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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]

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]