Nonstationary Teleconnection Between the Pacific Ocean and Arctic Sea Ice

Abstract

Over the last 40 years observations show a teleconnection between summertime Pacific Ocean sea surface temperatures and September Arctic sea ice extent. However, the short satellite observation record has made it difficult to further examine this relationship. Here, we use 30 fully coupled general circulation models (GCMs) participating in Phase 5 of the Coupled Model Intercomparison Project to assess the ability of GCMs to simulate this teleconnection and analyze its stationarity over longer timescales. GCMs can temporarily simulate the teleconnection in continuous 40‐year segments but not over longer, centennial timescales. Each GCM exhibits considerable teleconnection variability on multidecadal timescales. Further analysis shows that the teleconnection depends on an equally nonstationary atmospheric bridge from the subequatorial Pacific Ocean to the upper Arctic troposphere. These findings indicate that the modulation of Arctic sea ice loss by subequatorial Pacific Ocean variability is not fixed in time, undermining the assumption of teleconnection stationarity as defined by the satellite record.

Do Multi‐Model Ensembles Improve Reconstruction Skill in Paleoclimate Data Assimilation?

“…Variables such as precipitation, geopotential height, or sea ice concentration tend to covary with one another and with surface temperature quite differently among CMIP5 models (e.g., Coats et al., 2013; Parsons et al., 2018; Weare, 2013), so using MMEs could help account for some of this heterogeneity in model covariance. For example, CMIP5 models tend to simulate varied mean states and teleconnections associated with Arctic sea ice concentration (e.g., Bonan & Blanchard‐Wrigglesworth, 2019; Li et al., 2017), so using a MME may help reduce uncertainty where models disagree on sea ice coverage. Multi‐model ensembles could also be used to reconstruct time periods beyond the Common Era, in which paleoclimate observations are even more geographically sparse and covariance uncertainty is expected to play an even larger role in reconstructions.…”

The role of blocking circulation and emerging open water feedbacks on Greenland cold‐season air temperature variability over the last century

“…Given evidence of Pacific SST linkages with northeastern Canada and Greenland summer and annual air temperatures through regional atmospheric forcing (i.e., GBI/NAO; Ding et al, 2014Ding et al, , 2019Bonan and Blanchard-Wrigglesworth, 2020), we evaluate whether (sub)tropical forcing modifies the local environmental and climate influences on Greenland cold-season T2m variability. We test for such relationships using similar methods as in Section 3.2; the PCA and SMLR models are re-run incorporating tropical Pacific indices (ENSO and/or PDO) and the total model-explained variance is then compared to that obtained by the local variables to determine the absolute change and thus the potential impact of (sub)tropical SSTs on the air temperatures (Table 4).…”

“…We additionally compare the autumn and winter temperature series with frontal position data for selected outlet glaciers. To supplement these analyses, we test a hypothesis, previously evaluated at the summer and annual timescales (e.g., Ding et al ., 2014; 2019; Bonan and Blanchard‐Wrigglesworth, 2020), that tropical/North Pacific teleconnections influence the North Atlantic ocean–atmosphere forcing of Greenland autumn and winter temperature variability.…”

Acceleration of western Arctic sea ice loss linked to the Pacific North American pattern

“…This anticyclonic circulation is largely generated by tropical Pacific sea surface temperature (SST) forcing through atmospheric teleconnections 25 – 27 . The link between the tropical Pacific and the Arctic has been referred to as the Pacific–Arctic teleconnection and features enhanced warming and sea ice loss in response to La Niña-like Pacific SST anomaly 27 – 29 (although this relationship is nonstationary in time 30 ). Some other modes of decadal climate variability, such as the Interdecadal Pacific Oscillation (IPO), the Pacific Decadal Oscillation (PDO), and the Atlantic Multidecadal Variability (AMV) have also been shown to drive low-frequency Arctic sea ice fluctuations 21 , 27 , 31 – 35 .…”