1. Introduction
Seasonal weather predictions are helpful tools for risk mitigation, and can guide more efficient use of resources in various sectors of society (Palmer 2002; Troccoli 2010; Bruno Soares and Dessai 2015; De Felice et al. 2015; Clark et al. 2017). In recent years, progress in forecasting the state of the North Atlantic Oscillation (NAO), the dominant pattern of low-frequency atmospheric variability in the North Atlantic (Walker 1924; Barnston and Livezey 1987), has been documented (Scaife et al. 2014; Dunstone et al. 2016; Wang et al. 2017). Better NAO forecasts translate to more accurate predictions of variables such as pressure, temperature, and precipitation (Hurrell 1995; Athanasiadis et al. 2017), as well as storminess (Feser et al. 2015). The physical mechanisms behind the enhanced NAO forecast skill have often been shown to originate in the tropics. For instance, Cassou (2008) showed that the Madden–Julian oscillation (MJO) controls part of the distribution and sequence of the NAO on subseasonal time scales. On longer time scales, tropical rainfall anomalies have been shown to explain a large share of predicted year-to-year variations of the wintertime NAO (Scaife et al. 2017).
Climate variables outside the tropics have also been shown to influence the NAO (Smith et al. 2016, and references therein). The potential for using Arctic sea ice and sea surface temperature (SST) anomalies to predict subsequent surface air temperature (SAT) anomalies in the midlatitudes has been known for a long time, as summarized by Herman and Johnson (1978). For instance, correlations between SAT in northern Europe and sea ice extent in the Nordic seas—the marginal seas of the northeast Atlantic (see Fig. 1d for geographical references)—were identified as far back as the early 1900s, and Arctic sea ice and SST anomalies impart significant skill to modern-era empirical forecast models designed to predict climate variables in the midlatitudes (Koenigk et al. 2016; Hall et al. 2017; Wang et al. 2017). Moreover, it has been shown that proper initialization of Arctic sea ice and SST anomalies could be important for the prediction skill in dynamical seasonal forecast models (Day et al. 2014; Scaife et al. 2014; MacLachlan et al. 2015).
The colors show statistically significant (at the 5% level) 
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
The colors show statistically significant (at the 5% level)
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
The colors show statistically significant (at the 5% level)
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
It has proven useful to separate the influences of SST and sea ice anomalies on other climatic variables into dynamical and thermodynamical components. The dynamical response to a changing Arctic sea ice cover has been widely studied in recent years (e.g., Peings and Magnusdottir 2014; Vihma 2014; Barnes and Screen 2015). For the Barents and Kara Seas specifically, the transient large-scale atmospheric response to sea ice extent anomalies has often resembled the NAO pressure anomaly pattern (e.g., Deser et al. 2007; Kim et al. 2014; Nakamura et al. 2015; King et al. 2016; Screen 2017a). In particular, below-normal late summer or fall sea ice extent in the Barents and Kara Seas has been linked to negative NAO conditions and cold anomalies in Eurasia during winter (Honda et al. 2009; Petoukhov and Semenov 2010; Mori et al. 2014). At the same time, it is important to be aware of the thermodynamical effects of reduced sea ice; model results by Screen (2017a) indicate that negative NAO conditions in years with below-average sea ice cover in the Arctic are more strongly NAO-negative but give less cooling over northern Europe than negative NAO conditions in years with above-average sea ice cover.
We believe that although the latest dynamical prediction systems have started to show useful skill, there is still a need for simple empirical studies for extending our understanding of the pathways for this skill. As Folland et al. (2012) studied potential predictability of northern European winter climate one season ahead to “develop ideas to act as a benchmark for improving the performance of dynamical climate models” (p. 801), we aim here to contribute to the ongoing debate about the influence of the Arctic on the midlatitudes by quantifying the potential for predicting European SAT anomalies from Arctic SST anomalies one season earlier. We do this by addressing three questions. First, we ask where and in which seasons European SAT anomalies can be empirically predicted from Arctic SST anomalies. We consider the whole year, not just the influence of summer/fall anomalies on winter.
