Seasonal variations of the number of atmospheric river days in the eight latitudinal bins from 25° to 60°N (the last bin is over Alaska) estimated from each of the CMIP5 models (gray) and their MME mean (blue line) during 1975–2004 and from four reanalysis data sets: CFSR (solid red), ERA‐Interim (solid green), Modern‐Era Retrospective Analysis for Research and Applications (MERRA) (dashed red), and National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis 1 (NCEP1) (dashed green) during 1979–2004. The numbers inside each panel indicate the number of seasonal AR days over each area shown in Figure 2 a, including Mexico, southwestern U.S., northwestern U.S., Canada, and Alaska. The numbers in blue and black indicate the ARs from CMIP5 mean and the mean of the four reanalysis data sets.

We compared the number of AR days within the eight coastal bins in each season from the CMIP5 models against the four reanalysis data sets in Figure 2 . Overall, the CMIP5 multimodel ensemble mean (MME) tracks remarkably well the latitudinal and seasonal variations of AR events, with an exception of the underestimation of the springtime number of AR days near the southwest coast of the U.S. (the bin between 35°N and 40°N). Consistent with previous studies, landfalling ARs are more prevalent during fall and winter [e.g., Neiman et al ., 2008 ]. The peak of the landfall shifts from the higher latitudes to the Californian coast from fall to winter; it becomes smaller and retreats poleward more abruptly from the colder to warmer season. There is also a considerable number of AR events that make landfall on the Alaska coast in summer (about four/bin/season [see Neiman et al ., 2008 ]). We also composite the IVT, near‐surface (850 hPa) velocity, and sea level pressure (SLP) based on the AR events for the CMIP5 multimodel ensemble (MME; Figure S2 ) and ERA‐Interim (Figure S3 ) and found consistent spatial distributions.

3.2 The Change of ARs and Thermodynamical and Dynamical Modulations

The impact of climate change on the statistics of landfalling ARs is elucidated in Figure 3 by comparing the numbers of AR days estimated from 1975 to 2004 (black solid) with those from 2070 to 2099 (red), with the percentage increase indicated on the top row of the numbers. The Student's t test is used to assess whether the MME differences are statistically significant, with the significant differences highlighted in red. It is striking to see that the increases in all seasons over all the coastal areas of North America are significant, and the west coast will experience a manyfold increase of AR days, ranging from doubling (near California coast during winter and spring) to 6 times (along the Alaskan coast in spring), depending on the seasons and locations. However, caution should be used in the interpretation of the changes near the west coast of the U.S. and Mexico during summer, as AR events are very rare so the sample size may not yield a credible assessment. As moisture increases, the AR‐induced extreme precipitation is projected to increase in the western U.S. and southwestern Canada particularly during fall and winter when AR‐induced heavy (95th percentile) precipitation increases by 100%–200% or even higher (Text S4 in the SI). The increase is contributed by increases in both the number of AR days with extreme precipitation and the intensity of AR extreme precipitation. The latter indicates that the increase in saturation specific humidity associated with warmer temperature generally does not limit the ability of ARs to produce heavier precipitation with the enhanced water vapor in the future.

Figure 3 Open in figure viewer PowerPoint x axis for the present‐day (1975–2004, black) and future climate under RCP8.5 (2070–2099, red) simulated by the CMIP5 MME. Also shown are the number of AR days determined by rescaling the future IVT , rescaling the present‐day IVT and rescaling the present‐day IVT with the C‐C rate (dashed black). The shading indicates one standard deviation of the intermodel spread. In each panel, the numbers in the first row denoted by the legends on the right side of Figures V 2 Q 2 − V 1 Q 1 )/V 1 Q 1 * 100 %); the second row shows the effect of increasing moisture ; the third row shows the effect of wind changes ; the fourth row indicates the differences between the rescaling using future mean water vapor versus the C‐C rate . Red numbers are statistically significant at 95% level. Number of AR days by seasons for eight latitudinal bins along theaxis for the present‐day (1975–2004, black) and future climate under RCP8.5 (2070–2099, red) simulated by the CMIP5 MME. Also shown are the number of AR days determined by rescaling the future IVT, rescaling the present‐day IVTand rescaling the present‐day IVT with the C‐C rate (dashed black). The shading indicates one standard deviation of the intermodel spread. In each panel, the numbers in the first row denoted by the legends on the right side of Figures 3 b and 3 d with colors corresponding to the scenarios indicate the percentage change of AR days in future compared to the present (()/* 100 %); the second row shows the effect of increasing moisture; the third row shows the effect of wind changes; the fourth row indicates the differences between the rescaling using future mean water vapor versus the C‐C rate. Red numbers are statistically significant at 95% level.

