Japan Geoscience Union Meeting 2019

Presentation information

[E] Oral

A (Atmospheric and Hydrospheric Sciences ) » A-OS Ocean Sciences & Ocean Environment

[A-OS07] Climate variability and predictability on subseasonal to decadal timescales

Thu. May 30, 2019 3:30 PM - 5:00 PM 105 (1F)

convener:Takashi Mochizuki(Japan Agency for Marine-Earth Science and Technology), V Ramaswamy(NOAA GFDL), Doug Smith(Met Office), Yushi Morioka(Japan Agency for Marine-Earth Science and Technology), Chairperson:Yushi Morioka(Japan Agency for Marine-Earth Science and Technology), Takashi Mochizuki(Japan Agency for Marine-Earth Science and Technology)

3:30 PM - 3:45 PM

[AOS07-07] Decadal variability and predictability of the North Atlantic Oscillation

★Invited Papers

*Rosemary Eade1,2, David Stephenson2, Doug Smith1, Adam Scaife1,2, Leon Hermanson1, Nick Dunstone1 (1.Met Office Hadley Centre, 2.University of Exeter)

Keywords:Decadal variability, North Atlantic Oscillation, Polar Amplification, Trend analysis

The winter of 1962/63 was the coldest in the UK in over a century while the mildest winter occurred in 1988/89. For countries to be resilient against the impacts of large weather variations in the future, it is important to assess the likelihood of seeing such extreme fluctuations and understand the physical drivers. In Europe and North America, these fluctuations are related to a combination of year-to-year variability and low-frequency variability of the North Atlantic Oscillation (NAO). The NAO is now a significant source of predictability for seasonal forecasts in these regions, however the signal-to-noise ratio of the ensemble mean to total variability in these ensemble predictions has been shown to be anomalously small, which means the real world is more predictable than our climate models suggest. Here we provide a new evaluation of the ability of climate models to simulate and predict longer-term variability and extreme trends in the NAO. For this we use statistical analyses of the observations and climate model simulations, and assess predictability with a large multi-model ensemble of decadal predictions. We also investigate the drivers of NAO variability and trends using new results from the CMIP6 Decadal Climate Prediction Project and the Polar Amplification MIP.