Second, we know that the recent decades’ ocean heat transport into the Arctic and the concurrent decline in sea ice appear to be unprecedented in recent centuries (Kinnard et al. 2011; Spielhagen et al. 2011; Onarheim and Årthun 2017). European SAT has increased rapidly during the same period (Jones et al. 2013). If two variables exhibit positive trends that are caused by the same external mechanism (such as increased radiative forcing), the correlation between them will increase. If the variables are not linked by any physical mechanism, they may still be correlated just because of their concurrent trends. But if the variables are linked by a physical mechanism, the inflated correlation can make it seem as if that mechanism is more important than it really is. And conversely, if two variables are negatively correlated due to some physical mechanism, concurrent positive trends in the two variables can make it seem as if that mechanism is less important than it really is (because the trends make the correlation between the two less negative). These issues are often taken into account by making use of linearly detrended data (e.g., Wang et al. 2017). Our question is this: To what extent does the general warming trends of both SAT and SST influence the lagged correlations between SST and SAT? This is assessed by considering how sensitive the potential for prediction is to linear detrending.
Third, several studies have suggested that regional predictability varies with time. One point is that the calculated skill of dynamical models is dependent on the length of the analysis period (Shi et al. 2015). Another is that there are nonstationarities in the relationship between atmospheric circulation patterns and SAT in Europe on decadal time scales (Slonosky et al. 2001). For example, the NAO appears to be more predictable by dynamical models in recent decades than earlier in the twentieth century (Müller et al. 2005). Similar results have been obtained with hindcast experiments with prescribed SST (O’Reilly et al. 2017; Weisheimer et al. 2017). Ogawa et al. (2018) recently claimed that there is no significant link between reduced Arctic sea ice and Eurasian temperatures, suggesting that the observed trends in Eurasian temperatures and atmospheric circulation have been due to internal atmospheric variability. We ask, Has the predictive skill of European SAT from Arctic SST been stationary in time, or has it varied during the twentieth century?
The remainder of the paper is structured as follows. In section 2, the data and methods are described. Section 3 presents our results in five subsections. The geographical distribution of the potential for using Arctic SST anomalies to predict European SAT anomalies is explored in section 3a. The annual cycles of the potential for prediction are investigated for two oceanic regions in section 3b. In section 3c, the role of trends in influencing the potential for prediction is studied. The stationarity of the potential for prediction is assessed in in section 3d, and in section 3e we examine to which degree fall SST could have been used to predict SAT in central England in the period after 1900 and what role the NAO has played in mediating this relationship. We wrap up with a summary and a discussion of the results in section 4.
2. Data and methods
a. Data
The first part of the analysis is based on ERA-Interim data (Dee et al. 2011) for the period from January 1979 to May 2017, from which we used SAT (i.e., 2-m temperature), and SST (which was set to the freezing temperature under sea ice). We always computed SAT anomalies for season A [e.g., October–December or (OND)] and SST anomalies for season B, which followed directly after season A [e.g., January–March (JFM)]. Although we had 38 years of data, we only used 37-yr time series of these variables, as some seasons required data from the end of the years and from the start of the subsequent years (this is why we used data up to May 2017). Note also that all time series were standardized (i.e., we subtracted the interannual mean and divided by the interannual standard deviation). This is important because trends and standard deviations were usually larger for SAT than for SST.
For the stationarity analysis we used data from the coupled ECMWF reanalysis of twentieth-century data (CERA-20C), the first fully coupled atmosphere–ocean reanalysis of the twentieth century (Buizza et al. 2018; Laloyaux et al. 2018), covering the period 1901–2010. After CERA-20C was produced, it was discovered that the model does not melt enough sea ice during summer, and that this led to a gradual accumulation of Arctic sea ice. But as the SSTs were relaxed toward the HadISST2 dataset (Titchner and Rayner 2014), the sea ice extent was kept under control. Data from 10 CERA-20C ensemble members are available, and we used the ensemble mean. Note that we also performed the analysis using the atmospheric hindcast ERA-20C (Poli et al. 2016), and this gave qualitatively similar results.
