Atmospheric gaseous elemental mercury (GEM) concentrations in the Arctic exhibit a clear summertime maximum, while the origin of this peak is still a matter of debate in the community. Based on summertime observations during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition and a modeling approach, we further investigate the sources of atmospheric Hg in the central Arctic. Simulations with a generalized additive model (GAM) show that long-range transport of anthropogenic and terrestrial Hg from lower latitudes is a minor contribution (~2%), and more than 50% of the explained GEM variability is caused by oceanic evasion. A potential source contribution function (PSCF) analysis further shows that oceanic evasion is not significant throughout the ice-covered central Arctic Ocean but mainly occurs in the Marginal Ice Zone (MIZ) due to the specific environmental conditions in that region. Our results suggest that this regional process could be the leading contributor to the observed summertime GEM maximum. In the context of rapid Arctic warming and the observed increase in width of the MIZ, oceanic Hg evasion may become more significant and strengthen the role of the central Arctic Ocean as a summertime source of atmospheric Hg.

Typhoon Kalmaegi (2014) in the South China Sea (SCS) is simulated using a fully coupled atmosphere–ocean–wave model (COAWST). A set of sensitivity experiments are conducted to investigate the effects of different model coupling combinations on the typhoon simulation. Model results are validated by employing in-situ data at four locations in the SCS, and best-track and satellite data. Correlation and root-mean-square difference are used to assess the simulation quality. A skill score system is defined from these two statistical criteria to evaluate the performance of model experiments relative to a baseline. Atmosphere–ocean feedback is crucial for accurate simulations. Our baseline experiment successfully reconstructs the atmospheric and oceanic conditions during Typhoon Kalmaegi. Typhoon-induced sea surface cooling that weakens the system due to less heat and moisture availability is captured best in a Regional Ocean Modeling System (ROMS)-coupled run. The Simulated Wave Nearshore (SWAN)-coupled run has demonstrated the ability to estimate sea surface roughness better. Intense winds lead to a larger surface roughness where more heat and momentum are exchanged, while the rougher surface causes more friction, slowing down surface winds. From our experiments, we show that these intricate interactions require a fully coupled Weather Research and Forecasting (WRF)–ROMS–SWAN model to best reproduce the environment during a typhoon.

As sea ice disappears, the emergence of open ocean deep convection in the Arctic, which would enhance ice loss, has been suggested. Here, using 36 state‐of‐the‐art climate models and up to 50 ensemble members per model, we show that Arctic deep convection is rare under the strongest warming scenario. Only five models have convection by 2100, while 11 have had convection by the middle of the run. For all, the deepest mixed layers are in the eastern Eurasian basin. When that region undergoes a salinification and increasing wind speeds, the models convect; yet most models are freshening. The models that do not convect have the strongest halocline and most stable sea ice, but those that lose their ice earliest ‐because of their strongly warming Atlantic Water‐ do not have a persistent deep convection: it shuts down mid‐century. Halocline and Atlantic Water changes urgently need to be better constrained in models.

This study explores the seasonally lagged impact of wintertime sea surface temperature (SST) in the Atlantic main development region (MDR) on the subsequent Atlantic hurricane season. It is found that wintertime SST anomalies in the MDR can persist into the summer, explaining 42% of the variance in the subsequent hurricane season's SST during 1951–2010. An anomalously warm wintertime in the MDR is usually followed by an anomalously active hurricane season. Analysis shows an important constraint on the seasonal evolution of the MDR SST by the water vapor feedback process, in addition to the well‐known wind‐evaporation‐SST and cloud‐SST feedback mechanisms over the tropical North Atlantic. The water vapor feedback influences the seasonal evolution of MDR SST by modulating seasonal variations of downward longwave radiation. This wintertime thermal control of hurricane activity has significant implications for seasonal predictions and long‐term projections of hurricane activity over the North Atlantic.