Given the lack of sufficient validation data for the ETCW period it is difficult to directly quantify the uncertainty of the sea ice volume trend. Since ERA-20C only provides a single-member simulation, an uncertainty estimate based on ensemble simulations using this dataset is not possible, and uncertainties due to biases in the atmospheric reanalysis could not be addressed in this fashion in any case. To provide some measure of the uncertainty in the ETCW volume trends we use a different approach. Previous research has utilized the relationship between sea ice extent and surface air temperatures as a way to reconstruct sea ice conditions over centuries (Alekseev et al. 2016; Connolly et al. 2017). We also utilize this approach to establish the relationship between 1901 and 2010 sea ice volume anomalies (all months and September) from PIOMAS-20C and surface air temperatures temperature anomalies (all months and September) from ERA-20C over the PIOMAS-20C model domain (ocean areas north of ~49°N). These linear relationships have correlations of −0.49 for all months and −0.64 for September (coefficients given in Table 3). The regression equation is then used to reconstruct sea ice volume based on air temperature anomalies from ERA-20C, CERA-20C, and 20CRv2c. For the latter two, ensemble means are used. Figure 17 shows PIOMAS-20C sea ice volume, temperature anomalies for all months in the year over the PIOMAS-20C model domain from ERA-20C, CERA-20C, and 20CRv2c. Although air temperature anomalies are clearly correlated with sea ice volume anomalies, there is also considerable variability that is not related to average temperature anomalies. This variability is generated by dynamic processes including ocean and sea ice dynamics, by responses to thermodynamic forces not captured by the domain averages, or by interactions between them. In fact, thermodynamic and dynamic (wind) forcing contribute in equal parts to Arctic sea ice volume variability based on dedicated experiments (Koberle and Gerdes 2003; Rothrock and Zhang 2005). While temperature-based sea ice reconstruction therefore can only account for part of the sea ice variability, they can provide some measure of the uncertainty for our derived ETCW sea ice volume trend due to differences in trends in the forcing data. Using the range of reconstructed sea ice volume anomalies as a measure of uncertainty, we can characterize the PIOMAS-20C ice volume anomaly time series (Fig. 18). An examination of temperature and reconstructed sea ice volume anomalies during the ETCW (Fig. 17a) shows that ERA-20C has the weakest temperature increase and smallest sea ice volume decrease during the ETCW. Table 4 provides PIOMAS-20C and trends reconstructed from air temperature anomalies for 1901–40 and 1980–2010. PIOMAS-20C sea ice volume trends from 1980 to 2010 period are about 6 times larger than during the 1901–40 ETCW period. ERA-20C temperature-based volume trends for the ETCW period are about half (−0.27 × 103 km3 decade−1) than when sea ice dynamics (−0.56 × 103 km3 decade−1) are included. Using CERA-20C and 20CRv2c temperature anomalies to reconstruct sea ice volume during the ETCW yields significantly larger sea ice volume losses (−0.43 × 103 km3 decade−1 and −0.45 × 103 km3 decade−1, respectively) because of the relatively stronger warming during that period in both of these reanalysis products. Applying the same approach to reconstructing sea ice volume for September based on air temperature anomalies yields similar results (Fig. 18b). While these temperature-based reconstructions indicate that PIOMAS-20C ice volume losses during the ETCW are likely conservative estimates, the strong contrast with the more recent warming remains robust. Conservatively estimating the uncertainty of the ETCW sea ice loss based on temperature sensitivities as 100% would yield a maximal 1200 km3 decade−1 sea ice volume loss during the ETCW. Comparing this number to the more reliably known sea ice losses during the more recent period, shows that the 1979–2010 losses are still larger by a factor of 3.