We also used a wintertime NAO index computed as the standardized pressure anomaly difference between Lisbon, Portugal, and Stykkisholmur, Iceland, provided by the Climate Analysis Section at NCAR in Boulder, Colorado (Hurrell et al. 2003), as well as the aggregated series of Central England Temperature (CET; Parker et al. 1992; Parker and Horton 2005).
b. Methods
Three geographical regions were defined: the Norwegian Sea (westernmost outlines in Fig. 1), the Barents Sea (easternmost outlines in Fig. 1), and Europe (outlines in Fig. 2). The boundaries of the regions were chosen manually, but our results were not particularly sensitive to these subjective choices. These regions were used for area-weighted spatial averaging (just “area averaging” henceforth). In the simple case, we took the average of a variable inside a region and computed interannual time series for that variable, but we also performed slightly more complicated operations. For instance, we calculated the correlation coefficients R between SST in season A in each oceanic grid point and SAT in season B in each grid point in Europe. We then computed the area-averaged R value over all the land points. The resulting value, for which we obtained one for each oceanic grid point, is denoted as RE, where the subscript E indicates that we averaged over Europe. Similarly, we computed area-averaged R values inside each of the two oceanic regions in turn. The area-averaged R values for the Barents Sea are named RB, and we obtained one such value for each land grid point; RN represents area-averaged values for the Norwegian Sea. To aggregate this information further, we also computed the area-averaged RE value inside the oceanic regions, and we call these RBE for the Barents Sea and RNE for the Norwegian Sea.
(a),(b) 
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
(a),(b)
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
(a),(b)
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
To avoid cancellations between positive and negative R values when area-averaging, we also computed the area-averaged R2 values as 
We also used bootstrapping to compute confidence intervals for area-averaged R2 values by computing n = 1000 artificial R2 values for each combination of grid points. Each sample was calculated by drawing 37 random data points from the original time series (with replacement), but now the pairing in time between each SST and SAT data point was maintained. The 10th and 90th percentiles of the n bootstrap samples then made up the bounds of the 80% confidence intervals for the area-averaged R2 values.
3. Results
a. Geographical distributions
To assess the potential for using SST anomalies in season A to predict SAT anomalies in season B, we computed 
The 
In Fig. 1c, 
We now study the geographical patterns of predictability in Europe by exploring 
Figure 2c shows 
b. Annual cycles
We now show 
Figure 3a shows 
(a) 
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
(a)
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(a)
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
In Fig. 3b, 
c. The role of trends
The previous sections showed that R2 was generally sensitive to detrending, and especially so for Norwegian Sea SST from winter to early summer predicting European SAT one season later. To investigate the reasons for this, we now show the trends of standardized and area-averaged SST in the two oceanic regions and SAT in Europe during the ERA-Interim period in Fig. 4. The trends are given in standard deviation units.
ERA-Interim trends of standardized area-averaged SST in the Barents Sea (blue bars), SST in the Norwegian Sea (orange bars), and SAT in Europe (magenta bars), all in standard deviation units per decade. The labels along the x axis indicate that the SST trends are for one season before the SAT trends. For instance, the first set of three bars show SST trends during SON and SAT trends during DJF.
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
ERA-Interim trends of standardized area-averaged SST in the Barents Sea (blue bars), SST in the Norwegian Sea (orange bars), and SAT in Europe (magenta bars), all in standard deviation units per decade. The labels along the x axis indicate that the SST trends are for one season before the SAT trends. For instance, the first set of three bars show SST trends during SON and SAT trends during DJF.
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
ERA-Interim trends of standardized area-averaged SST in the Barents Sea (blue bars), SST in the Norwegian Sea (orange bars), and SAT in Europe (magenta bars), all in standard deviation units per decade. The labels along the x axis indicate that the SST trends are for one season before the SAT trends. For instance, the first set of three bars show SST trends during SON and SAT trends during DJF.
