1    

Uncertainties in projecting future changes in atmospheric rivers and their 1  

impacts on heavy precipitation over Europe 2  

Yang Gao, Jian Lu and L. Ruby Leung 3  

Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, 4  

Richland, Washington, USA 5  

Correspondence to: Dr. Yang Gao (Yang.Gao@pnnl.gov) 6  

Dr. L. Ruby Leung (Ruby.Leung@pnnl.gov) 7  

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9  

10  

11  

2    

Abstract 12  

This study investigates the North Atlantic atmospheric rivers (ARs) making landfall over western 13  

Europe in the present and future climate from the multi-model ensemble of the Coupled Model 14  

Intercomparison Project Phase 5 (CMIP5). Overall, CMIP5 captures the seasonal and spatial 15  

variations of historical landfalling AR days, with the large inter-model variability strongly 16  

correlated with the inter-model spread of historical jet position. Under RCP 8.5, AR frequency is 17  

projected to increase a few times by the end of this century. While thermodynamics plays a 18  

dominate role in the future increase of ARs, wind changes associated with the midlatitude jet 19  

shifts also significantly contribute to AR changes, resulting in dipole change patterns in all 20  

seasons. In the North Atlantic, the model projected jet shifts are strongly correlated with the 21  

simulated historical jet position. As models exhibit predominantly equatorward biases in the 22  

historical jet position, the large poleward jet shifts reduce AR days south of the historical mean 23  

jet position through the dynamical connections between the jet positions and AR days. Using the 24  

observed historical jet position, which is more poleward than simulated, as an emergent 25  

constraint, dynamical effects further increase AR days in the future above the large increases due 26  

to thermodynamical effects. In the future, both total and extreme precipitation induced by AR 27  

contribute more to the seasonal mean and extreme precipitation compared to present primarily 28  

because of the increase in AR frequency. While AR precipitation intensity generally increases 29  

more relative to the increase in integrated vapor transport except in western Europe, AR extreme 30  

precipitation intensity increases much less relative to the increase in extreme integrated vapor 31  

transport, or even decreases, most notably in mountain regions. Future improvements in 32  

simulating the midlatitude jet and orographic clouds and precipitation may potentially yield 33  

further insights on changes in ARs and their impacts on precipitation in a warmer climate. 34  

3    

Keywords: Atmospheric rivers, CMIP5, jet position, RCP 8.5, extreme precipitation 35  

36  

4    

1. Introduction 37  

Atmospheric rivers (ARs) are narrow corridors of water vapor, usually with a length of 2000 km 38  

or more, that account for over 90% of the meridional moisture transport associated with storm 39  

tracks in the extratropical atmosphere [Zhu and Newell, 1998]. During winter, AR induced 40  

precipitation could account for 15-30% of the total precipitation in Europe and western US 41  

[Lavers and Villarini, 2015]. In northwestern US, ARs contribute to 25-55% of extreme 42  

precipitation [Rutz et al., 2014]. Because of their importance to floods and water resources, ARs 43  

have been extensively studied in the last decade [Gao et al., 2015; Hagos et al., 2015; Lavers 44  

and Villarini, 2015; Leung and Qian, 2009; Neiman et al., 2011; Ralph and Dettinger, 2012; 45  

Ralph et al., 2006]. 46  

As prominent features over the Pacific Ocean, ARs that make landfall in the west coast of North 47  

America have been investigated both in the context of historical climatology and extreme events 48  

[Neiman et al., 2011; Ralph and Dettinger, 2012; Ralph et al., 2006] and future changes under 49  

climate warming [Gao et al., 2015; Payne and Magnusdottir, 2014]. Neiman [2008] and Gao et 50  

al. [2015] found higher number of ARs in the cool (warm) season in the southern (northern) 51  

coast. Changes in AR frequency in the future are closely related to changes in water vapor as 52  

well as atmospheric circulation, both strongly modulated by global warming [Barnes and 53  

