CNRM‐CM6‐1 reaches a stationary state after only few hundred of years of integration. This "Fast‐Forward" method consists of an optimal two‐step forcing pathway designed using the framework of a two‐layer energy balance model. It can be applied easily to any coupled climate model. Key Points: A simple method for estimating the equilibrium climate sensitivity is proposedThe method allows to simulate the stationary climate corresponding to any given radiative perturbation with a limited computational costThe method can be applied to any atmosphere‐ocean coupled climate model [ABSTRACT FROM AUTHOR]

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 modelMPI‐GE currently has the most forcing scenarios of all large ensemble projects: RCP2.6, RCP4.5, RCP8.5, and 1% CO2The power of MPI‐GE is to estimate the forced response and internal variability, including changing variability, to unprecedented precision [ABSTRACT FROM AUTHOR]

Recent studies, using Lagrangian single-column atmospheric models, have proposed that in warmer climates more low clouds would form as maritime air masses advect into Northern Hemisphere high-latitude continental interiors during winter (DJF). This increase in low cloud amount and optical thickness could reduce surface radiative cooling and suppress Arctic air formation events, partly explaining both the warm winter high-latitude continental interior climate and frost-intolerant species found there during the Eocene and the positive lapse-rate feedback in future Arctic climate change scenarios. Here the authors examine the robustness of this low-cloud mechanism in a three-dimensional atmospheric model that includes large-scale dynamics. Different warming scenarios are simulated under prescribed CO2 and sea surface temperature, and the sensitivity of winter temperatures and clouds over high-latitude continental interior to mid- and high-latitude sea surface temperatures is examined. Model results show that winter 2-m temperatures on extreme cold days increase about 50% faster than the winter mean temperatures and the prescribed SST. Low cloud fraction and surface longwave cloud radiative forcing also increase in both the winter mean state and on extreme cold days, consistent with previous Lagrangian air-mass studies, but with cloud fraction increasing for different reasons than proposed by previous work. At high latitudes, the cloud longwave warming effect dominates the shortwave cooling effect, and the net cloud radiative forcing at the surface tends to warm high-latitude land but cool midlatitude land. This could contribute to the reduced meridional temperature gradient in warmer climates and help explain the greater warming of winter cold extremes relative to winter mean temperatures. [ABSTRACT FROM AUTHOR]

Two ensembles of dynamically downscaled climate simulations for North America—the North American Regional Climate Change Assessment Program (NARCCAP) and the Coordinated Regional Climate Downscaling Experiment (CORDEX) featuring simulations for North America (NA-CORDEX)—are analyzed to assess the impact of using a small set of global general circulation models (GCMs) and regional climate models (RCMs) on representing uncertainty in regional projections. Selecting GCMs for downscaling based on their equilibrium climate sensitivities is a reasonable strategy, but there are regions where the uncertainty is not fully captured. For instance, the six NA-CORDEX GCMs fail to span the full ranges produced by models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) in summer temperature projections in the western and winter precipitation projections in the eastern United States. Similarly, the four NARCCAP GCMs are overall poor at spanning the full CMIP3 ranges in seasonal temperatures. For the Southeast, the NA-CORDEX GCMs capture the uncertainty in summer but not in winter projections, highlighting one consequence of downscaling a subset of GCMs. Ranges produced by the RCMs are often wider than their driving GCMs but are sensitive to the experimental design. For example, the downscaled projections of summer precipitation are of opposite polarity in two RCM ensembles in some regions. Additionally, the ability of the RCMs to simulate observed temperature trends is affected by the internal variability characteristics of both the RCMs and driving GCMs, and is not systematically related to their historical performance. This has implications for adequately sampling the impact of internal variability on regional trends and for using model performance to identify credible projections. These findings suggest that a multimodel perspective on uncertainties in regional projections is integral to the interpretation of RCM results. [ABSTRACT FROM AUTHOR]

Abstract: On interannual to decadal time scales, the climate mode with many of the strongest societal impacts is the El Niño–Southern Oscillation (ENSO). However, quantifying ENSO's changes in a warming climate remains a formidable challenge, due to both the noise arising from internal variability and the complexity of air‐sea feedbacks in the tropical Pacific Ocean. In this work, we use large (≥30‐member) ensembles of climate simulations to show that anthropogenic climate change can produce systematic increases in ENSO teleconnection strength over many land regions, driving increased interannual variability in regional temperature extremes and wildfire frequency. As the spatial character of this intensification exhibits strong land‐ocean contrasts, a causal role for land‐atmosphere feedbacks is suggested. The identified increase in variance occurs in multiple model ensembles, independent of changes in sea surface temperature variance. This suggests that in addition to changes in the overall likelihoods of heat and wildfire extremes, the variability in these events may also be a robust feature of future climate. [ABSTRACT FROM AUTHOR]

