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Abstract. Climate simulation uncertainties arise from internal variability, model structure, and external forcings. Model intercomparisons (such as the Coupled Model Intercomparison Project; CMIP) and single-model large ensembles have provided insight into uncertainty sources. Under the Community Earth System Model (CESM) project, large ensembles have been performed for CESM2 (a CMIP6-era model) and CESM1 (a CMIP5-era model). We refer to these as CESM2-LE and CESM1-LE. The external forcing used in these simulations has changed to be consistent with their CMIP generation. As a result, differences between CESM2-LE and CESM1-LE ensemble means arise from changes in both model structure and forcing. Here we present new ensemble simulations which allow us to separate the influences of these model structural and forcing differences. Our new CESM2 simulations are run with CMIP5 forcings equivalent to those used in the CESM1-LE. We find a strong influence of historical forcing uncertainty due to aerosol effects on simulated climate. For the historical period, forcing drives reduced global warming and ocean heat uptake in CESM2-LE relative to CESM1-LE that is counteracted by the influence of model structure. The influence of the model structure and forcing vary across the globe, and the Arctic exhibits a distinct signal that contrasts with the global mean. For the 21st century, the importance of scenario forcing differences (SSP3–7.0 for CESM2-LE and RCP8.5 for CESM1-LE) is evident. The new simulations presented here allow us to diagnose the influence of model structure on 21st century change, despite large scenario forcing differences, revealing that differences in the meridional distribution of warming are caused by model structure. Feedback analysis reveals that clouds and their impact on shortwave radiation explain many of these structural differences between CESM2 and CESM1. In the Arctic, albedo changes control transient climate evolution differences due to structural differences between CESM2 and CESM1. 

Excessive precipitation over the southeastern tropical Pacific is a major common bias that persists through generations of global climate models. While recent studies suggest an overly warm Southern Ocean as the cause, models disagree on the quantitative importance of this remote mechanism in light of ocean circulation feedback. Here, using a multimodel experiment in which the Southern Ocean is radiatively cooled, we show a teleconnection from the Southern Ocean to the tropical Pacific that is mediated by a shortwave subtropical cloud feedback. Cooling the Southern Ocean preferentially cools the southeastern tropical Pacific, thereby shifting the eastern tropical Pacific rainbelt northward with the reduced precipitation bias. Regional cloud locking experiments confirm that the teleconnection efficiency depends on subtropical stratocumulus cloud feedback. This subtropical cloud feedback is too weak in most climate models, suggesting that teleconnections from the Southern Ocean to the tropical Pacific are stronger than widely thought. 

Abstract Do changes in ocean heat transport (OHT) that occur with CO 2 forcing, impact climate sensitivity in Earth system models? Changes in OHT with warming are ubiquitous in model experiments: when forced with CO 2 , such models exhibit declining poleward OHT in both hemispheres at most latitudes, which can persist over multicentennial time scales. To understand how changes in OHT may impact how the climate system responds to CO 2 forcing, particularly climate sensitivity, we perform a series of Earth system model experiments in which we systematically perturb OHT (in a slab ocean, relative to its preindustrial control climatology) while simultaneously doubling atmospheric CO 2 . We find that equilibrium climate sensitivity varies substantially with OHT. Specifically, there is a 0.6 K decrease in global mean surface warming for every 10% decline in poleward OHT. Radiative feedbacks from CO 2 doubling, and the warming attributable to each of them, generally become more positive (or less negative) when poleward OHT increases. Water vapor feedback differences account for approximately half the spread in climate sensitivity between experiments, while differences in the lapse rate and surface albedo feedbacks account for the rest. Prescribed changes in OHT instigate opposing changes in atmospheric energy transport and the general circulation, which explain differences in atmospheric water vapor and lapse rate between experiments. Our results show that changes in OHT modify atmospheric radiative feedbacks at all latitudes, thereby driving changes in equilibrium climate sensitivity. More broadly, they demonstrate that radiative feedbacks are not independent of the coupled (atmosphere and ocean) dynamic responses that accompany greenhouse gas forcing. 

Abstract This study quantifies the state of the art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multimodel dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–20 for predictions of pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on 1 June, 1 July, 1 August, and 1 September. This diverse set of statistical and dynamical models can individually predict linearly detrended pan-Arctic SIE anomalies with skill, and a multimodel median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and central Arctic sectors. The skill of dynamical and statistical models is generally comparable for pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least 3 months in advance. 

The sensitivity of sea ice to fire emissions highlights climate model uncertainty related to the accuracy of prescribed forcings. 

Abstract The relative importance of radiative feedbacks and emissions scenarios in controlling surface warming patterns is challenging to quantify across model generations. We analyze three variants of the Community Earth System Model (CESM) with differing equilibrium climate sensitivities under identical CMIP5 historical and high‐emissions scenarios. CESM1, our base model, exhibits Arctic‐amplified warming with the least warming in the Southern Hemisphere middle latitudes. A variant of CESM1 with enhanced extratropical shortwave cloud feedbacks shows slightly increased late‐21st century warming at all latitudes. In the next‐generation model, CESM2, global‐mean warming is also slightly greater, but the warming is zonally redistributed in a pattern mirroring cloud and surface albedo feedbacks. However, if the nominally equivalent CMIP6 scenario is applied to CESM2, the redistributed warming pattern is preserved, but global‐mean warming is significantly greater. These results demonstrate how model structural differences and scenario differences combine to produce differences in climate projections across model generations. 

Abstract. In recent decades, Arctic sea ice has shifted toward ayounger, thinner, seasonal ice regime. Studying and understanding this“new” Arctic will be the focus of a year-long ship campaign beginning inautumn 2019. Lagrangian tracking of sea ice floes in the Community EarthSystem Model Large Ensemble (CESM-LE) during representative “perennial”and “seasonal” time periods allows for understanding of the conditionsthat a floe could experience throughout the calendar year. These modeltracks, put into context a single year of observations, provide guidance onhow observations can optimally shape model development, and how climatemodels could be used in future campaign planning. The modeled floe tracksshow a range of possible trajectories, though a Transpolar Drift trajectoryis most likely. There is also a small but emerging possibility of high-risktracks, including possible melt of the floe before the end of a calendaryear. We find that a Lagrangian approach is essential in order to correctlycompare the seasonal cycle of sea ice conditions between point-basedobservations and a model. Because of high variability in the melt season seaice conditions, we recommend in situ sampling over a large range of ice conditionsfor a more complete understanding of how ice type and surface conditionsaffect the observed processes. We find that sea ice predictability emergesrapidly during the autumn freeze-up and anticipate that process-basedobservations during this period may help elucidate the processes leading tothis change in predictability.