Polvani, 2013]. In particular, changes in the winds associated with AR moisture transport were 54  

found to predominantly counter the effects of increasing water vapor that substantially increases 55  

the frequency of landfalling ARs in western North America [Gao et al., 2015]. However, with a 56  

poleward shift of the storm tracks, wind changes could also increase AR days in the high 57  

latitudes such as coastal Alaska in spring time. 58  

5    

In the North Atlantic, Lavers et al. [2013] investigated the dynamical and thermodynamical 59  

modulations of future landfalling ARs in the United Kingdom (50-60°N) and found that the 60  

increase of future winter AR is mainly a result of thermodynamical effect whereas dynamical 61  

effect plays very little role. However, similar to AR frequency in western North America, the 62  

number of AR days in western Europe could vary dramatically by seasons and locations. Thus, 63  

to fully understand the changes of North Atlantic ARs that make landfall in Europe and the 64  

driven mechanism, this study investigates the seasonal changes in AR days and extreme 65  

precipitation across the entire coastal area in western Europe. Using a multi-model ensemble of 66  

climate projections, uncertainty in projecting the changes in AR days is investigated, with the 67  

goal of exploring emergent constraints for AR changes for more robust projections of future 68  

changes. 69  

In what follows, we first investigate the seasonality of landfalling ARs over Europe and examine 70  

the sources of the inter-model spread of AR days in a multi-model ensemble. Using outputs from 71  

the same set of models, the projected changes of the number of AR days under climate warming 72  

and the thermodynamical and dynamical modulations of the AR changes are evaluated. Lastly, 73  

the total and extreme precipitation associated with ARs and the future changes are discussed. 74  

2. Data and method 75  

In this study, the same 24 CMIP5 models used by Gao et al. [2015] and listed in Table S1 of the 76  

supplementary material are analyzed for the historical period of 1975-2004 and future period of 77  

2070-2099 under the RCP 8.5 scenario [Moss et al., 2010; Vuuren et al., 2011]. Details regarding 78  

the CMIP5 data and the four reanalysis data used for model evaluation are discussed in Text S1 79  

in the supplementary material of Gao et al. [2015]. To summarize briefly, outputs from one 80  

6    

member for each CMIP5 model are interpolated to a 1.25o latitude by 1.875o longitude grid. To 81  

evaluate how well the CMIP5 models capture the seasonal variations of ARs, four reanalysis 82  

datasets are used in this study: NCEP Climate Forecast System Reanalysis (CFSR) [Saha et al., 83  

2010], ECMWF Interim Reanalysis Data (ERA-Interim) [Dee et al., 2011], MERRA [Rienecker 84  

et al., 2011] and National Centers for Environmental Prediction/National Center for Atmospheric 85  

Research (NCEP/NCAR) Reanalysis 1 (NCEP1) [Kalnay et al., 1996]. The common period of 86  

these four datasets (1979-2004) is used for evaluating the historical ARs. For consistency, all 87  

reanalysis data are also interpolated to the 1.25o by 1.875o grid. Variables used in this study 88  

mainly include daily mean temperature, specific humidity, zonal and meridional winds from 89  

1000 hPa to 500 hPa and daily total precipitation from CMIP5 and the reanalysis data. 90  

The vertically integrated vapor transport (IVT) is estimated by integrating the moisture transport 91  

between the 1000 hPa and 500 hPa pressure levels as 92  

𝐼𝑉𝑇 = !!

𝑞𝑢  𝑑𝑝!""!"""

!+ !

!𝑞𝑣  𝑑𝑝!""

!"""

!    , 93  

where g represents the gravitational acceleration, q represents the layer mean specific humidity, 94  

𝑢 represents zonal wind and 𝑣 represents meridional wind. 95  

Following [Gao et al., 2015; Lavers and Villarini, 2013; Lavers et al., 2012], we first identify 96  

ARs that make landfall in the west coast of Europe between 30°N to 70°N. The coastal grids, 97  

indicated by the colored grid cells in Figure 1, are grouped into 8 bins shown by the different 98  

colors separated by the gray dashed lines for each 5-degree latitudinal band. Daily IVT was 99  

computed first and on each day, the grid cell with the maximum IVT along the west coast of 100  

Europe is recorded, and the 85th percentile of IVT in each bin was determined as the threshold 101  

7    

for that bin. If the IVT in the recorded grid cell exceeds the threshold of its corresponding bin, a 102  

backward (northwest/west/southwest/south) and forward (north/northeast/east/southeast) search 103  

was performed. The search continues only if one of the adjacent grids exceeds the IVT threshold. 104  