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
We recall that 
For the MAM→JJA season pair (see Fig. 3b), 
d. Stationarity
The third question raised in section 1 was whether the potential for prediction was stationary during the twentieth century. We now use the coupled CERA-20C to quantify how much 
Figure 5a shows 
For each possible 37-yr period in the CERA-20C period, (a) 
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
For each possible 37-yr period in the CERA-20C period, (a)
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
For each possible 37-yr period in the CERA-20C period, (a)
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
In Fig. 6a, 
For each possible 37-yr period in the CERA-20C period, (a) 
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
For each possible 37-yr period in the CERA-20C period, (a)
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
For each possible 37-yr period in the CERA-20C period, (a)
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
From the results presented in Figs. 5 and 6, it is clear that the potential for predictability of European SAT based on Arctic SST has not been stationary with respect to time. Not only did the values of 
(a) Trends of standardized area-averaged Barents Sea SST in OND for each possible 37-yr CERA-20C period (blue circles) and for the ERA-Interim period (blue stars). Orange markers indicate standardized area-averaged European SAT trends in JFM. (b) As in (a), but for Norwegian Sea SST in MAM and European SAT in JJA. All the trends are given in standard deviation units per decade.
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
(a) Trends of standardized area-averaged Barents Sea SST in OND for each possible 37-yr CERA-20C period (blue circles) and for the ERA-Interim period (blue stars). Orange markers indicate standardized area-averaged European SAT trends in JFM. (b) As in (a), but for Norwegian Sea SST in MAM and European SAT in JJA. All the trends are given in standard deviation units per decade.
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
(a) Trends of standardized area-averaged Barents Sea SST in OND for each possible 37-yr CERA-20C period (blue circles) and for the ERA-Interim period (blue stars). Orange markers indicate standardized area-averaged European SAT trends in JFM. (b) As in (a), but for Norwegian Sea SST in MAM and European SAT in JJA. All the trends are given in standard deviation units per decade.
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
Figure 7a shows that the periods when 
e. An application to Central England Temperature
Figures 2a and 2b indicated that SAT in JFM in a region encompassing the British Isles could be skillfully predicted from SST in the Barents Sea in OND during the ERA-Interim period. To what degree does this relationship hold for the observational time series of CET? First, we computed the correlation between ERA-Interim SST during OND in each oceanic grid point in the Nordic seas and CET during JFM. These are shown for raw data in Fig. 8a and for detrended data in Fig. 8b. Significant negative correlations occurred in the northern Barents Sea, as well as in some parts of the Norwegian Sea.
(a),(b) For each oceanic grid point in the Nordic seas region, the correlation coefficient between its SST anomalies in OND and CET anomalies in JFM is shown for raw and detrended data, respectively. The dots indicate coefficients that are not significant at the 5% level. The black outline shows the Barents Sea region. (c),(d) Time series of standardized Barents Sea area-averaged SST anomalies in OND (blue) and −1 × standardized CET anomalies in JFM (orange) during the ERA-Interim period, based on raw and detrended data, respectively, with correlation coefficients and R2 values given above the graphs.
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
(a),(b) For each oceanic grid point in the Nordic seas region, the correlation coefficient between its SST anomalies in OND and CET anomalies in JFM is shown for raw and detrended data, respectively. The dots indicate coefficients that are not significant at the 5% level. The black outline shows the Barents Sea region. (c),(d) Time series of standardized Barents Sea area-averaged SST anomalies in OND (blue) and −1 × standardized CET anomalies in JFM (orange) during the ERA-Interim period, based on raw and detrended data, respectively, with correlation coefficients and R2 values given above the graphs.
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
(a),(b) For each oceanic grid point in the Nordic seas region, the correlation coefficient between its SST anomalies in OND and CET anomalies in JFM is shown for raw and detrended data, respectively. The dots indicate coefficients that are not significant at the 5% level. The black outline shows the Barents Sea region. (c),(d) Time series of standardized Barents Sea area-averaged SST anomalies in OND (blue) and −1 × standardized CET anomalies in JFM (orange) during the ERA-Interim period, based on raw and detrended data, respectively, with correlation coefficients and R2 values given above the graphs.
