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The Arctic is warming two to four times faster than the global average. Debate continues on the relative roles of local factors, such as sea ice reductions, versus remote factors in driving, or amplifying, Arctic warming. This study examines the vertical profile and seasonality of observed tropospheric warming, and addresses its causes using atmospheric general circulation model simulations. The simulations enable the isolation and quantification of the role of three controlling factors of Arctic warming: 1) observed Arctic sea ice concentration (SIC) and sea surface temperature (SST) changes; 2) observed remote SST changes; and 3) direct radiative forcing (DRF) due to observed changes in greenhouse gases, ozone, aerosols, and solar output. Local SIC and SST changes explain a large portion of the observed Arctic near‐surface warming, whereas remote SST changes explain the majority of observed warming aloft. DRF has primarily contributed to Arctic tropospheric warming in summer. Key Points Arctic troposphere has warmed at all heights, but most strongly near the surface Sea ice loss and local SST changes are central to near‐surface Arctic warming Remote SST changes are the main driver of Arctic warming aloft (above 700 hPa)

The possibility that Arctic sea ice loss weakens mid-latitude westerlies, promoting more severe cold winters, has sparked more than a decade of scientific debate, with apparent support from observations but inconclusive modelling evidence. Here we show that sixteen models contributing to the Polar Amplification Model Intercomparison Project simulate a weakening of mid-latitude westerlies in response to projected Arctic sea ice loss. We develop an emergent constraint based on eddy feedback, which is 1.2 to 3 times too weak in the models, suggesting that the real-world weakening lies towards the higher end of the model simulations. Still, the modelled response to Arctic sea ice loss is weak: the North Atlantic Oscillation response is similar in magnitude and offsets the projected response to increased greenhouse gases, but would only account for around 10% of variations in individual years. We further find that relationships between Arctic sea ice and atmospheric circulation have weakened recently in observations and are no longer inconsistent with those in models. The degree to which Arctic sea ice decline influences the mid-latitude atmospheric circulation is widely debated. Here, the authors use a coordinated multi-model experiment to show that Arctic sea ice loss causes a weakening of the mid-latitude westerly winds, but the effect is overall small.

The El Niño‐Southern Oscillation (ENSO) influences climate variability across the globe. ENSO is highly predictable on seasonal timescales and therefore its teleconnections are a source of extratropical forecast skill. To fully harness this predictability, teleconnections must be represented accurately in seasonal forecasts. We find that a multimodel ensemble from five seasonal forecast systems can successfully capture the spatial structure of the late winter (JFM) El Niño teleconnection to the North Atlantic via North America, but the simulated amplitude is half of that observed. We find that weak amplitude teleconnections exist in all five models throughout the troposphere, and that the La Niña teleconnection is also weak. We find evidence that tropical forcing of the El Niño teleconnection is not underestimated and instead, deficiencies are likely to emerge in the extratropics. We investigate the impact of underestimated teleconnection strength on North Atlantic winter predictability, including its relevance to the signal‐to‐noise paradox. Plain Language Summary The El Niño‐Southern Oscillation (ENSO) describes the cycle of warmer and cooler sea surface temperatures in the tropical Pacific Ocean, which influences climate around the globe. The high heat capacity of the ocean means that ENSO changes relatively slowly and so the ENSO phase—known as El Niño or La Niña—can be predicted with high accuracy several months ahead. Far‐flung influences—known as teleconnections—of ENSO can provide predictability away from the tropics in seasonal forecasts if they are accurately modeled. In this work, the late winter (January–March) ENSO teleconnection to the North Atlantic, traveling via the North Pacific and North America, is investigated in five forecast models. We find that in each model, the pattern of the teleconnection is accurately captured, but the strength of the modeled teleconnection is half of that in the real world. We find that the strength of processes in the tropics which cause the El Niño teleconnection—including changes in sea surface temperatures and rainfall—are not underestimated by models, meaning that the problem arises further along the pathway to the extratropics. This error likely contributes to the currently unresolved “signal to noise paradox” in climate forecasts. Key Points Seasonal forecasts severely underestimate the response to El Niño‐Southern Oscillation (ENSO) in the extratropical North Pacific The underestimated model response exists throughout the troposphere and is present for both El Niño and La Niña Tropical processes which generate the El Niño teleconnection are well predicted and so the error does not appear to originate in the tropics

Purpose of Review Dynamic manifestations of climate change, i.e. those related to circulation, are less well understood than are thermodynamic, or temperature-related aspects. However, this knowledge gap is narrowing. We review recent progress in understanding the causes of observed changes in polar tropospheric and stratospheric circulation, and in interpreting climate model projections of their future changes. Recent Findings Trends in the annular modes reflect the influences of multiple drivers. In the Northern Hemisphere, there appears to be a “tug-of-war” between the opposing effects of Arctic near-surface warming and tropical upper tropospheric warming, two predominant features of the atmospheric response to increasing greenhouse gases. Future trends in the Southern Hemisphere largely depend on the competing effects of stratospheric ozone recovery and increasing greenhouse gases. Summary Human influence on the Antarctic circulation is detectable in the strengthening of the stratospheric polar vortex and the poleward shift of the tropospheric westerly winds. Observed Arctic circulation changes cannot be confidently separated from internal atmospheric variability.