If the total trajectory (including both backward and forward) spans longer than 2000 km [Neiman 105  

et al., 2008; Ralph et al., 2004], and the mean vertically integrated water vapor (IWV) over the 106  

path is greater than 2 cm, all the grids along the path is defined to have an AR day. 107  

108  

3. Results 109  

3.1 Historical number of AR days in CMIP5 110  

To gain confidence in how well the CMIP5 models are able to simulate North Atlantic ARs, the 111  

seasonal total number of AR days simulated across the eight bins over the coastal area in Europe 112  

is compared with four reanalysis datasets, shown in Figure 2. Similar to ARs in eastern Pacific, 113  

ARs occur more frequently in fall and winter along the European coast, with a maximum of 114  

about three to four AR days between 45o–50o N, while spring has the lowest likelihood for AR 115  

occurrence. Overall, the latitudinal variations in the CMIP5 multi-model ensemble mean (MME) 116  

correspond relatively well with the reanalysis data across the four seasons. However, there is 117  

significant inter-model spread and the CMIP5 models overestimate and underestimate the 118  

number of ARs in a few locations and seasons, with the most prominent overestimation 119  

occurring over the 35o-40o bin during the winter. 120  

121  

8    

As a prominent atmospheric circulation feature, uncertainties and biases in simulating AR 122  

frequency may be related to those of the model simulated large-scale environment. Since ARs 123  

are associated with large IWV and IVT, we first analyze the relationships between temperature 124  

and winds, which influence water vapor and its transport, with AR frequency in the models and 125  

global reanalyses. In all seasons, the relationships between the inter-model spread of temperature 126  

and water vapor with the inter-model spread of historical number of ARs are weak, especially in 127  

the areas with larger model biases. On the contrary, there are significant correlations between the 128  

historical AR numbers and the jet stream. Jet speed and position are defined as the maximum of 129  

the seasonal mean 850 hPa zonal wind averaged between 30° W and 10° E and the 130  

corresponding latitude, respectively. 131  

Focusing on the winter season because of the larger biases and inter-model spreads (Figure 3), 132  

we find that the mean winter jet position from the four reanalyses is around 53°N, but the CMIP5 133  

jet position ranges from 41°N to 54°N with a mean position at 48°N, showing an evident 134  

equatorward bias. There are distinct relationships between AR days and jet position on both sides 135  

of the CMIP5 mean jet position. South of the CMIP5 mean jet position (40°-45° N, Fig. 3a), 136  

models that have larger equatorward biases in the jet position simulate more ARs. Conversely, 137  

north of the CMIP5 mean jet position (i.e., 50°-55°N, Fig. 3c), models with more poleward jet 138  

position simulate more ARs. The identical but opposite slopes from linear regression of AR days 139  

on jet position in the two regions suggest an asymmetric response of AR days to the jet position 140  

in the models. For the latitude bin (45°-50° N, Fig. 3b) that coincides with the mean CMIP5 jet 141  

position, AR frequency shows a weak relationship with jet position, but correlates more strongly 142  

with the jet speed. Hence the overestimation (underestimation) of AR days between 40°-45°N 143  

(50°-55°N) in Figure 2a is largely attributable to the equatorward biases in the CMIP5 jet 144  

9    

position, while the overestimation of jet speed contributes to the overestimation of AR days 145  

between 45°-50°N. Similar relationships are also found for the fall (Figure S3) and spring 146  

(Figure S1) seasons. In summer (Figure S2), the mean CMIP5 jet position (52° N) is very close 147  

to that of the four reanalyses, so the AR days are more tightly correlated with jet speed instead of 148  

jet position. Overall, uncertainties in model simulated jet position and speed contribute 149  

importantly to uncertainties in the simulated AR days. 150  

3.2 Thermodynamical and dynamical contributions to changes in AR days in the future 151  

To investigate the impact of climate change on landfalling ARs, Figure 4 shows the numbers of 152  

AR days at present (1975-2004; black) and future under RCP 8.5 (2070-2099; red), with the 153  

percentage change ([RCP 8.5 – Present]/Present) indicated by the numbers in the top row. A 154  

majority of coastal areas show significant increases in the number of AR days by a few times 155  

under warming. The AR frequency peaks between 45° N to 55° N for all seasons in both current 156  

and future climate. This region corresponds to the mean CMIP5 jet position, where the peak AR 157  

days increase between 127% and 275%. 158  

To investigate the thermodynamical and dynamical contributions to the increase of AR days, a 159  

scaling method is used to separate the effects of changes in water vapor and winds that influence 160  

the IVT. As described in detail in Gao et al. [2015] and briefly summarized here, we rescaled the 161  

water vapor simulated for the present climate by a factor of !!!!!!

