The combined influences of autumnal snow and sea ice on Northern Hemisphere winters

Abstract

Past studies have demonstrated a significant relationship between the phase and amplitude of the Northern Annular Mode (NAM) and both Arctic sea ice and high‐latitude snow cover during boreal autumn. However, those studies have considered these forcings separately. Here we consider the collective effect of Arctic sea ice and snow cover variability for producing skillful subseasonal forecasts for Northern Hemisphere (NH) winter conditions. We find that these two cryospheric elements interact with the extratropical atmosphere differently through the cold season. Sea ice extent minima during November play a role in stratospheric and tropospheric circulation anomalies during November/December with a secondary maximum in late January/February. October snow cover anomalies, however, have impacts on the NAM primarily during middle to late winter. These timing differences are likely tied to differences in anomalous wave driving between the two cases, though other processes may be in play. We exploit these different influences to produce a skillful forecast model of subseasonal NH surface temperatures using both sea ice and snow cover as predictors, with large gains in skills in January. Overall, our study suggests that the Arctic has a demonstrable and detectable influence on midlatitude winter weather in the present and likely future climate.

Snow‐(N)AO relationship revisited over the whole twentieth century

“…[]. Note that a similar but even stronger stochastic QBO effect might also have inflated the influence of the fall Arctic sea ice extent on the winter NAO over recent decades [e.g., Garcia‐Serrano et al ., ; Furtado et al ., ]. This is at least what is suggested by Figure S10 showing that five out of the seven fall seasons with a sea ice excess over the Barents‐Kara seas experienced a westerly phase of the QBO, while 9 out of the 10 fall seasons with a sea ice deficit experienced an easterly phase.…”

Nonlinear response of mid-latitude weather to the changing Arctic

“…Together these factors make linkage attribution challenging. Many previous data and modelling analyses start with straightforward Arctic changes using, for example, diminished sea ice, and at least implicitly assume quasi-linear, sufficient causal connections 5,7,[25][26][27][28][29][30][31][32][33][34][35][36][37] . While this approach has been helpful in elucidating relevant linkage mechanisms, we provide a view that at the system level, multiple processes can mask simple cause and effect.…”

“…Novel methods to distinguish between statistical and causal relationships 68 , the application of artificial intelligence such as evolutionary algorithms 69 and a Bayesian hierarchical model approach may enable progress. Evidence for a variety of mid-latitude responses to Arctic warming is beginning to emerge [28][29][30][31][32][33][34][35][36][37][38] . Linkage mechanisms vary with season, region and system state, and they include both thermodynamic and dynamical processes.…”

Winter 2015/16: A Turning Point in ENSO-Based Seasonal Forecasts

“…Though tropical variability has been considered the dominant predictor for subseasonal-to-seasonal forecasts, more recently the rapidly warming Arctic has also been suggested as a potential source of mid-latitude weather predictability (Furtado et al, 2016). It has also been shown to add skill to long-range predictions in some global climate models (GCMs; Scaife et al, 2014).…”