Projected trends, trend variances, and signal-to-noise ratios
Figure 9 displays the DJF mean warm extreme TX90 trends over the period 2010–2055 for each ensemble member and the EnM. Figure 10 shows the corresponding cold extreme TX10 results. In general, TX90 increases and TX10 decreases over North America. This suggests that more severe warm days and fewer extreme cold days are projected over North America in the next half century, generally consistent with previous studies (e.g., Meehl and Washington 1996; Sillmann et al. 2013b). The patterns of the warm and cold extreme trends differ from that of the SAT trends described above. In addition, the projected TX10 trends show larger uncertainties across the 50 members compared to the TX90 trends.
The projected TX90 trends exhibit consistent increases of warm days over North America across the ensemble members, with strong warm extreme increases over the western coast and northern Canada, accompanied by relatively weak extreme increases over the midsection of the continent (Fig. 9). The pattern correlations between each member and the EnM range from 0.70 to 0.96, with a mean of 0.87. Meanwhile, most members have relatively higher spatial variances than the EnM variance, with the variance ratio below 150% (Fig. 11, upper-left). These further indicate the good correspondence in the individual members, and differences mainly in trend magnitude. Hence, an increase in extreme warm temperature days will be seen in the future, with high risks of extreme warm days over the western coast and northern Canada, although large uncertainties are seen in the climatological means of individual ensemble members (Fig. 2). By contrast, the projected TX10 trends reveal considerable differences across the 50 ensemble members (Fig. 10). The TX10 trends are dominated by decreases in extreme cold days for most members, but with large variations in spatial pattern and magnitude. Additionally, 9 out of the 50 members show patches of cold extreme increases over North America, especially with TX10 increases over the central US in member 5. The EnM of TX10 trends reveals a somewhat northwest-to-southeast orientation belt of strong cold extreme decreases, extending from Alaska to the northeast US. The pattern correlations between each member and this EnM are low, with a wide range from 0.14 to 0.68 and a mean of 0.43. In addition, all members have much higher spatial variances than the EnM variance, with the variance ratio ranging approximately from 150 to 350% (Fig. 11, upper-right). The spread in individual values of spatial correlations and variances indicates that the individual members depart considerably from the EnM. Overall, the agreement of the TX90 trends among individual members is evident, whereas large uncertainties are apparent in the TX10 trends.
Figure 12 further shows the ESTD and SNR patterns of the TX90 and TX10 trends. For warm extreme trends, the inter-member variability features a broadly uniform structure, with relatively high variances over the western-central US. The SNR pattern tends to be dominated by the EnM trend pattern, with strong signals over the western coast and northern Canada, accompanied by relatively weak signals over the midsection of North America. This SNR pattern also bears some resemblance to that of the SAT trends (Fig. 5). In contrast, large variances of the cold extreme trends across the ensemble members appear over the western-central parts of southern Canada and the US, especially the Great Plains of North America. The SNR pattern is contributed by both the EnM and ESTD patterns of TX10 trends, with strong signals over the southwestern US, northern Canada, and the northeastern US.
The large spread of projected TX10 changes has also been found in previous studies, especially for weak climate change scenarios (Sillmann et al. 2013b). It remains unclear what is responsible for the spread difference between the projected TX10 and TX90 trends. The difference may be attributed to different changes in the surface temperature advection and local radiative and turbulent fluxes that are directly related to the temperature variation through the surface energy balance (e.g., Campbell and Vonder Haar 1997; Durre and Wallace 2001; Loikith and Broccoli 2012; Horton et al. 2015; Krueger et al. 2015; Tamarin-Brodsky et al. 2019), and/or to differences in remote driving mechanisms for warm and cold extremes (e.g., Johnson et al. 2018).
Forced and internal components of projected trends
The temperature extreme trend is also decomposed into externally anthropogenic forced and internal climate variability generated components. For warm extreme TX90, the regional means of total trends over North America range from 13.01%/45 year (member 27, M27) to 21.18%/45 year (member 40, M40) across the 50 members, with a mean of 16.83%/45 year that is also the regional mean of the forced trend. Hence, the regional means of internally generated TX90 trends have a changing range from − 3.82%/45 to 4.35%/45 year. Figure 13 shows the total, externally forced, and internally generated TX90 trends for the two members with the lowest and highest regional mean trends. These two members have broadly similar structure of total trends compared to their EnM (Fig. 13, top and middle rows), with a spatial correlation of 0.89 (0.83) between M27 (M40) and EnM over North America. The most notable discrepancy between them is in trend magnitude, with differences of 10–25%/45 year over the central parts of North America. The difference is also clearly evident in the internally generated TX90 trend (Fig. 13, bottom row), which shows large-scale spatial coherence over North America. In addition, the internally generated trend is comparable to the forced trend in the central parts of the continent, and hence contributes noticeably to the total trend.