, where 𝑞!! and 𝑞!! are the 162  

thirty-year mean IWV averaged over the eastern North Atlantic basin (20°N to 60°N, 60°W to 163  

15°W) for the present and future, respectively. ARs were detected using the rescaled IVT that 164  

combines the rescaled water vapor with the present day winds, referred to as 𝑉!𝑄!, for 165  

comparison with the AR days simulated for the present (black line). Contribution to changes in 166  

10    

AR days from the increase of water vapor in the future is estimated as the difference in AR days 167  

between the rescaled and present-day IVT and shown as percentage increases, quantified by the 168  

numbers in the second row in Figure 4. 169  

The thermodynamical contribution can also be evaluated by rescaling the present-day water 170  

vapor based on the Clausius-Clapeyron (C-C) relationship with warming as in Lavers et al. [2013] 171  

for winter AR changes over United Kingdom (50-60° N). The C-C scaling approximated with a 172  

7% increase of water vapor per degree K warming is shown by the dashed line in Figure 4. As 173  

discussed by Gao et al. [2015], the C-C scaling may underestimate the water vapor changes in 174  

ARs, which carry high percentile water vapor content. As shown in Figure 4, the underestimation 175  

using the C-C scaling (comparing the dashed line and the blue line) is strong particularly in 176  

spring, summer and fall, although the agreement with the rescaling of IVT is better during winter 177  

in the latitudinal range of the United Kingdom (50-60N) (see also Lavers et al. [2013] ). 178  

179  

To examine the dynamical modulations of AR events, the effects of wind changes on the ARs 180  

can be inferred by rescaling the future IVT by a factor of !!!!!!

, referred to as 𝑉!𝑄! (orange line in 181  

Figure 4) and comparing the AR days with that of 𝑉!𝑄!(black line in Figure 4). The resulting 182  

difference in AR days due to dynamical effects is shown in Figure 5 together with the 183  

corresponding changes in zonal wind speed. It is clear that the AR days increase and decrease 184  

following the changes in zonal wind speed averaged over each 10-degree latitudinal bin and 20 185  

degrees west of the coastal area. A dipole pattern of positive and negative changes on each side 186  

of the peak AR frequency for the respective season (see the black curves in Figure 5) indicates a 187  

poleward dynamical shift, in concert with the shift of the zonal wind. South of the peak area, the 188  

11    

number of ARs is reduced dynamically due to the decreases in zonal wind speed, while north of 189  

the peak area, the increases of zonal wind speed largely drive the increase of ARs. The change in 190  

AR days due to dynamical effects can also be estimated by comparing the AR days simulated for 191  

the present and future, each detected using its respective 85th percentile threshold for IVT, thus 192  

eliminating the large influence of enhanced water vapor with warming that drives a significant 193  

increase in IVT. As discussed in Gao et al. [2015], this method circumvents errors in the 194  

rescaling method related to the covariance between water vapor and winds. Overall, we found 195  

consistent changes in AR days due to dynamical effects using the rescaling method and the 196  

respective percentile IVT thresholds for present and future climate. 197  

198  

3.3 Emergent constraint on AR changes in a warmer climate 199  

Analysis of the CMIP5 model biases and inter-model spreads in the historical AR days indicates 200  

an important control of the jet stream on ARs. As ARs are associated with extratropical storms, 201  

and storm tracks are steered by the jet stream, the equatorward bias of historical jet position in 202  

CMIP5 models displaces the AR frequency equatorward compared to the global reanalyses 203  

(Figure 3). The dipole changes in AR days due to dynamical effects that increase (decrease) the 204  