The six summers from 2007 to 2012 were all wetter than average over northern Europe. Although none of these individual events are unprecedented in historical records, the sequence of six consecutive wet summers is extraordinary. Composite analysis reveals that observed wet summer months in northern Europe tend to occur when the jet stream is displaced to the south of its climatological position, whereas dry summer months tend to occur when the jet stream is located further north. Highly similar mechanisms are shown to drive simulated precipitation anomalies in an atmospheric model. The model is used to explore the influence of Arctic sea ice on European summer climate, by prescribing different sea ice conditions, but holding other forcings constant. In the simulations, Arctic sea ice loss induces a southward shift of the summer jet stream over Europe and increased northern European precipitation. The simulated precipitation response is relatively small compared to year-to-year variability, but is statistically significant and closely resembles the spatial pattern of precipitation anomalies in recent summers. The results suggest a causal link between observed sea ice anomalies, large-scale atmospheric circulation and increased summer rainfall over northern Europe. Thus, diminished Arctic sea ice may have been a contributing driver of recent wet summers.

Summertime Greenland blocking (GB) can drive melting of the Greenland ice sheet, which has global implications. A strongly increasing trend in GB in the early twenty‐first century was observed but is missing in climate model simulations. Here, we analyze the temporal evolution of GB in nearly 500 members from the CMIP6 archive. The recent period of increased GB is not present in the members considered. The maximum 10‐year trend in GB in the reanalysis, associated with the recent increase, lies almost outside the distributions of trends for any 10‐year period in the climate models. GB is shown to be partly driven by the sea surface temperatures and/or sea ice concentrations, as well as by anthropogenic aerosols. Further work is required to understand why climate models cannot represent a period of increased GB, and appear to underestimate its decadal variability, and what implications this may have. Plain Language Summary An increasing trend in summertime atmospheric blocking over Greenland was observed during the early twenty‐first century. However, this trend is not reproduced in climate models. This may have important implication for climate change projections, as summertime Greenland blocking drives the melting of its ice sheet which is a major contributing factor to global sea level rise. Here, recent trends in Greenland blocking are assessed in nearly 500 ensemble members from a large archive of state‐of‐the‐art climate models. We find that a recent increasing trend like that observed is absent in all of the ensemble members, and a trend of such magnitude is very unlikely to be simulated in them, which suggests a deficiency in the climate model simulation of Greenland blocking. The model simulations do however suggest that Greenland blocking is partly forced by sea surface temperatures/sea ice concentrations and/or anthropogenic aerosols, but the response of the models to these forcings may be too weak. These results provide new understanding on drivers of Greenland blocking in climate models and offer avenues for model development designed to improve simulations of Greenland climate. Key Points The observed rapid increase in summertime Greenland blocking during the first decade of the twenty‐first century has not continued A period of increased summer Greenland blocking of similar magnitude to observed is rarely reproduced in a large ensemble of climate models Decadal variability in Greenland blocking in climate models is partly driven by SST/sea ice and/or anthropogenic aerosols

Three sea ice data sets commonly used for climate research display a large and abrupt increase in Antarctic sea ice area (SIA) in recent years. This unprecedented change of SIA is diagnosed to be primarily caused by an apparent sudden increase in sea ice concentrations within the ice pack, especially in the area of the most‐concentrated ice (greater than 95% concentration). A series of alternative satellite‐derived records do not display any abnormal sudden SIA changes, but do reveal substantial discrepancies between different satellite sensors and sea ice algorithms. Sea ice concentrations in the central ice pack and SIA values derived from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSRE) are consistently greater than those derived from the Special Sensor Microwave Imager (SSMI). A switch in source data from the SSMI to AMSRE in mid‐2009 explains most of the SIA increase in all three affected data sets. If uncorrected for, the discontinuity artificially exaggerates the winter Antarctic SIA increase (1979–2010) by more than a factor of 2 and the spring trend by almost a factor of 4. The discontinuity has a weaker influence on the summer and autumn SIA trends, on calculations of Antarctic sea ice extent, and in the Arctic. Key Points Three highly‐cited data sets depict a sudden large increase in Antarctic sea ice This step‐change is fake and is related to a switch in source data Recent sea ice trends are significantly exagerated becuase of this data problem

Internal variability in the climate system confounds assessment of human-induced climate change and imposes irreducible limits on the accuracy of climate change projections, especially at regional and decadal scales. A new collection of initial-condition large ensembles (LEs) generated with seven Earth system models under historical and future radiative forcing scenarios provides new insights into uncertainties due to internal variability versus model differences. These data enhance the assessment of climate change risks, including extreme events, and offer a powerful testbed for new methodologies aimed at separating forced signals from internal variability in the observational record. Opportunities and challenges confronting the design and dissemination of future LEs, including increased spatial resolution and model complexity alongside emerging Earth system applications, are discussed.Climate change detection is confounded by internal variability, but recent initial-condition large ensembles (LEs) have begun addressing this issue. This Perspective discusses the value of multi-model LEs, the challenges of providing them and their role in future climate change research.

Antarctic sea ice area exhibited an abrupt decline in 2015–2016, transitioning from a near record maximum state to a then‐record minimum state. The underlying drivers are still being studied, raising questions whether this marks the onset of a long‐term decline, or an isolated internal climate variability event. We identify extreme events in CMIP6 pre‐industrial control simulations that are comparable to the observed extreme event in 2015–2016 and explore their atmospheric and oceanic drivers. Results show these events are rare but possible. The most robust association we find is between a negative Southern Annular Mode transition and extreme Antarctic sea ice loss. Most models show sea ice recovery after extreme loss, differing from the persistent decline observed in recent years. This contrast suggests anthropogenic forcing may now be playing a role. Our results underscore the role of internal variability while improving understanding of extreme events and their relevance for future sea ice predictability.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.