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
A time series of standardized area-averaged SST anomalies inside the Barents Sea region in OND was calculated from the raw data. This is shown in blue in Fig. 8c. The correlation between this time series and the time series of standardized CET anomalies during JFM (shown multiplied by −1 in orange) was −0.44. The detrended time series of area-averaged SST and CET had a correlation of −0.52, as shown in Fig. 8d. If we had omitted the last year from the time series (SST in OND 2016 predicting SAT in JFM 2017), the correlation coefficients would have been −0.58 for raw data and −0.61 for detrended data. This illustrates again how volatile the correlations during short time periods can be (see also the fluctuations of 
Lagged correlations between area-averaged Barents Sea SST anomalies in OND and CET anomalies in JFM are shown for each possible 37-yr period since 1901 in Fig. 9, using SST from CERA-20C and ERA-Interim data. The results are shown for both raw data in blue and for detrended data in orange. The ERA-Interim values (−0.44 and −0.52, respectively) are shown as stars. The absolute values of the ERA-Interim correlations are unprecedented, as they never exceeded 0.4 during the CERA-20C periods. In fact, the correlations were only statistically significant for a few of those periods. We also note that, as was found for the larger European reference region (see Fig. 5b), the signs of the correlations changed over time, from positive values for the periods starting early in the twentieth century to the present-day negative values.
For each possible 37-yr period in the CERA-20C period, the circles show the correlation coefficients between standardized Barents Sea area-averaged SST anomalies in OND and CET anomalies in JFM. The coefficients that are significant at the 5% level have black edges. The corresponding ERA-Interim values are shown as stars. The colors denote whether the correlations were calculated using raw (blue) or detrended (orange) data, and the numbers along the x axis indicate the first year of each time series.
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
For each possible 37-yr period in the CERA-20C period, the circles show the correlation coefficients between standardized Barents Sea area-averaged SST anomalies in OND and CET anomalies in JFM. The coefficients that are significant at the 5% level have black edges. The corresponding ERA-Interim values are shown as stars. The colors denote whether the correlations were calculated using raw (blue) or detrended (orange) data, and the numbers along the x axis indicate the first year of each time series.
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
For each possible 37-yr period in the CERA-20C period, the circles show the correlation coefficients between standardized Barents Sea area-averaged SST anomalies in OND and CET anomalies in JFM. The coefficients that are significant at the 5% level have black edges. The corresponding ERA-Interim values are shown as stars. The colors denote whether the correlations were calculated using raw (blue) or detrended (orange) data, and the numbers along the x axis indicate the first year of each time series.
Citation: Journal of Climate 31, 20; 10.1175/JCLI-D-18-0016.1
As mentioned, several studies have shown links between reduced sea ice in the Barents Sea in fall and negative NAO conditions in the subsequent winter. By following the steps described in section 2b and using ERA-Interim data for the period 1979–2017, it can be shown that the NAO in JFM fully mediated the total effect of Barents Sea SST anomalies in OND on CET anomalies in JFM. Using the parameters defined in Eqs. (1)–(3), the first step was satisfied because 
4. Summary and discussion
We sought answers to three questions to investigate the potential for using Arctic SST to predict European SAT, and to highlight some challenges associated with empirical prediction models that assume stationarity. First, on the issue of the geographical distribution and annual cycles of the potential for prediction, we found that according to ERA-Interim, the highest potential predictability was obtained when SST in the Barents Sea region in October–December (OND) predicted European SAT in January–March (JFM). Qualitatively, this is in agreement with earlier studies (e.g., Honda et al. 2009; Petoukhov and Semenov 2010; Mori et al. 2014). A statistical mediation analysis revealed that the total effect of Barents Sea SST anomalies in OND on Central England Temperature (CET) anomalies in JFM was fully mediated by NAO anomalies in JFM during the ERA-Interim period. This suggests the following causal pathway for the lagged correlation between SST anomalies in fall and CET anomalies in winter: Barents Sea SST anomalies in OND caused NAO anomalies in JFM, which then again caused CET anomalies during the same season. We did not seek a physical mechanism for the first part of this pathway, but we note that several recent model-based studies have indicated that the polar stratosphere may have acted as a “bridge” between Barents (and Kara) Sea sea ice anomalies in fall and NAO anomalies during subsequent winters (e.g., Sun et al. 2015; Screen 2017b; Zhang et al. 2018). However, we emphasize that the mediation by the NAO was only valid for ERA-Interim data. None of the CERA-20C periods gave indications of a mediating role of the NAO. An in-depth analysis of the physical mechanisms that potentially linked Arctic SST and sea ice anomalies to subsequent midlatitude SAT anomalies through the NAO (and other atmospheric circulation anomalies) is suggested for future work.