AR days equatorward (poleward) of the historical mean jet position (Figure 5) further hints at a 205  

role for jet stream changes in future ARs. 206  

Kidston and Gerber [2010] found a statistically significant correlation between the model-207  

projected changes in jet position and their climatological jet position in CMIP3 models. Similar 208  

correlations are shown in Figure 6 for the CMIP5 models for the Atlantic jet during winter and 209  

spring. Models with a larger equatorward bias in the historical jet position project a larger 210  

12    

poleward jet shift in the future. This relationship is stronger in winter than spring. Unlike the 211  

robust poleward shift of the southern hemispheric jet stream [Kidston and Gerber, 2010], both 212  

poleward and equatorward shifts are projected for the North Atlantic jet, with an ensemble mean 213  

shift of jet position close to zero in winter and about 1.5 degrees poleward in spring (gray 214  

horizontal dashed line in Figure 6). Were the reanalysis jet positions used as emergent 215  

constraints, the mean shift of jet position would be calibrated to be 1- 2.5° equatorward in winter 216  

and ~1° equatorward in spring. 217  

To further link the historical jet position with the change of AR days, Figure 7 shows the 218  

relationships between the changes in AR days and the historical jet position for winter. The 219  

changes in AR days are those associated with the dynamical effects (i.e., excluding the large 220  

changes due to changes in water vapor in a warmer climate) and shown for two regions between 221  

35°-45° N, as suggested by the dipole changes displayed in Figure 5a. In this analysis, the 222  

dynamical effects are estimated by comparing the AR days based on the respective 85% IVT 223  

thresholds for the current and future climate, which yielded higher correlations between the 224  

historical jet position and the change of AR days than if the latter were estimated based on the 225  

rescaling method, possibly because the rescaling method does not account for covariance 226  

between moisture and wind changes [Gao et al., 2015]. The CMIP5 ensemble mean change of 227  

AR days due to dynamics in 35°-45° N (Portugal and Spain) is close to zero (which is also 228  

shown in Figure S4a in the supporting information). Using the historical mean jet position from 229  

the reanalysis as an emergent constraint, that is, adjusting the ensemble mean projection of AR 230  

days along the gray regression slope in Figure 7, AR days are projected to increase by extra 0.4 231  

days above the CMIP5 ensemble mean change over 35°-40° N and 0.7 days over 40°-45° N in 232  

winter, as the more realistic jet (should be more poleward in its mean position) would shift less 233  

13    

poleward (i.e., equatorward by 1- 2.5° as depicted in Figure 6a) with warming and the associated 234  

reduction of the wind at the equatorward flank is smaller. 235  

236  

4. Changes of landfalling AR induced precipitation in the future 237  

By virtue of the enhanced water vapor transport, heavy precipitation often accompanies 238  

landfalling ARs as they encounter mountainous terrains that provide a lifting mechanism for 239  

cloud formation. We define AR induced precipitation as the precipitation over land that occurs 240  

on the same day as the AR and within 250 km of the AR trajectory defined in section 2. The AR 241  

induced precipitation is considered extreme if the daily precipitation amount exceeds the 95th 242  

percentile of all daily precipitation in each season. The fractional contributions of AR induced 243  

precipitation to the seasonal total precipitation are shown in Figure 8, with the ERA-Interim in 244  

the top row and present and future CMIP5 multi-model ensemble (MME) mean in the middle 245  

and bottom rows. Similar fractional contributions but for AR induced total seasonal extreme 246  

precipitation are shown in Figure 9. Note that our estimates of AR contributions are slightly 247  

lower than that of Lavers and Villarini [2015] because a smaller IVT threshold of 50th percentile 248  

(ranges from 218 kg m-1 s-1 to 295 kg m-1 s-1 with an average of 244 kg m-1 s-1) was used in their 249  

study compared to the 85th percentile (ranges from 311 kg m-1 s-1 to 435 kg m-1 s-1 with an 250  

average of 376 kg m-1 s-1) used here for AR detection. The ERA-Interim global reanalysis shows 251  

the largest contributions of ARs to seasonal total precipitation of up to 15% in Portugal and 252  

Spain during fall, followed by up to 10% in the same areas as well as the coastal regions of 253  

