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A novel sub‐sampling method has been used to isolate the dynamic effects of the response of the North Atlantic Oscillation (NAO) and the Siberian High (SH) from the total response to projected Arctic sea‐ice loss under 2°C global warming above preindustrial levels in very large initial‐condition ensemble climate simulations. Thermodynamic effects of Arctic warming are more prominent in Europe while dynamic effects are more prominent in Asia/East Asia. This explains less‐severe cold extremes in Europe but more‐severe cold extremes in Asia/East Asia. For Northern Eurasia, dynamic effects overwhelm the effect of increased moisture from a warming Arctic, leading to an overall decrease in precipitation. We show that the response scales linearly with the dynamic response. However, caution is needed when interpreting inter‐model differences in the response because of internal variability, which can largely explain the inter‐model spread in the NAO and SH response in the Polar Amplification Model Intercomparison Project. Plain Language Summary The projected loss of Arctic sea‐ice under 2°C global warming will cause large warming in the Arctic region and climate and weather anomalies outside the Arctic. The warming in the Arctic will mean warmer airmasses coming from the Arctic and also more moisture from the open Arctic Ocean. Furthermore, it will also change atmospheric circulation. These effects together will determine the impacts of Arctic warming. In this study, we introduce a novel sub‐sampling method to isolate atmospheric circulation change in response to the Arctic warming. The method involves selecting members of simulations from the experiment with future Arctic sea‐ice conditions, the average of which is equal to the average of the members of simulations in the experiment with present‐day Arctic sea‐ice conditions. We found that atmospheric circulation change in European regions is relatively weak so that warming effects will dominate the climate and weather response there. On the other hand, atmospheric circulation change will dominate the climate and weather response in East Eurasia. We also found that stronger atmospheric circulation changes will generally increase the response to the Arctic warming. We suggest caution when assessing whether different responses in different models can be interpreted as true differences in model physics. Key Points A novel sub‐sampling method is introduced to isolate the role of dynamics in the response to projected Arctic sea‐ice loss A dynamical Siberian High response dominates the temperature response over East Eurasia while that of the North Atlantic Oscillation is weak Inter‐model differences in Polar Amplification Model Intercomparison Project likely contain a large fraction of internal variability due to the unconstrained dynamic effects

Observations have shown subseasonal reversal of temperature anomalies between early and late winter over Eurasia, which is distinct from the seasonal mean condition. Based on the reanalysis data, the 1800-year control simulation and the 40-member ensemble simulations in 1920–2100 from the Community Earth System Model (CESM) Large Ensemble (CESM-LE), this study reveals that the reversal of surface air temperature (SAT) anomalies between early and late winter is one of the dominant and intrinsic features of the Arctic-Eurasian winter climate. Such a reversal is characterized by “colder Arctic, warmer Eurasia” in December (January–February) and “warmer Arctic, colder Eurasia” in January–February (December). Robust climate dynamic processes associated with the reversal of SAT anomalies, including subseasonal reversals of anomalies in the Ural blocking, midlatitude westerlies, and stratospheric polar vortex, are found in both reanalysis data and CESM simulations, indicating the important role of internal atmospheric variability. Further analysis reveals that the reversal of Ural blocking anomalies in late December can be a potential precursor for the reversal of SAT anomalies in late winter. The reversal of midlatitude westerly wind anomalies associated with the Ural blocking can affect upward propagation of planetary-scale waves especially with wavenumber 1, subsequently promoting the contribution of stratospheric polar vortex to the reversal of SAT anomalies in late winter over the Arctic-Eurasian regions. Such a troposphere-stratosphere pathway triggered by the perturbation of tropospheric circulations is confirmed by the CESM-LE simulations, and it may be useful for the prediction of subseasonal reversal of SAT anomalies.