We also identified a considerable potential for prediction of European SAT in spring and summer based on SST anomalies in the Norwegian Sea one season before, but this potential was only apparent when raw data were used. When we used detrended data, there was no clearly significant lagged correlation between SST in the Norwegian Sea and European SAT during these seasons.
Our second question touched on the influence of trends on the predictive skill. In some cases, we found that our metric for predictive skill changed considerably after the data had been detrended. This indicates that what appeared to be a significant potential for prediction when nondetrended data were used may have been artificial if there were strong trends in both the predictor and the predictand. Positive trends of both SAT in Europe (Jones et al. 2013) in summer and SST along the Norwegian Atlantic Current (Spielhagen et al. 2011) in spring have transpired over the last few decades, and those trends have contributed to the sensitivity to detrending. Another sensitivity to detrending was also revealed. For SST in the Barents Sea in late autumn predicting European SAT in late winter, our metric for potential predictive skill was higher for detrended than raw data. The reason is that the lagged correlations between the two variables were predominantly negative, while the trends of both variables were positive. These examples demonstrate that it is important to understand how trends influence the correlations between variables before those correlations are used to predict the future.
In answer to our third question, we found that the strength of the links between Arctic SST and subsequent European SAT was nonstationary with respect to time. There are at least four possible explanations for this. First, the length of the periods considered here (37 years) may be too short to obtain robust relationships. When just one year was taken out of our analysis of the lagged relationship between Barents Sea SST anomalies in fall and CET anomalies in winter, the negative correlation between the two increased from −0.44 to −0.58 (using raw data). Second, the fluctuations in potential skill could be linked to the aforementioned strong trends in oceanic heat transport and SST in the Nordic seas and SAT in Europe. Third, our results indicated that the physical mechanisms that have led to lagged correlations between SST and SAT have changed over time. In the case of CET, a statistical mediation analysis revealed that the lagged relationship between Barents Sea SST in fall and CET in winter was mediated by NAO anomalies during the ERA-Interim period (1979–2017). The mediating role of the NAO is in agreement with process-based studies (Deser et al. 2007; Jaiser et al. 2012; Dunstone et al. 2016; King et al. 2016). However, according to CERA-20C, the lagged correlation between Barents Sea SST anomalies in fall and European SAT anomalies changed from being positive early in the twentieth century to negative values for periods starting around the middle of the twentieth century. The same was found for CET anomalies, and the NAO was not a mediator between Barents Sea SST and CET during any of the CERA-20C periods studied here. The implication is that while the proposed physical mechanisms may have been valid during recent decades, it is unlikely that these were operative earlier in the twentieth century. Fourth, the differences between 
In summary, we have shown that SST anomalies in the Norwegian and Barents Seas have shown potential to be skillful predictors of European midlatitude temperature anomalies on the seasonal time scale, but that several pitfalls exist. In particular, there has been a lack of stationarity in the lagged relationship, which brings the future utility of statistical prediction models into question. Our results contribute to the ongoing debate about the influence of Arctic SST and sea ice changes on midlatitude weather, and have immediate implications for the emerging field of seasonal prediction.
Acknowledgments
The authors thank three anonymous reviewers, the editor, and Erica Madonna and Camille Li for constructive comments. Funding for E.W.K. was provided by the Research Council of Norway (RCN) through the Seasonal Forecasting Engine project (Grant 270733) and Blue-Action, funded by the European Union’s Horizon 2020 research and innovation program (Grant 727852). M.Å. was funded by the RCN project PATHWAY (Grant 263223) and Blue-Action. The European Centre for Medium-Range Weather Forecasts (ECMWF) provided the ERA-Interim, ERA-20C, and CERA-20C datasets.
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