France and United Kingdom in winter. Similar seasonal and spatial variability is noted for the 254  

AR contributions to seasonal extreme precipitation, except that the latter more than doubles the 255  

14    

AR contributions to seasonal total precipitation, because ARs are sporadic events with the 256  

potential to generate intense precipitation. 257  

Overall, the CMIP5 MME captures the spatial variability of the AR contributions to seasonal 258  

total and extreme precipitation (Figure 8, 9 top and middle rows), albeit a slight overestimation 259  

from 35° N to 45° N in fall and winter. The overestimation is primarily contributed by the 260  

positive bias in the number of ARs in CMIP5 as discussed in section 3.1. 261  

Under climate warming, the contributions of AR induced precipitation to both the seasonal total 262  

and extreme precipitation increase substantially. For instance, the contributions of ARs induced 263  

precipitation to the total precipitation are projected to increase by 10-20% (bottom row in Figure 264  

8) from 35° N to 45° N in Portugal, Spain, Ireland and United Kingdom, with more modest 265  

increase projected for other areas. Similar increases (15-25%) in the contributions of ARs to 266  

extreme precipitation are found in the west coast of Europe from 35° N to 60° N, as a result ARs 267  

are projected to contribute to 25-70% of seasonal extreme precipitation in the future. 268  

The analysis presented above shows that ARs contribute more to seasonal total and extreme 269  

precipitation compared to other precipitation systems in a warmer climate. To understand the 270  

changes in AR precipitation, Figure 10 shows the percentage changes in AR total and extreme 271  

precipitation and the corresponding changes in IVT for winter. Lavers et al. [2014] showed that 272  

in Europe, AR induced precipitation is highly correlated with the IVT, particularly in 273  

mountainous regions where moisture flux convergence from orographic uplift is quasi-stationary. 274  

In particular, IVT may be a useful proxy for moisture flux convergence upwind of mountains. 275  

Hence a comparison between the changes in precipitation and IVT may provide some insights on 276  

potential changes in AR related precipitation processes in a warmer climate. Most areas in 277  

15    

Europe are marked by large percentage increases in total precipitation of above 100% (Fig. 10a). 278  

The changes are particularly significant in central Europe near 50oN. Changes in IVT (Fig. 10c) 279  

generally follow a similar spatial pattern, except they are more modest especially in central 280  

Europe suggesting possible changes in precipitation processes. Compared to the changes in AR 281  

total precipitation (Fig. 10a), the increases in AR extreme precipitation (Fig. 10b) are generally 282  

smaller, and more comparable to the changes in AR extreme IVT (Fig. 10d). 283  

To delineate the contributions to changes in AR precipitation from changes in AR precipitation 284  

frequency versus precipitation intensity, Figure 11 shows the same changes corresponding to 285  

Figure 10, but with the precipitation and IVT normalized by the AR days, hence representing the 286  

changes in intensity rather than the total amount. For all the quantities shown in the Figure 11, 287  

the percentage changes in intensity are much smaller than that of the total amount, demonstrating 288  

that the significant increases shown in Figure 10 as well as the increased contributions of ARs to 289  

total and extreme precipitation shown in Figures 8 and 9 are largely associated with the increases 290  

in AR days in the future. Comparing the intensity changes in AR precipitation (Fig. 11a) and 291  

IVT (Fig. 11c), most areas especially in central Europe still exhibit amplifications of the changes 292  

from IVT to precipitation, potentially related to changes in precipitation processes in a warmer 293  

climate. However, decreases in AR precipitation intensity (Fig. 11a) are notable in Portugal, 294  

Spain, and Morocco, despite increases in IVT intensity corresponding to warming in the future. 295  

Similar results are also obtained for the fall season when ARs also occur frequently. 296  

Comparing the percentage changes in AR total (Fig. 11a) and extreme precipitation (Fig. 11b) 297  

intensity, changes in the latter are generally much smaller. An even more striking difference 298  

between the two is that while AR total precipitation intensity (Fig. 11a) is mostly amplified 299  

compared to the IVT (Fig. 11c) changes, the increases in AR extreme precipitation (Fig. 11b) 300  

16    

intensity are overall subdued compared to the extreme IVT (Fig. 11d) changes. In Portugal, 301  