Climate change threatens the resource adequacy of future power systems. Existing research and practice lack frameworks for identifying decarbonization pathways that are robust to climate‐related uncertainty. We create such an analytical framework, then use it to assess the robustness of alternative pathways to achieving 60% emissions reductions from 2022 levels by 2040 for the Western U.S. power system. Our framework integrates power system planning and resource adequacy models with 100 climate realizations from a large climate ensemble. Climate realizations drive electricity demand; thermal plant availability; and wind, solar, and hydropower generation. Among five initial decarbonization pathways, all exhibit modest to significant resource adequacy failures under climate realizations in 2040, but certain pathways experience significantly less resource adequacy failures at little additional cost relative to other pathways. By identifying and planning for an extreme climate realization that drives the largest resource adequacy failures across our pathways, we produce a new decarbonization pathway that has no resource adequacy failures under any climate realizations. This new pathway is roughly 5% more expensive than other pathways due to greater capacity investment, and shifts investment from wind to solar and natural gas generators. Our analysis suggests modest increases in investment costs can add significant robustness against climate change in decarbonizing power systems. Our framework can help power system planners adapt to climate change by stress testing future plans to potential climate realizations, and offers a unique bridge between energy system and climate modeling. Plain Language Summary Over the past few years, large power outage events in California and Texas have underscored the vulnerability of our power systems to extreme weather. By increasing the intensity and frequency of extreme weather, climate change could lead to more power outages. In response, power system planners are grappling with how to plan for extreme weather and climate change when making investment decisions, such as in wind and solar power. In our research, we build and apply a new analytical framework for making power system investment decisions under climate change. Our framework draws on a hundred realizations of future climate, and integrates weather in those realizations with power system models that make investment decisions and explore the risk of power outages. We find five alternative investment pathways all could suffer from moderate to significant power outages under possible climate realizations by 2040. But by identifying what realizations drive outage risk in these pathways, we construct a new pathway that does not exhibit outage risks to our future climate realizations. Overall, these insights demonstrate the value of our new analytical framework for making better investment decisions under uncertainty posed by climate change. Key Points We identify a decarbonization pathway for the power system that is robust to future climate realizations Our framework is extensible to long‐term planning by utilities, regions, and regulators Large climate ensembles expose significant resource adequacy vulnerabilities in alternative decarbonization pathways

Variability in hydroclimate impacts natural and human systems worldwide. In particular, both decadal variability and extreme precipitation events have substantial effects and are anticipated to be strongly influenced by climate change. From a practical perspective, these impacts will be felt relative to the continuously evolving background climate. Removing the underlying forced trend is therefore necessary to assess the relative impacts, but to date, the small size of most climate model ensembles has made it difficult to do this. Here we use an archive of large ensembles run under a high-emissions scenario to determine how decadal “megadrought” and “megapluvial” events—and shorter-term precipitation extremes—will vary relative to that changing baseline. When the trend is retained, mean state changes dominate: In fact, soil moisture changes are so large in some regions that conditions that would be considered a megadrought or pluvial event today are projected to become average. Time-of-emergence calculations suggest that in some regions including Europe and western North America, this shift may have already taken place and could be imminent elsewhere: Emergence of drought/pluvial conditions occurs over 61% of the global land surface (excluding Antarctica) by 2080. Relative to the changing baseline, megadrought/megapluvial risk either will not change or is slightly reduced. However, the increased frequency and intensity of both extreme wet and dry precipitation events will likely present adaptation challenges beyond anything currently experienced. In many regions, resilience against future hazards will require adapting to an ever-changing “normal,” characterized by unprecedented aridification/wetting punctuated by more severe extremes.

We use a methodological framework exploiting the power of large ensembles to evaluate how well ten coupled climate models represent the internal variability and response to external forcings in observed historical surface temperatures. This evaluation framework allows us to directly attribute discrepancies between models and observations to biases in the simulated internal variability or forced response, without relying on assumptions to separate these signals in observations. The largest discrepancies result from the overestimated forced warming in some models during recent decades. In contrast, models do not systematically over- or underestimate internal variability in global mean temperature. On regional scales, all models misrepresent surface temperature variability over the Southern Ocean, while overestimating variability over land-surface areas, such as the Amazon and South Asia, and high-latitude oceans. Our evaluation shows that MPI-GE, followed by GFDL-ESM2M and CESM-LE offer the best global and regional representation of both the internal variability and forced response in observed historical temperatures.

Anthropogenic changes in the variability of precipitation stand to impact both natural and human systems in profound ways. Precipitation variability encompasses not only extremes like droughts and floods, but also the spectrum of precipitation which populates the times between these extremes. Understanding the changes in precipitation variability alongside changes in mean and extreme precipitation is essential in unraveling the hydrological cycle’s response to warming. We use a suite of state-of-the-art climate models, with each model consisting of a single-model initial-condition large ensemble (SMILE), yielding at least 15 individual realizations of equally likely evolutions of future climate state for each climate model. The SMILE framework allows for increased precision in estimating the evolving distribution of precipitation, allowing for forced changes in precipitation variability to be compared across climate models. We show that the scaling rates of precipitation variability, the relation between the rise in global temperature and changes in precipitation variability, are markedly robust across timescales from interannual to decadal. Over mid- and high latitudes, it is very likely that precipitation is increasing across the entire spectrum from means to extremes, as is precipitation variability across all timescales, and seasonally these changes can be amplified. Model or structural uncertainty is a prevailing uncertainty especially over the Tropics and Subtropics. We uncover that model-based estimates of historical interannual precipitation variability are sensitive to the number of ensemble members used, with ‘small’ initial-condition ensembles (of less than 30 members) systematically underestimating precipitation variability, highlighting the utility of the SMILE framework for the representation of the full precipitation distribution.

