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Diagnosing the role of internal variability over recent decades is critically important for both model validation and projections of future warming. Recent research suggests that for 1980–2022 internal variability manifested as Global Cooling and Arctic Warming (i‐GCAW), leading to enhanced Arctic Amplification (AA), and suppressed global warming over this period. Here we show that such an i‐GCAW is rare in CMIP6 large ensembles, but simulations that do produce similar i‐GCAW exhibit a unique and robust internally driven global surface air temperature (SAT) trend pattern. This unique SAT trend pattern features enhanced warming in the Barents and Kara Sea and cooling in the Tropical Eastern Pacific and Southern Ocean. Given that these features are imprinted in the observed record over recent decades, this work suggests that internal variability makes a crucial contribution to the discrepancy between observations and model‐simulated forced SAT trend patterns. Plain Language Summary: When comparing model simulations of Earth's recent warming to real‐world observations, differences may arise from several factors. Two important factors are the model errors in the simulated response to increased greenhouse gases, and natural fluctuations within the climate system that produced discrepancies between observations and models. Thus, quantifying the role of these natural fluctuations is important for the assessment of model‐observation differences. Previous studies have shown that natural climate variability has depressed global warming and enhanced Arctic warming. By compositing the multi‐decadal trend patterns from CMIP6 simulations in which natural variability warms the Arctic but has a global cooling effect, we find that the majority of these model simulations also produce enhanced warming in the Barents and Kara Seas and cooling in the Tropical Eastern Pacific and Southern Ocean due to natural variability. Since these are the exact features imprinted on observed surface temperature changes over 1980–2022, our work suggests that natural variability is an important component of several noteworthy differences between models and observations. Key Points: Internal variability has enhanced Arctic warming but suppressed global warming over 1980–2022This manifestation of internal variability is rare in model simulations but has a robust global surface air temperature (SAT) trend patternThis internal SAT pattern features warming in the Barents and Kara Sea and cooling of the Tropical Eastern Pacific and Southern Ocean [ABSTRACT FROM AUTHOR]

Since 1980, the Arctic surface has warmed four times faster than the global mean. Enhanced Arctic warming relative to the global average warming is referred to as Arctic Amplification (AA). While AA is a robust feature in climate change simulations, models rarely reproduce the observed magnitude of AA, leading to concerns that models may not accurately capture the response of the Arctic to greenhouse gas emissions. Here, we use CMIP6 data to train a machine learning algorithm to quantify the influence of internal variability in surface air temperature trends over both the Arctic and global domains. Application of this machine learning algorithm to observations reveals that internal variability increases the Arctic warming but slows global warming in recent decades, inflating AA since 1980 by 38% relative to the externally forced AA. Accounting for the role of internal variability reconciles the discrepancy between simulated and observed AA. Plain Language Summary: The Arctic has been warming four times as quickly as the global mean since 1980. This so‐called Arctic Amplification (AA) has unprecedented impacts on Arctic environments and livelihoods. AA is robustly simulated by climate models, but simulations rarely reproduce the observed levels of AA for 1980–2022. This may be due to a model misrepresentation of the Arctic's sensitivity to increasing greenhouse gases. Another possibility is that the large, observed value of AA is inflated by natural fluctuations in the climate system. Here, we use machine learning to quantify the contribution of natural fluctuations to observed AA. We show that natural fluctuations have inflated AA by 38%, and thus reconcile model‐observation differences and suggest that the observed large AA over 1980 to present would not persist into the future. Key Points: Internally generated and externally forced temperature trends over the Arctic and globe can be partitioned using machine learning methodsInternal variability has enhanced Arctic warming while damping global warming over 1980‐2022Accounting for internal variability in observations reconciles discrepancies between simulated and observed Arctic Amplification [ABSTRACT FROM AUTHOR]