In an attempt to separate the effect of wind changes or dynamical effects from that of increasing moisture or thermodynamical effects in the projected increase of AR days, for each model, each season, and each grid point, we rescale the present‐day IVT by a factor of , where q m is the 30 year average of the IWV over the eastern Pacific basin (25°N to 55°N, 180°W to 130°W) for the corresponding model and season, and subscripts 1 and 2 indicate the present‐day and future periods, respectively. The resultant IVT, referred to as symbolically, is used to identify ARs in a hypothetical scenario in which the present‐day wind advects the moisture scaled to have the same mean moisture of the future warmer climate. An example of the probability distributions of historical, future, and rescaled IVT is shown in Figure S4. We apply the AR detection procedure to the rescaled data, and the resulting statistics of ARs are plotted as the blue lines in Figure 3. The difference between the blue line and the black solid line (representing V 1 Q 1 ) can be construed as the contribution of increasing water vapor in the future climate to the total change of the AR frequency, and the corresponding percentage increases are indicated by the numbers in the second row in Figure 3. Alternatively, one could also rescale the future scenario case (V 2 Q 2 ) back with the ratio of , and contrasting V 2 Q 2 (red lines) with (orange lines) should result in the same thermodynamical effect. Quantitatively similar fractional changes result from compared to (not shown). Through the rescaling above, one may also infer the effect of the changing advection wind in the ARs, or the dynamical modulation, by comparing against V 1 Q 1 , or V 2 Q 2 against . The percentage differences of are shown as the color‐coded numbers in the third row in Figure 3, with the red numbers indicating significant differences at the 95% confidence level. As a cross validation, we also compute the percentage changes of and the result is qualitatively consistent, but with nonnegligible difference.

As explained in Text S5 in the SI, the condition for the rescaling to work is that the rate of increase of the IWV in the ARs can be approximated by that of the seasonal mean IWV, i.e., . This holds true only approximately, as ARs are associated with anomalously high moisture and strong low‐level winds, which may respond to future warming differently compared to the seasonal mean. But for most cases, the approximation has an error of up to about 10% (see Table S2), which may be tolerable for the purpose of qualitative evaluation for the relative contributions of dynamical and thermodynamical effects. For a sufficiently small error, the dynamical and thermodynamical effects from this rescaling exercise should add up to the total change. However, adding the third row to the second row does not exactly result in the numbers in the first row in Figure 3. This is mainly because (i) the rescaling for the dynamical effect is very sensitive to the accuracy of the assumption (see the second term of equation (S3)) and (ii) the covariation, both altering IVT, is ignored in the rescaling approach. As such, the numbers listed in Figure 3 can only be interpreted qualitatively. Nevertheless, examining the numbers in the first to third rows indicates that the covariation contributes negatively to changes of AR days mainly in the latitudinal bin of 35–40°N in fall. Performing a moisture budget analysis of IVT using a method similar to Seager et al. [2010] further confirms the negative effect of the covariation term on the change of the AR days (not shown). Hence, the rescaling method to evaluate the dynamical and thermodynamical contributions works reasonably well for ARs that make landfall in western North America. Overall, it is clear that water vapor increase plays an overwhelmingly dominant role in the increase of AR days, while the dynamical effects are mostly negative or negligible for a majority of seasons and latitudinal areas with one exception: a positive dynamical contribution of 39% to the increase of the spring time ARs near the Alaskan coast. Statistically significant dynamical effects are found in the change of ARs in spring and fall, with the largest dynamical reduction (by 46%) associated with the ARs that influence the California‐Oregon border in fall. This seasonal dependence of the dynamical modulation has a mechanistic explanation that will be discussed later.

The thermodynamical dominance on the increase of the ARs can also be illustrated by the Clausius‐Clapeyron (C‐C) scaling following the approach of Lavers et al. [2013]. This is done by rescaling the specific humidity by the C‐C ratio of increase (7%/K) based on the near‐surface temperature increase averaged over the eastern Pacific basin (25°N to 55°N, 180°W to 130°W). The resultant statistics of ARs (denoted by ) is presented as the black dashed line in Figure 3, and its discrepancies from the AR statistics based on scaling by the actual future humidity are displayed in the fourth row of numbers in Figure 3. It is interesting to note that the C‐C scaling underestimates the actual increase of the water vapor in the ARs throughout all seasons for the west coast of North America. This appears to be consistent with the super‐C‐C increase of column‐integrated water vapor associated with the precipitation extreme simulated in an idealized aquaplanet model in Lu et al. [2014]. However, its origin is less clear because ARs can draw moisture from multiple pathways [Dacre et al., 2015; Ryoo et al., 2015]. Thus, the super‐C‐C rate of increase of water vapor in the ARs hints at a warmer source than the eastern Pacific used in the C‐C scaling. It suffices to say, however, that the C‐C scaling largely explains the CMIP5 ensemble mean change in AR days in the future.