Arctic amplification (AA) is a coupled atmosphere-sea ice-ocean process. This understanding has evolved from the early concept of AA, as a consequence of snow-ice line progressions, through more than a century of research that has clarified the relevant processes and driving mechanisms of AA. The predictions made by early modeling studies, namely the fall/winter maximum, bottom-heavy structure, the prominence of surface albedo feedback, and the importance of stable stratification have withstood the scrutiny of multi-decadal observations and more complex models. Yet, the uncertainty in Arctic climate projections is larger than in any other region of the planet, making the assessment of high-impact, near-term regional changes difficult or impossible. Reducing this large spread in Arctic climate projections requires a quantitative process understanding. This manuscript aims to build such an understanding by synthesizing current knowledge of AA and to produce a set of recommendations to guide future research. It briefly reviews the history of AA science, summarizes observed Arctic changes, discusses modeling approaches and feedback diagnostics, and assesses the current understanding of the most relevant feedbacks to AA. These sections culminate in a conceptual model of the fundamental physical mechanisms causing AA and a collection of recommendations to accelerate progress towards reduced uncertainty in Arctic climate projections. Our conceptual model highlights the need to account for local feedback and remote process interactions within the context of the annual cycle to constrain projected AA. We recommend raising the priority of Arctic climate sensitivity research, improving the accuracy of Arctic surface energy budget observations, rethinking climate feedback definitions, coordinating new model experiments and intercomparisons, and further investigating the role of episodic variability in AA.

Understanding the cloud response to sea ice change is necessary for modeling Arctic climate. Previous work has primarily addressed this problem from the interannual variability perspective. This paper provides a refined perspective of sea ice‐cloud relationship in the Arctic using a satellite footprint‐level quantification of the covariance between sea ice and Arctic low cloud properties from NASA A‐Train active remote sensing data. The covariances between Arctic low cloud properties and sea ice concentration are quantified by first partitioning each footprint into four atmospheric regimes defined using thresholds of lower tropospheric stability and midtropospheric vertical velocity. Significant regional variability in the cloud properties is found within the atmospheric regimes indicating that the regimes do not completely account for the influence of meteorology. Regional anomalies are used to account for the remaining meteorological influence on clouds. After accounting for meteorological regime and regional influences, a statistically significant but weak covariance between cloud properties and sea ice is found in each season for at least one atmospheric regime. Smaller average cloud fraction and liquid water are found within footprints with more sea ice. The largest‐magnitude cloud‐sea ice covariance occurs between 500 m and 1.2 km when the lower tropospheric stability is between 16 and 24 K. The covariance between low cloud properties and sea ice is found to be largest in fall and is accompanied by significant changes in boundary layer temperature structure where larger average near‐surface static stability is found at larger sea ice concentrations.

Rapid and, in many cases, unprecedented Arctic climate changes are having far-reaching impacts on natural and human systems. Despite state-of-the-art climate models capturing the rapid nature of Arctic climate change, termed Arctic amplification, they significantly disagree on its magnitude. Using a regional, process-oriented surface energy budget analysis, we argue that differences in seasonal energy exchanges in sea ice retreat regions via increased absorption and storage of sunlight in summer and increased upward surface turbulent fluxes in fall/winter contribute to the inter-model spread. Models able to more widely disperse energy drawn from the surface in sea ice retreat regions warm more, suggesting that differences in the local Arctic atmospheric circulation response contribute to the inter-model spread. We find that the principle mechanisms driving the inter-model spread in Arctic amplification operate locally on regional scales, requiring an improved understanding of atmosphere-ocean-sea ice interactions in sea ice retreat regions to reduce the spread.

Forty years ago, climate scientists predicted the Arctic to be one of Earth’s most sensitive climate regions and thus extremely vulnerable to increased CO₂. The rapid and unprecedented changes observed in the Arctic confirm this prediction. Especially significant, observed sea ice loss is altering the exchange of mass, energy, and momentum between the Arctic Ocean and atmosphere. As an important component of air–sea exchange, surface turbulent fluxes are controlled by vertical gradients of temperature and humidity between the surface and atmosphere, wind speed, and surface roughness, indicating that they respond to other forcing mechanisms such as atmospheric advection, ocean mixing, and radiative flux changes. The exchange of energy between the atmosphere and surface via surface turbulent fluxes in turn feeds back on the Arctic surface energy budget, sea ice, clouds, boundary layer temperature and humidity, and atmospheric and oceanic circulations. Understanding and attributing variability and trends in surface turbulent fluxes is important because they influence the magnitude of Arctic climate change, sea ice cover variability, and the atmospheric circulation response to increased CO₂. This paper reviews current knowledge of Arctic Ocean surface turbulent fluxes and their effects on climate. We conclude that Arctic Ocean surface turbulent fluxes are having an increasingly consequential influence on Arctic climate variability in response to strong regional trends in the air-surface temperature contrast related to the changing character of the Arctic sea ice cover. Arctic Ocean surface turbulent energy exchanges are not smooth and steady but rather irregular and episodic, and consideration of the episodic nature of surface turbulent fluxes is essential for improving Arctic climate projections.

Arctic clouds exhibit a robust annual cycle with maximum cloudiness in fall and minimum cloudiness in winter. These variations affect energy flows in the Arctic with a large influence on the surface radiative fluxes. Contemporary climate models struggle to reproduce the observed Arctic cloud amount annual cycle and significantly disagree with each other. The goal of this analysis is to quantify the cloud-influencing factors that contribute to winter–summer cloud amount differences, as these seasons are primarily responsible for the model discrepancies with observations. We find that differences in the total cloud amount annual cycle are primarily caused by differences in low, rather than high, clouds; the largest differences occur between the surface and 950 hPa. Grouping models based on their seasonal cycles of cloud amount and stratifying cloud amount by cloud-influencing factors, we find that model groups disagree most under strong lower tropospheric stability, weak to moderate mid-tropospheric subsidence, and cold lower tropospheric air temperatures. Intergroup differences in low cloud amount are found to be a function of lower tropospheric thermodynamic characteristics. Further, we find that models with a larger low cloud amount in winter have a larger ice condensate fraction, whereas models with a larger low cloud amount in summer have a smaller ice condensate fraction. Stratifying model output by the specifics of the cloud microphysical scheme reveals that models treating cloud ice and liquid condensate as separate prognostic variables simulate a larger ice condensate fraction than those that treat total cloud condensate as a prognostic variable and use a temperature-dependent phase partitioning. Thus, the cloud microphysical parameterization is the primary cause of inter-model differences in the Arctic cloud annual cycle, providing further evidence of the important role that cloud ice microphysical processes play in the evolution and modeling of the Arctic climate system.