The Arctic is warming much faster than the global average. This is known as Arctic Amplification and is caused by feedbacks in the local climate system. In this study, we explore a previously proposed hypothesis that an associated wind feedback in the Barents Sea could play an important role by increasing the warm water inflow into the Barents Sea. We find that the strong recent decrease in Barents Sea winter sea ice cover causes enhanced ocean‐atmosphere heat flux and a local air temperature increase, thus a reduction in sea level pressure and a local cyclonic wind anomaly with eastward winds in the Barents Sea Opening. By investigating various reanalysis products and performing high‐resolution perturbation experiments with the ocean and sea ice model FESOM2.1, we studied the impact of cyclonic atmospheric circulation changes on the warm Atlantic Water import into the Arctic via the Barents Sea and Fram Strait. We found that the observed wind changes do not significantly affect the warm water transport into the Barents Sea, which rejects the wind‐feedback hypothesis. At the same time, the cyclonic wind anomalies in the Barents Sea increase the amount of Atlantic Water recirculating westwards in Fram Strait by a downslope shift of the West Spitsbergen Current, and thus reduce Atlantic Water reaching the Arctic basin via Fram Strait. The resulting warm‐water anomaly in the Greenland Sea Gyre drives a local anticyclonic circulation anomaly. Plain Language Summary: The Barents Sea has been experiencing a rapid decrease in its winter sea ice extent during the last 30 years. The loss of sea ice creates new areas where, in winter, the relatively warm ocean loses heat to the cold atmosphere. As warm air rises, the warming reduces the sea level air pressure, changing the atmospheric circulation to develop a local anticlockwise wind system centered over the northern Barents Sea. The associated eastward winds in the Barents Sea Opening and southeastward winds in Fram Strait affect how warm water from the North Atlantic moves toward the Arctic. There has been a long debate on whether this wind anomaly can increase the warm Atlantic Water transport into the Barents Sea and thus cause a positive feedback mechanism for further reducing the sea ice through melting. We find that the observed atmospheric circulation changes have no significant impact on the Barents Sea warm water inflow and thus reject the wind feedback as a strong player in contributing to Arctic Amplification. However, strong anomalous southeastward winds in Fram Strait and the northern Nordic Seas cause a southward shift of the warm Atlantic Water recirculation and reduce its flow toward the Arctic. Key Points: A hypothesis that a wind feedback contributes to Arctic Amplification is rejected by performing dedicated wind perturbation simulationsWinter sea ice retreat in the northern Barents Sea causes anomalous cyclonic winds by locally enhancing ocean heat lossAnomalous cyclonic winds result in less Atlantic Water transport through Fram Strait [ABSTRACT FROM AUTHOR]

Reanalysis data and a numerical model are employed to explore how diffusion in the upper ocean evolves in different seasons and to understand its contribution to seasonal heat storage (SHS) and Arctic amplification. The numerical simulation results are closely consistent with observations. First, Arctic solar radiation absorption anomaly (maximum in June) due to ice‐loss is mainly stored as SHS (maximum in June and July) or warms the surface (minimum in July) in observations. Furthermore, the numerical simulation suggests that vertical diffusion dominates SHS formation/discharge. Second, surface ocean becomes warmer than surface air in cold season and releases SHS in Arctic. Sea‐ice loss allows more ocean be directly driven by wind; surface high pressure over Arctic may enhance the wind stress; increased water freeze leads to more salt rejection and additional dense water being produced and sinking downward; all of the occurrences strengthen vertical mixing and release more SHS to the atmosphere. As a result, Arctic surface warming reaches its maximum in cold season. In observations, much stronger outgoing longwave radiation due to the warmer surface in October causes Arctic surface warming to reach its maximum in November, although increased carbon dioxide forcing also contributes to maintain longwave radiation in November. Finally, Arctic surface (air) warming in cold season exhibits different spatial patterns from SHS discharge due to heat convergence/divergence induced by anomalous surface wind, which is determined by the variation in surface high pressure over the Arctic. Plain Language Summary: Due to little seasonal variation