The long-term trend of extreme ocean waves in the emerging ice-free waters of the summer Arctic is studied using ERA-Interim wave reanalysis, with validation by two drifting wave buoys deployed in summer 2016. The 38-year-long reanalysis dataset reveals an increase in the expected largest significant wave height from 2.3 m to 3.1 m in the ice-free water from the Laptev to the Beaufort Seas during October. The trend is highly correlated with the expected increase in highest wind speed from 12.0 m/s to 14.2 m/s over the ice-free ocean, and less so with the extent of the ice-free water. Since the storms in this area did not strengthen throughout the analysis period, the increase in the expected largest significant wave height follows from the enhanced probability of storms in ice-free waters, which is pertinent to the estimation of extreme sea conditions along the Northern Sea Route.

The relationship between cloud phase, ice‐nucleating particles (INPs), and ocean state is vital in climate system modeling. High wave conditions induced by the Arctic sea‐ice decline and intensified atmospheric forcing over the ice‐free ocean increase dynamical geochemical oceanic mixing and sea spray supply containing organic substances, which could act as INPs promoting formation of lower tropospheric ice‐containing clouds. Here, we show that the surface ocean state regulates INP concentration and ice cloud presence in the boundary layer. Arctic research cruise data from the marginal ice zone in the Chukchi Sea in November 2018 revealed high number concentrations of INPs active greater than °C and ice‐containing clouds under high wave conditions. Chemical analysis revealed high contents of organic carbon and sea salt that coincided with the increased oceanic turbidity. The findings suggest that elevated levels of marine organics will contribute to modification of cloud phase from liquid droplets to ice crystals.

The presence of clouds in the Arctic regulates the surface energy budget (SEB) over the sea‐ice surface and the ice‐free ocean. Following several previous field campaigns, the cloud‐radiation relationship, including cloud vertical structure and phase, has been elucidated; however, modeling of this relationship has matured slowly. In recognition of the recent decline in the Arctic sea‐ice extent, representation of the cloud system in numerical models should consider the effects of areas covered by sea ice and ice‐free areas. Using an in situ stationary meteorological observation data set obtained over the ice‐free Arctic Ocean by the Japanese Research Vessel Mirai (September 2014), coordinated evaluation of six regional climate models (RCMs) with nine model runs was performed by focusing on clouds and the SEB. The most remarkable findings were as follows: (1) reduced occurrence of unstable stratification with low‐level cloud water in all models in comparison to the observations, (2) significant differences in cloud water representations between single‐ and double‐moment cloud schemes, (3) extensive differences in partitioning of hydrometeors including solid/liquid precipitation, and (4) pronounced lower‐tropospheric air temperature biases. These issues are considered as the main sources of SEB uncertainty over ice‐free areas of the Arctic Ocean. The results from a coupled RCM imply that the SEB is constrained by both the atmosphere and the ocean (and sea ice) with considerable feedback. Coordinated improvement of both stand‐alone atmospheric and coupled RCMs would promote a more comprehensive and improved understanding of the Arctic air‐ice‐sea coupled system.

Constraints from ozone (O₃) observations over oceans are needed in addition to those from terrestrial regions to fully understand global tropospheric chemistry and its impact on the climate. Here, we provide a large data set of ozone and carbon monoxide (CO) levels observed (for 11 666 and 10 681 h, respectively) over oceans. The data set is derived from observations made during 24 research cruise legs of R/V Mirai during 2012 to 2017, in the Southern, Indian, Pacific, and Arctic oceans, covering the region from 67^∘ S to 75^∘ N. The data are suitable for critical evaluation of the over-ocean distribution of ozone derived from global atmospheric chemistry models. We first give an overview of the statistics in the data set and highlight key features in terms of geographical distribution and air mass type. We then use the data set to evaluate ozone mixing ratio fields from the tropospheric chemistry reanalysis version 2 (TCR-2), produced by assimilating a suite of satellite observations of multiple species into a global atmospheric chemistry model, namely CHASER. For long-range transport of polluted air masses from continents to the oceans, during which the effects of forest fires and fossil fuel combustion were recognized, TCR-2 gave an excellent performance in reproducing the observed temporal variations and photochemical buildup of O₃ when assessed from ΔO₃∕ΔCO ratios. For clean marine conditions with low and stable CO mixing ratios, two focused analyses were performed. The first was in the Arctic (> 70^∘ N) in September every year from 2013 to 2016; TCR-2 underpredicted O₃ levels by 6.7 ppbv (21 %) on average. The observed vertical profiles from O₃ soundings from R/V Mirai during September 2014 had less steep vertical gradients at low altitudes (> 850 hPa) than those obtained by TCR-2. This suggests the possibility of a more efficient descent of the O₃-rich air from above than assumed in the models. For TCR-2 (CHASER), dry deposition on the Arctic ocean surface might also have been overestimated. In the second analysis, over the western Pacific equatorial region (125–165^∘ E, 10^∘ S to 25^∘ N), the observed O₃ level more frequently decreased to less than 10 ppbv in comparison to that obtained with TCR-2 and also those obtained in most of the Atmospheric Chemistry Climate Model Intercomparison Project (ACCMIP) model runs for the decade from 2000. These results imply loss processes that are unaccounted for in the models. We found that the model's positive bias positively correlated with the daytime residence times of air masses over a particular grid, namely 165–180^∘ E and 15–30^∘ N; an additional loss rate of 0.25 ppbv h⁻¹ in the grid best explained the gap. Halogen chemistry, which is commonly omitted from currently used models, might be active in this region and could have contributed to additional losses. Our open data set covering wide ocean regions is complementary to the Tropospheric Ozone Assessment Report data set, which basically comprises ground-based observations and enables a fully global study of the behavior of O₃.

Predictability of sea ice concentrations (SICs) in the Barents Sea in early winter (November–December) is studied using canonical correlation analysis with atmospheric and ocean anomalies from the NCEP Climate Forecast SystemReanalysis (CFSR) data. It is found that the highest prediction skill for a single-predictormodel is obtained from the 13-month lead subsurface temperature at 200-m depth (T200) and the in-phase meridional surface wind (Vsfc). T200 skillfully predicts SIC variability in 35% of the Barents Sea, mainly in the eastern side. The T200 for negative sea ice anomalies exhibits warm anomalies in the subsurface ocean temperature downstream of the Norwegian Atlantic Slope Current (NwASC) on a decadal time scale. The diagnostic analysis of NCEP CFSR data suggests that the subsurface temperature anomaly stored below the thermocline during summer reemerges in late autumn by atmospheric cooling and affects the sea ice. The subsurface temperature anomaly of the NwASC is advected from the North Atlantic subpolar gyre over ;3 years. Also, Vsfc skillfully predicts SIC variability in 32% of the Barents Sea, mainly in the western side. The Vsfc for the negative sea ice anomalies exhibits southerly wind anomalies; Vsfc is related to the large-scale atmospheric circulation patterns from the subtropical North Atlantic to the Eurasian continent. This study suggests that both atmospheric and oceanic remote effects have a potential impact on the forecasting accuracy of SIC