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.

Radiative energy fluxes exiting the atmosphere toward the surface and to space are sensitive to the vertical structure of temperature. Under anthropogenic forcing, this sensitivity gives rise to the lapse‐rate feedback. Negative in the tropics and positive in the Arctic, studies argue that the lapse‐rate feedback is a primary cause of Arctic Amplification. While the negative tropical lapse‐rate feedback is understood mechanistically, the mechanics of the positive Arctic lapse‐rate feedback are less clear. Stable stratification is a necessary condition for the positive Arctic lapse‐rate feedback; however, does stable stratification place any further constraint on the feedback? We find that the magnitude, spatial variability, seasonality, and intermodel spread in the Arctic lapse‐rate feedback are dictated by the surface temperature change and not the degree of stable stratification. Our results support the multiprocess perspective of the Arctic lapse‐rate feedback, whereby the mechanisms responsible for the nonuniform warming profile determine the lapse‐rate feedback.

The Arctic region is warming at a rate more than double the global average, a trend predicted to continue by all Coupled Model Intercomparison Project 5 (CMIP5) climate models. Despite this consistency, significant intermodel spread exists in the simulated Arctic climate related to differences in the Arctic surface radiation budget. Building upon previous work to characterize and understand surface radiation budget biases in climate models, the annual mean and seasonal cycle of the Arctic surface radiation budget in 17 CMIP5 models using the Historical‐forcing scenario is evaluated against state‐of‐the‐art Cloud and Earth's Radiant Energy System Surface Energy Balanced and Filled data. The CMIP5 multimodel ensemble is found to simulate longwave surface fluxes well during the sunlit months (~1 W m⁻² differences in July) but exhibits significant wintertime biases (up to −19 W m⁻²). Shortwave fluxes show substantial across‐model spread during summer; the model standard deviation approaches 20 W m⁻² in July. Applying a decomposition analysis to the cloud radiative effect (CRE) seasonal cycles, an unrealistic compensation is uncovered between the model‐simulated seasonal cycles of cloud fraction, all‐sky/clear‐sky flux differences, and surface albedo that enables models to simulate realistic CRE seasonal cycles with unrealistic individual contributions. This unrealistic behavior in models must be constrained to improve Arctic climate simulation; observational uncertainty is sufficient to do so. Lastly, biases in all and clear‐sky longwave downwelling fluxes positively correlate with model surface temperature in winter, while in summer surface temperature is most strongly related to clear‐sky upwelling radiation biases from surface albedo errors.

Arctic clouds play a key role in Arctic climate variability and change; however, contemporary climate models struggle to simulate cloud properties accurately. Model‐simulated cloud properties are determined by the physical parameterizations and their interactions within the model configuration. Quantifying effects of individual parameterization changes on model‐simulated clouds informs efforts to improve models and provides insights on climate system behavior. This study quantities the influence of parameterization scheme changes on Arctic low cloud properties within the Hadley Center Global Environmental Model 3 atmospheric model using a suite of experiments where individual parameterization packages are changed between the two configurations. The results indicate, surprisingly, that single parameterization changes explain most of the cloud property changes, whereas multiple parameterizations, including non‐cloud schemes, contribute to cloud radiative effect differences. Non‐cloud parameterizations are those not used to compute time step cloud properties. We employ a three‐term decomposition to quantify contributions from (a) regime independent, (b) regime dependent, and (c) the regime frequency of occurrence changes. Decomposition results indicate that cloud property changes vary by meteorological regime, each term contributes differently to each cloud property change, and non‐cloud parameterization changes make substantial contributions to the LW and SW cloud radiative effects by affecting clear‐sky fluxes. The analysis provides insights on the role of non‐cloud parameterizations for setting cloud radiative effects, a pathway for cloud‐atmosphere circulation interactions, raises questions on the importance of infrequently occurring regimes to climate simulations, and on useful observational approaches for improving models.

Arctic clouds exhibit a robust annual cycle with maximum cloudiness in fall and minimum 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, not high, clouds; the largest differences occur between the surface and 950 hPa. 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. Inter-group differences in low cloud amount are found to be a function of the dependence of low cloud amount on the lower tropospheric thermodynamic characteristics. We find that models with a larger low cloud amount in winter produce more cloud ice, whereas models with a larger low cloud amount in summer produce more cloud liquid. Thus, the parameterization of ice microphysics, specifically the ice formation mechanism (deposition vs. immersion freezing) and cloud liquid and ice partitioning, contributes to the inter-model differences in the Arctic cloud annual cycle and provides further evidence of the important role that cloud ice microphysical processes play in the evolution and modeling of the Arctic climate system.