in global surface warming, seasonality of Arctic amplification (AA) is mainly determined by seasonal variation of Arctic surface warming. Seasonal heat storage (SHS) in the subsurface Arctic Ocean contributes to Arctic surface warming by holding anomalous heat in summer and releasing it in autumn and winter. As a result, there is significant seasonal variation in AA. In this study, we directly estimate the SHS with ocean data and focus on spatial feature discussions. (a) In summer, additional solar radiation is stored as SHS via vertical diffusion in the upper ocean, resulting in weak Arctic warming. (b) In cold season, more current is driven directly by wind due to sea‐ice loss; variation in surface high pressure over the Arctic enhances the wind stress; more dense water, which is produced due to additional water freezes, sink downward; all of which strengthen the vertical mixing and release more SHS, resulting in the Arctic warming maximum. (c) Although SHS discharge dominates Arctic warming in cold season, anomalous surface wind induces heat convergence/divergence and influences the spatial distribution of surface air temperature, which in turn impacts surface warming via air‐sea/air‐land interactions. Finally, the SHS anomaly and surface warming share different spatial patterns. Key Points: With (without) land surface warming occurring in June, the observational (numerical) Arctic warming/Arctic amplification reaches its minimum in July (June)Though seasonal heat storage (SHS) discharge dominates Arctic warming in cold season, anomalous wind makes their spatial patterns differentLess insulation by sea‐ice loss, wind acceleration or more dense water sinking increases vertical mixing and SHS discharge in cold season [ABSTRACT FROM AUTHOR]

While the annual mean Arctic Amplification (AA) index varied between two and three during the 1970–2000 period, it reached values exceeding four during the first two decades of the 21st century. The AA did not change in a continuous fashion but rather in two sharp increases around 1986 and 1999. During those steps the mean global surface air temperature trend remained almost constant, while the Arctic trend increased. Although the "best" CMIP6 models reproduce the increasing trend of the AA in 1980s they do not capture the sharply increasing trend of the AA after 1999 including its rapid step‐like increase. We propose that the first sharp AA increase around 1986 is due to external forcing, while the second step close to 1999 is due to internal climate variability, which models cannot reproduce in the observed time. Plain Language Summary: The rate of Arctic warming varied in time, while the rate of global warming was nearly constant over the period 1960–2020. This led to a variable Arctic Amplification (AA) defined as a ratio of the Arctic warming trend to mean global warming trend. In addition, the Arctic experienced sudden changes in the rate of warming with step‐like changes in annual mean Arctic Amplification around the years 1986 and 1999. Climate models do not capture the second observed sharp increase of the AA. We suggest that the first AA increase around 1986 is primarily due to external forcing (increasing concentration of carbon dioxide) while the second sharp increase around 1999 is dominated by internal climate variability which current models cannot capture accurately within the time scale of the physical phenomena. Key Points: Annual mean Arctic Amplification (AA) within the period 1970–2020 changed in steep steps around 1986 and 1999. It reached values over 4.0Even those CMIP6 models best at reproducing the AA did not reproduce the second sudden increase near 1999We conjecture that the first AA sharp increase is due to external forcing, while the second is due to internal climate variability [ABSTRACT FROM AUTHOR]

Radiation changes at the Earth's surface alter climate, however, the causes of observed surface radiation changes are not precisely quantified globally. With complete global coverage by ERA‐Interim, the drivers of the clear sky surface downwelling longwave irradiance (SDLI) trends from 1984 to 2017 are quantifiable everywhere. Trends in atmospheric temperature and water vapor contributed significantly (∼90%) to clear sky SDLI trends, including trends consistent with Arctic warming and Southern Ocean cooling. CO2 contributed ∼10% and other greenhouse gases (CH4, N2O, CFC‐11, and CFC‐12) ∼1% to the SDLI trends. These observation‐based results are consistent with early CO2‐doubling climate model calculations wherein temperature and water vapor changes drove ∼90% of the SDLI change. The well‐mixed greenhouse gases drive location‐dependent SDLI trends that are strongest over regions with climatologically high temperatures and low