NAO predictability from external forcing in the late 20th century

Abstract

The North Atlantic Oscillation (NAO) is predictable in climate models at near-decadal timescales. Predictive skill derives from ocean initialization, which can capture variability internal to the climate system, and from external radiative forcing. Herein, we show that predictive skill for the NAO in a very large uninitialized multi-model ensemble is commensurate with previously reported skill from a state-of-the-art initialized prediction system. The uninitialized ensemble and initialized prediction system produce similar levels of skill for northern European precipitation and North Atlantic SSTs. Identifying these predictable components becomes possible in a very large ensemble, confirming the erroneously low signal-to-noise ratio previously identified in both initialized and uninitialized climate models. Though the results here imply that external radiative forcing is a major source of predictive skill for the NAO, they also indicate that ocean initialization may be important for particular NAO events (the mid-1990s strong positive NAO), and, as previously suggested, in certain ocean regions such as the subpolar North Atlantic ocean. Overall, we suggest that improving climate models’ response to external radiative forcing may help resolve the known signal-to-noise error in climate models.

Robust but weak winter atmospheric circulation response to future Arctic sea ice loss

“…However, the eddy feedback constraint indicates a robust weakening of the stratospheric polar vortex, suggesting that in the real world the stratosphere may play an active role that amplifies the surface response, consistent with other studies 30,33,36,64,66,74 . Our emergent constraint suggests that models may underestimate the response to sea ice loss and is consistent with other evidence that models underestimate the predictable fraction of NAO variability in seasonal [85][86][87] , interannual 88 , and decadal forecasts [89][90][91] , and in historical climate simulations [92][93][94] . This has been referred to as the signal-to-noise paradox 95 since models are unexpectedly able to predict the real world better than they can predict one of their own ensemble members.…”

NorCPM1 and its contribution to CMIP6 DCPP

“…While the focus of this study leans towards DA innovations, future skill improvement clearly depends also on improving the ESM component of NorCPM. The dynamical model representation has been demonstrated key to skilful climate prediction (Athanasiadis et al, 2020;Yeager et al, 2018) and recent studies revealed a larger role of external forcing than previously thought (Borchert et al, 2021;Klavans et al, 2021;Liguori et al, 2020). The skill benefit from DA-assisted initialization does not only relate to synchronization of internal climate variability, but also to correcting the externally forced climate signal at forecast initialization time -which is subject model and forcing errors.…”

Atmospheric River Variability Over the Last Millennium Driven by Annular Modes

“…Skillful S2S prediction of ARs, for instance, can be accomplished with advanced knowledge of prominent modes of tropical variability (such as the Madden‐Julian Oscillation and quasi‐biennial oscillation; Baggett et al., 2017; Mundhenk et al., 2018). The annular modes too can be skillfully predicted on S2S timescales, though more advanced forecasts are difficult because of chaotic weather‐scale atmospheric motions (Albers & Newman, 2021; Klavans et al., 2021; Marshall et al., 2012; Seviour et al., 2014). We demonstrate here that AR activity is intimately tied to the annular modes: this unequivocal role indicates annular mode forecasts may also skillfully predict AR activity with S2S lead times.…”