A New Perspective on Seasonal Predictability of Winter Climate in Middle Latitudes

Abstract:

While numerical weather prediction is quite accurate, prediction of regional and local climate variability remains a great challenge. One of the factors hampering skillful seasonal prediction (forecasts for future times ranging from about two weeks to a year) is a lack of understanding of the relevant feedbacks between climate subsystems, especially in the extratropics. Sources for seasonal predictability of surface atmospheric anomalies in middle latitudes have been previously sought in teleconnections to the tropical phenomenon of El-Niño-Southern Oscillation and among various extratropical drivers including sea surface temperature anomalies, Arctic sea ice cover extremes, continental snow variability and tropospheric-stratospheric interactions. However, impacts of extratropical subsurface ocean variability on atmospheric teleconnections are poorly known. Here we use a lagged regression analysis between an index of the observed summertime Atlantic water temperature (AWT) anomalies at the entrance to the Barents Sea in the period 1982-2005 and the corresponding year-round data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis to show that subsurface oceanic heat anomalies heading the Arctic Ocean are significant precursors of wintertime atmospheric anomalies over the mid-latitude Eurasia and North Pacific. We find that the remote tropospheric response arises from modification of planetary waves and interaction of mean winds with synoptic eddies leading to a reorganization of the mid-latitude storm tracks. Moreover, we show that this response may occur via a „stratospheric bridge”. As about 50% of the variance of relevant wintertime meteorological fields (storm-track activity, air temperature, winds) in some regions is explained by the previous summer AWT anomalies, we recommend incorporation of subsurface ocean temperature variability in high latitudes into seasonal prediction systems.