water vapor amounts. Plain Language Summary: Using ERA‐Interim analyzed fields, we show how downward longwave radiation has changed at the Earth's surface from 1984 to 2017. The longwave radiation changes primarily occurred as a consequence of air temperature and water vapor changes. Air temperatures and water vapor amounts increased over most locations. The CO2 increase alone contributed to about 10% of the change in downward longwave radiation that occurred over the last 34 years. This ∼10% change in CO2 was associated with rises in water vapor and atmospheric temperatures that in turn dominated the remaining ∼90% of the change in downward longwave radiation at the surface. Compared to CO2, we find that the other greenhouse gases (methane, nitrous oxide, and chlorofluorocarbons) did not contribute significantly to surface warming from 1984 to 2017. Finally, we show that the greenhouse gases contributed to greater amounts of downward longwave radiation over high latitudes due to lower water vapor amounts. Key Points: Surface downwelling longwave irradiance changes contributed to surface warming over the Arctic and surface cooling over the Southern OceanAtmospheric temperature and water vapor trends drove ∼90% of the surface downwelling longwave irradiance trend and CO2 contributed ∼10%Well‐mixed greenhouse gases caused spatially varying irradiance trends due to spatial variations in the water vapor and temperature fields [ABSTRACT FROM AUTHOR]

Whether sea‐ice loss or lapse‐rate feedback dominates the Arctic amplification (AA) remains an open question. Analysis of data sets based upon observations reveals a 1.11 K per decade surface warming trend in the Arctic (70°–90°N) during 1979–2020 cold season (October–February) that is five times higher than the corresponding global mean. Based on surface energy budget analysis, we show that the largest contribution (∼82%) to this cold season warming trend is attributed to changes in clear‐sky downward longwave radiation. In contrast to that in Arctic summer and over tropics, a reduction in lower‐tropospheric inversions plays a unique role in explaining the reduction of the downward longwave radiation associated with atmospheric nonuniform temperature and corresponding moisture changes. Our analyses also suggest that Arctic lower‐tropospheric stability should be considered in conjunction with sea‐ice decline during the preceding warm season to explain AA. Plain Language Summary: Observations and climate models have consistently shown a stronger surface warming in the Arctic than the global mean, a.k.a. Arctic amplification (AA), which has a strong asymmetry between the cold season and warm season. Previous studies suggested that key contributors to AA are the positive surface‐albedo feedback and lapse‐rate feedback. However, the lapse‐rate feedback itself depends on temperature profiles and sea‐ice loss. Whether sea‐ice loss or lapse‐rate feedback dominates AA remains an open question. Here, by analyzing the latest generation of observationally‐based reanalysis data (1979–2020), we present a unique role of lower‐tropospheric temperature inversion changes in representing the contribution of vertically inhomogeneous atmospheric temperature and associated moisture changes to clear‐sky downward longwave radiation during the cold season. This unique role is not found either in the tropics or during Arctic summertime. We further link the inversion during the cold season to sea‐ice loss during the preceding warm season. These results reinforce previous findings not only that lapse‐rate feedback and sea‐ice loss play a key role in AA but also that lapse‐rate feedback in the cold season is likely a consequence of sea‐ice albedo feedback during the preceding warm season. Key Points: An increase in clear‐sky downward longwave radiation (rldsclr) explains 82% of the recent cold season Arctic surface warming trendReduced low inversion largely explains the Arctic rldsclr reduction due to vertical nonuniform temperature and related moisture changesLapse‐rate feedback in the Arctic cold season is likely a consequence of sea‐ice albedo feedback during the preceding warm season [ABSTRACT FROM AUTHOR]

