TheinﬂuenceofArcticseaicelossonmid-latitude climateinthecoldseason Claude Frankignoul, Amélie Simon [amelie.simon@locean-ipsl.upmc.fr], Guillaume Gastineau LOCEAN-IPSL, Sorbonne Universités/CNRS/IRD/MNHN, Paris, France CONTEXT Since 1979, satellite observations have shown a rapid decline of the Arctic sea ice extent (SIE) and these trends might continue in response to increased green- house gas concentrations. The Arctic sea ice decline has strong local impacts, but its inﬂuence on the lower latitudes remains actively debated. This work aims at linking the anthropogenic sea ice loss to mid-latitudes winter climate by using observations. OUR BASIC ASSUMPTIONS • The direct atmospheric response to the slow Arctic sea ice loss is the same as that to inter- annual pan-Arctic sea ice ﬂuctuations with iden- tical spatial patterns • The response is sufﬁciently linear to be esti- mated by lag regression analysis • Atmospheric response to SIC ﬂuctuations is seen 1 to 3 months later. DATA & METHODS Observational data • Monthly sea ice concentration (SIC) from pas- sive microwave measurements January 1979- February 2017 (NASA-NSIDC) • ERA-Interim, HadISST, snow cover (NOAA/Rutgers University) Method • Determine the main pattern of monthly sea ice loss between 1979 and 2016 from EOF analysis • Represent sea ice loss by quadratic ﬁt; inter- annual ﬂuctuations are the residual • Estimate the atmospheric response to the inter- annual SIC ﬂuctuations • Estimate statistical signiﬁcance by controlling the False Discovery Rate (FDR) • Determine synchronous inter-annual seas sur- face temperature (SST) and snow cover ﬂuctua- tions. If FDR signiﬁcant, use multiple regression for attribution • Extrapolate these results to the quadratic de- crease of the Arctic SIC. NOVEMBER SEA ICE CONCENTRATION (SIC) PATTERN Raw SIC (with trend) - EOF1 (left) and PC1 (right) Interannual SIC (detrended) - EOF1 (left) and PC1 (right) • Anthropogenic and inter-annual SIC ﬂuctuations has identical spatial pattern with different amplitude CONCOMITANT SST & SNOW ENSO (resp. Siberia snow) times series regression on quadratically detrended SST (resp. snow) Weak la Nina and increased Siberian snow cover LAG MULTIPLE REGRESSION OF GEOPOTENTIAL HEIGHT AT 50 HPA (Z50) IN DECEMBER ON NOVEMBER Simple regression on SIC Multiple regression on ENSO, SNOW and SIC • The simple regression suggests a downward negative NAO-like sig- nal propagation from December to February (not shown) • No signiﬁcant inﬂuence of Arctic sea ice loss in stratosphere. • December simple regression on SIC is mostly due to concomitant Siberian snow cover. LAG MULTIPLE REGRESSION OF SEA LEVEL PRESSURE (SLP) IN JANUARY ON NOVEMBER Simple regression on SIC Multiple regression on ENSO, SNOW and SIC • The contours indicate 10% signiﬁ- cance and hatching is based on 10 % FDR signiﬁcance. • In January, negative NAO-like signal is largely due to SIC • Very similar results in February and much weaker signal in March CONCLUSIONS • A negative NAO/AO-like relation with previous SIC ﬂuctuations is found from December to March. • Multiple regressions with SST and snow cover anomalies (concomitant with the SIC ﬂuctuations) indicate that the December negative NAO-like signal is largely due to Siberian snow cover via a stratospheric path- way whereas the January, February, and March signal is attributed to the SIC via a tropospheric pathway. • Scaling suggests that quadratic decrease in the Arctic SIC from 1979-2016 has increased SLP by 4 hPa and Z500 by 80 m above Greenland and increased surface air temperature over northeastern America by 2.5 K. ACKNOWLEDGMENT This research was supported by the Blue-Action project (European Union’s Horizon 2020 research and innovation program, grant number: 727852.