For cold extreme TX10, the regional means of total trends range from − 7.25%/45 year (member 17, M17) to − 2.89%/45 year (member 5, M5) over the 50 members, with a mean of − 4.40%/45 year, over North America. Figure 14 presents the total, externally forced and internally generated TX10 trends for M17 and M5. Unlike those seen in TX90 trends, the total TX10 trends in these two members are quite different from their EnM or the forced trend (Fig. 14, top and middle rows), including spatial structure and magnitude of the trends. M17 exhibits decreases of extreme cold days over the whole continent, with striking changes over western-central Canada and the northern US. In contrast, M5 reveals decreases of extreme cold days mainly over Canada and the western and northeastern US, accompanied by cold extreme increases over the central US. The two patterns are hence uncorrelated, with a correlation of 0.04 over North America. The pattern correlation between M17 (M5) and EnM is 0.58 (0.39). In addition, the magnitude of internally generated TX10 trends (Fig. 14, bottom row) is also comparable to that of the forced trend, especially over the central portions of the continent.
The internally generated TX90 and TX10 trends show large-scale spatial coherence over North America, a feature seen above in the SAT trends. The uncertainties in the climatological means and projected changes of extreme warm and cold days suggest that the simulation of North American temperature extremes is likely very uncertain and needs to be applied with caution.
Dynamically adjusted trends
Given the association between large-scale circulation anomalies and the synoptic-scale weather variability (e.g., Wallace and Gutzler 1981; Yu et al. 2019), as analyzed above, we remove the influence of the first three SLP trend predictors to get dynamically adjusted versions of the warm and cold extreme trends. The lower panels of Fig. 11 compare the spatial pattern correlation and variance for individual members to their EnMs for the adjusted TX90 and TX10 trends. For warm extreme TX90, the pattern correlations between each member and the EnM range from 0.71 to 0.96, with a mean of 0.89 that is slightly higher than that of the total trends (0.87) described above. For cold extreme TX10, the pattern correlations still show a wide range from 0.21 to 0.72, with a mean of 0.47 that is also slightly higher than the mean of the total trend (0.43). In addition, the ratios of individual member variances to their EnM variance for the adjusted TX90 and TX10 trends are slightly lower compared to those for the corresponding total trends.
The total and dynamically adjusted TX90 and TX10 trends for the two members discussed above in Figs. 13 and 14 have further been compared. For TX90, the dynamically adjusted trends for M27 and M40 exhibit similar structure compared to the corresponding total trends, with slightly weaker trend values over the centers of action (not shown). The pattern correlation increases slightly from 0.89 for the total trend to 0.94 for the adjusted trend between M27 and EnM, and from 0.83 to 0.86 between M40 and EnM. For TX10, the dynamically adjusted trends also show slightly weaker action centers than their total trends for M17 and M5 (cf. Fig. 15 with the top row of Fig. 14). In addition, a cold extreme decrease belt extending from northwest Canada to the northeast US prevails in the dynamically adjusted patterns for both members (Fig. 15), which broadly follows the extreme decrease belt apparent in the EnM (Fig. 14, middle panels). Accordingly, the two adjusted patterns resemble more than their total trend counterparts do. The pattern correlation of the dynamically adjusted trend between M17 and M5 is 0.60, much higher than that of the total trend (0.04) discussed above. In addition, the pattern correlation increases from 0.58 for the total trend to 0.67 for the adjusted trend between M17 and EnM, and from 0.39 to 0.56 between M5 and EnM.
Overall, by partially reducing the contribution of the circulation-induced components of the TX90 and TX10 trends, the individual ensemble members resemble their ensemble mean more than the total trends do. However, the circulation influence on the projected temperature extreme trends is generally modest, especially for the cold extreme TX10 (Fig. 11, right column).