Surface air temperature trends and extreme events are of global concern and they are related. Here, we show that the occurrence of record hot years over different latitudes from 1960 to 2019 are more strongly correlated with the observational annual mean temperature trends normalized by internal variability. Compared with the raw trends showing Arctic amplification, the normalized trends show a tropical amplification over land. Two hot spots with more frequent occurrence of record hot years are identified: northern hemisphere ocean (vs. land) and southern hemisphere tropical land (vs. mid‐ and high‐latitude lands). Ensemble mean results from 32 Earth system models agree with observations better than individual models, but they do not reproduce observed large differences in correlations across latitudes between normalized trends and record‐breaking events over land versus ocean. Our results enable the quantification of record hot year occurrence through normalized warming trends and provide new metrics for model evaluation and improvement. Plain Language Summary: Surface air temperature trends and extreme hot events are of global concern and they are related. Based on generic time series, it was argued that the occurrence of extreme events depends on the normalized, rather than the raw, trends. However, it remains unknown whether this dependence is relevant to the comparison of extreme hot event occurrences over different regions, such as the Arctic versus Amazon. Here, we show that the occurrence of record hot years over different regions from 1960 to 2019 are more strongly correlated with the observational annual mean temperature trends normalized by internal variability. Compared with the raw trends showing Arctic amplification, the normalized trends show a tropical amplification over land. Two hot spots with more frequent occurrence of record hot years are identified: Northern hemisphere ocean (vs. land) and southern hemisphere tropical land (vs. mid‐ and high‐latitude lands). Results averaged from 32 Earth system models agree with observations better than individual models, but they do not reproduce observed large differences in correlations across latitudes between normalized trends and record‐breaking events over land versus ocean. Our results enable the quantification of record hot year occurrence through normalized warming trends and provide new metrics for model evaluation and improvement. Key Points: Occurrence of record hot years in different latitudes is better correlated with normalized, rather than raw, temperature trendsCompared with the raw trends showing Arctic amplification, the normalized trends show a tropical amplification over landEarth system models' correlations between normalized trends and record‐breaking events are as high over land as over ocean, unlike in observations [ABSTRACT FROM AUTHOR]

The effect of future Arctic amplification (AA) on the extratropical atmospheric circulation remains unclear in modeling studies. Using a collection of coordinated atmospheric and coupled global climate model perturbation experiments, we find an emergent relationship between the high‐latitude 1,000–500 hPa thickness response and an enhancement of the Siberian High in winter. This wave number‐1‐like sea level pressure anomaly pattern is linked to an equatorward shift of the eddy‐driven jet and a dynamical cooling response in eastern Asia. Additional simulations, where AA is imposed directly into the model domain by nudging, demonstrate how the sea ice forcing is insufficient by itself to capture the vertical extent of the warming and by extension the amplitude of the response in the Siberian High. This study demonstrates the importance of the vertical extent of the tropospheric warming over the polar cap in revealing the "warm Arctic, cold Siberia" anomaly pattern in future projections. Plain Language Summary: Surface air temperature averaged within the Arctic Circle is rising more than twice as fast as the rate of the globally averaged temperature. This is known as "Arctic amplification" and is the result of numerous feedbacks in the Earth system. This warming is not restricted to the surface, as the temperature is also increasing at least to the middle troposphere in the Arctic. The effects of this warming on weather patterns in the midlatitudes remain uncertain in observations and model simulations. In this study, we conduct a series of climate model experiments to better understand how the vertical extent of warming in the Arctic atmosphere connects to a strengthening of the Siberian High in boreal winter. This high‐pressure response leads to colder air temperatures in eastern Asia through advection. We find that climate models prescribed with only Arctic sea ice loss do not fully capture the vertical extent of warming in the atmosphere, and thus underestimate the linkage between projected Arctic amplification and Eurasian climate. Key Points: Climate models forced only by sea ice anomalies do not capture the vertical extent of Arctic warming during winterIncrease in thickness of the winter 1,000–500 hPa layer is linked to a strengthening of the Siberian High and cold anomalies in eastern AsiaRole of the stratosphere is unclear due to large internal variability at future global warming of 2° C above preindustrial levels [ABSTRACT FROM AUTHOR]