Owing to the limited length of observed tropical cyclone data and the effects of multidecadal internal variability, it has been a challenge to detect trends in tropical cyclone activity on a global scale. However, there is a distinct spatial pattern of the trends in tropical cyclone frequency of occurrence on a global scale since 1980, with substantial decreases in the southern Indian Ocean and western North Pacific and increases in the North Atlantic and central Pacific. Here, using a suite of high-resolution dynamical model experiments, we show that the observed spatial pattern of trends is very unlikely to be explained entirely by underlying multidecadal internal variability; rather, external forcing such as greenhouse gases, aerosols, and volcanic eruptions likely played an important role. This study demonstrates that a climatic change in terms of the global spatial distribution of tropical cyclones has already emerged in observations and may in part be attributable to the increase in greenhouse gas emissions.

The dependence structure of temperature-precipitation compound events is analyzed across Canada using three datasets derived from Canadian Regional Climate Model Large Ensemble simulations, including raw model outputs (CanRCM4-LE) and two sets of multivariate bias-corrected model outputs (Canadian Large Ensembles Adjusted Datasets, CanLEAD-EWEMBI/S14FD). The performance of the ensembles to represent tail dependencies corresponding to warm-wet and warm-dry events is evaluated against NRCANmet observations for 1951–2000 using the copula goodness of fit test. The parameters of the copula model are estimated using a Bayesian framework to characterize the corresponding uncertainties. The non-stationarity of compound extreme climate events is analyzed for 1951–2100 using an ensemble pooling approach and the results are compared with the ones based on the independence assumption. Results show that multivariate bias-corrected climate simulations (i.e. CanLEAD) can better represent the correlated temperature-precipitation extremes compared to raw CanRCM4-LE outputs. The estimated joint return periods reduce significantly when the dependence structure is considered, compared to the independence assumption, for most regions especially in winter and summer. Therefore, analysis of extreme temperature and precipitation in isolation can result in dramatic underestimations of compound warm-wet and warm-dry events. Further, there is strong non-stationarity in the dependence structure of temperature and precipitation under climate change that can play a significant role in future compound extremes.

Understanding future changes in global terrestrial near-surface wind speed (NSWS) in specific global warming level (GWL) is crucial for climate change adaption. Previous studies have projected the NSWS changes; however, the changes of NSWS with different GWLs have yet to be studied. In this paper, we employ the Max Planck Institute Earth System Model large ensembles to evaluate the contributions of different GWLs to the NSWS changes. The results show that the NSWS decreases over the Northern Hemisphere (NH) mid-to-high latitudes and increases over the Southern Hemisphere (SH) as the GWL increases by 1.5 °C–4.0 °C relative to the preindustrial period, and that these characteristics are more significant with the stronger GWL. The probability density of the NSWS shifts toward weak winds over NH and strong winds over SH between the current climate and the 4.0 °C GWL. Compared to 1.5 °C GWL, the NSWS decreases −0.066 m s −1 over NH and increases +0.065 m s −1 over SH with 4.0 °C GWL, especially for East Asia and South America, the decrease and increase are most significant, which reach −0.21 and +0.093 m s −1 , respectively. Changes in the temperature gradient induced by global warming could be the primary factor causing the interhemispheric asymmetry of future NSWS changes. Intensified global warming induces the reduction in Hadley, Ferrell, and Polar cells over NH and the strengthening of the Hadley cell over SH could be another determinant of asymmetry changes in NSWS between two hemispheres.

Climate projection uncertainty can be partitioned into model uncertainty, scenario uncertainty and internal variability. Here, we investigate the different sources of uncertainty in the projected frequencies of daily maximum temperature and precipitation extremes, which are defined as events that exceed the 99.97th percentile. This is done globally using large initial-condition ensembles. For maximum temperature extremes, internal variability that generates deviations about the ensemble average, dominates in the next 2 decades. Around the middle of the twenty-first century model and scenario uncertainty become the dominant contribution in the tropics but internal variability remains dominant in the extra-tropics. Towards the end of the century, model and scenario uncertainty increase to near equal contributions of ∼ 40% each globally with large regional fluctuations. For precipitation extremes, internal variability dominates throughout the twenty-first century, except for some tropical regions, for example, West Africa. In regions where internal variability constitutes the major source of uncertainty, the potential impact of reducing model uncertainty on the signal-to-noise ratio of the climate projection is estimated to be small. We discuss the caveats of the methodology used and impact of our findings for the design of future climate models. The importance of internal variability found here emphasizes that large ensembles are a vital tool for understanding climate projections.