Upper-level wind mode over the North Atlantic-Arctic Ocean leading to coherent variations of Eurasian and North American winter temperatures and weakness in the CMIP6 models

This study discovers that the interannual synchronization of winter temperature anomalies between northern Eurasia and North America is closely related to variation in the second leading mode of the 300 hPa meridional wind anomalies (i.e., V300-EOF2) over the North Atlantic-Arctic Ocean. The V300-EOF2 mode modulates the large-scale Rossby wave propagating across the mid-high latitudes of the Eurasian and North American continents at the mid-high level of troposphere, and it also interferes with the climatological quasi-stationary wavenumber-2 to affect the stratosphere circulation, which facilitates the southward intrusion of cold air into Eurasia and North America simultaneously. This study also evaluated whether the CMIP6 high-top (HT) models could more accurately represent the V300-EOF2 mode and the associated modulation mechanism on synchronized temperature variation between Eurasia and North America than the low-top (LT) models. The results demonstrate that although the multi-model ensemble means (MME) of both the HT and LT models can reproduce the V300-EOF mode, they all underestimate its modulation mechanism on atmospheric circulation and coherent temperature changes over mid-latitude Eurasia and North America, with minor differences in simulation skill capability. Most importantly, although containing a full stratosphere, the HT models do not significantly improve their simulation ability to depict the observed linear interference between the anomalous wave-2 and the climatological stationary wave-2 compared to the LT models. Therefore, comparing to the LT models, the CMIP6 HT models present no clear advantages in reproducing the V300-EOF2 mode and its associated low-frequency climate anomalies. These results also have implications for the evaluation of the predictability of concomitant Eurasian-North American cooling in the Subseasonal-to-Seasonal Prediction Project.