It has been challenging to project the tropical cyclone (TC) intensity, structure and destructive potential changes in a warming climate. Here, we compare the sensitivities of TC intensity, size and destructive potential to sea surface warming with and without a pre-storm atmospheric adjustment to an idealized state of Radiative-Convective Equilibrium (RCE). Without RCE, we find large responses of TC intensity, size and destructive potential to sea surface temperature (SST) changes, which is in line with some previous studies. However, in an environment under RCE, the TC size is almost insensitive to SST changes, and the sensitivity of intensity is also much reduced to 3% °C−1-4% °C−1. Without the pre-storm RCE adjustment, the mean destructive potential measured by the integrated power dissipation increases by about 25% °C−1 during the mature stage. However, in an environment under RCE, the sensitivity of destructive potential to sea surface warming does not change significantly. Further analyses show that the reduced response of TC intensity and size to sea surface warming under RCE can be explained by the reduced thermodynamic disequilibrium between the air boundary layer and the sea surface due to the RCE adjustment. When conducting regional-scale sea surface warming experiments for TC case studies, without any RCE adjustment the TC response is likely to be unrealistically exaggerated. The TC intensity-temperature sensitivity under RCE is very similar to those found in coupled climate model simulations. This suggests global mean intensity projections under climate change can be understood in terms of a thermodynamic response to temperature with only a minor contribution from any changes in large-scale dynamics. [ABSTRACT FROM AUTHOR]

Much of the existing theory for the ocean's overturning circulation considers steady-state equilibrium solutions. However, Earth's climate is not in a steady state, and a better understanding of the ocean's nonequilibrium response to changes in the surface climate is urgently needed. Here, the time-dependent response of the deep-ocean overturning circulation to atmospheric warming is examined using a hierarchy of idealized ocean models. The transient response to surface warming is characterized by a shoaling and weakening of the Atlantic meridional overturning circulation (AMOC)—consistent with results from coupled climate simulations. The initial shoaling and weakening of the AMOC occurs on decadal time scales and is attributed to a rapid warming of northern-sourced deep water. The equilibrium response to warming, in contrast, is associated with a deepening and strengthening of the AMOC. The eventual deepening of the AMOC is argued to be associated with abyssal density changes and driven by modified surface fluxes in the Southern Ocean, following a reduction of the Antarctic sea ice cover. Full equilibration of the AMOC requires a diffusive adjustment of the abyss and takes many millennia. The equilibration time scale is much longer than most coupled climate model simulations, highlighting the importance of considering integration time and initial conditions when interpreting the deep-ocean circulation in climate models. The results also show that past climates are unlikely to be an adequate analog for changes in the overturning circulation during the coming decades or centuries. [ABSTRACT FROM AUTHOR]

Here we use an Earth system model with interactive biogeochemistry to project future ocean biogeochemistry impacts from the large-scale deployment of three different radiation management (RM) climate engineering (also known as geoengineering) methods: stratospheric aerosol injection (SAI), marine sky brightening (MSB), and cirrus cloud thinning (CCT). We apply RM such that the change in radiative forcing in the RCP8.5 emission scenario is reduced to the change in radiative forcing in the RCP4.5 scenario. The resulting global mean sea surface temperatures in the RM experiments are comparable to those in RCP4.5, but there are regional differences. The forcing from MSB, for example, is applied over the oceans, so the cooling of the ocean is in some regions stronger for this method of RM than for the others. Changes in ocean net primary production (NPP) are much more variable, but SAI and MSB give a global decrease comparable to RCP4.5 (∼6% in 2100 relative to 1971-2000), while CCT gives a much smaller global decrease of ∼3 %. Depending on the RM methods, the spatially inhomogeneous changes in ocean NPP are related to the simulated spatial change in the NPP drivers (incoming radiation, temperature, availability of nutrients, and phytoplankton biomass) but mostly dominated by the circulation changes. In general, the SAI- and MSB-induced changes are largest in the low latitudes, while the CCT-induced changes tend to be the weakest of the three. The results of this work underscore the complexity of climate impacts on NPP and highlight the fact that changes are driven by an integrated effect of multiple environmental drivers, which all change in different ways. These results stress the uncertain changes to ocean productivity in the future and advocate caution at any deliberate attempt at large-scale perturbation of the Earth system. [ABSTRACT FROM AUTHOR]

Efforts to understand the influence of historical global warming on individual extreme climate events have increased over the past decade. However, despite substantial progress, events that are unprecedented in the local observational record remain a persistent challenge. Leveraging observations and a large climate model ensemble, we quantify uncertainty in the influence of global warming on the severity and probability of the historically hottest month, hottest day, driest year, and wettest 5-d period for different areas of the globe. We find that historical warming has increased the severity and probability of the hottest month and hottest day of the year at >80% of the available observational area. Our framework also suggests that the historical climate forcing has increased the probability of the driest year and wettest 5-d period at 57% and 41% of the observed area, respectively, although we note important caveats. For the most protracted hot and dry events, the strongest and most widespread contributions of anthropogenic climate forcing occur in the tropics, including increases in probability of at least a factor of 4 for the hottest month and at least a factor of 2 for the driest year. We also demonstrate the ability of our framework to systematically evaluate the role of dynamic and thermodynamic factors such as atmospheric circulation patterns and atmospheric water vapor, and find extremely high statistical confidence that anthropogenic forcing increased the probability of record-low Arctic sea ice extent. [ABSTRACT FROM AUTHOR]