Spain, and France, AR extreme precipitation intensity (Fig. 11b) increases by less than 5% 302  

compared to increases of up to 20% in the extreme IVT (Fig. 11d). In the Scandinavian 303  

mountains and the Alps, AR extreme precipitation intensity (Fig. 11b) decreases by a few 304  

percent compared to increases in AR extreme IVT by up to 50%. This suggests potentially 305  

different processes governing changes in AR total and extreme precipitation in a warmer climate. 306  

307  

Discussions and Conclusions 308  

This study investigates the landfalling atmospheric rivers over western Europe in the present and 309  

future climate. The CMIP5 models reasonably capture the seasonal and spatial distributions of 310  

AR days. Although the multi-model mean AR days are generally comparable to those 311  

determined from four global reanalysis products, there are significant inter-model spreads, 312  

indicating large uncertainties in simulating AR days by state-of-the-art global climate models. 313  

Analysis of the atmospheric circulation demonstrates statistically significant correlations of 314  

model biases and uncertainties in simulating AR days with those of the jet position and strength 315  

simulated by the models. As most CMIP5 models show an equatorward jet bias, more AR events 316  

are simulated south of the mean jet position (45°-55°N) by the CMIP5 models than the 317  

reanalyses and vice versa for the poleward side of the jet. 318  

A poleward shift of the annual mean jet position in a warmer climate has been identified in 319  

several generations of coupled climate simulations [Barnes and Polvani, 2013; Kidston and 320  

Gerber, 2010; Miller et al., 2006; Swart and Fyfe, 2012; Yin, 2005]. Despite the rather robust 321  

tendency for the poleward shift, models disagree on the extent of the poleward shift and the 322  

17    

eastward extension of the jet that results in large uncertainty in projecting regional precipitation 323  

changes in the extratropics [Langenbrunner et al., 2015; Neelin et al., 2013]. The dependence of 324  

ARs on the jet stream has been demonstrated for ARs in aquaplanet simulations [Hagos et al., 325  

2015] and ARs making landfall in western North America [Gao et al., 2015; Hagos et al., 2016]. 326  

Analysis in this study for ARs making landfall in western Europe provides further evidence of 327  

the relationships between AR frequency and jet position and speed. Thus, uncertainty in 328  

projecting the changes in jet stream may also project onto uncertainty in projecting changes in 329  

ARs. 330  

By the end of this century, the number of AR events in western Europe was projected to increase 331  

by a few times compared to the historical level. Through a rescaling method, we found that 332  

thermodynamical changes play a dominate role in the future increase of ARs, but dynamical 333  

effect due to changes in wind also plays a significant role. The changes in AR days due to 334  

dynamical changes show a dipole feature with an overall negative (positive) effect south (north) 335  

of MME mean jet position. This dipole feature aligns well with the changes in zonal wind speed 336  

and consistent with the poleward jet shifts projected by the CMIP5 models. 337  

Previous studies have identified significant correlations between the model-projected shifts in the 338  

midlatitude jet positions with the simulated climatological jet position in the southern 339  

hemisphere [Grise and Polvani, 2014; Kidston and Gerber, 2010]. In the North Atlantic, we 340  

found similar correlations for winter and spring. With the overall equatorward bias in the jet 341  

position in CMIP5, the projected future jet positions are predominantly poleward shifted. 342  

Recognizing the relationships between the jet positions and AR frequency, we tested the use of 343  

the historical jet positions as emergent constraints on the projection of AR days in the future and 344  

found statistically significant correlations between the two. Accounting for the equatorward jet 345  

18    

bias in CMIP5 climatology compared with the global reanalyses, the projected increase of winter 346  

AR days at the equatorward side of the mean jet by the CMIP5 MME should be adjusted upward, 347  

in addition to the larger associated with water vapor increases in the future. 348  

For an emergent constraint to be effective, the empirical relationship between inter-model 349  

variations of an observable quantity and the inter-model variations in a future climate prediction 350  

must have a physical explanation [Klein and Hall, 2015]. The dynamical basis for the historical 351  

jet position as an emergent constraint for AR changes consists of two relationships linking the 352  

historical jet position with the projected jet shift, and the projected jet shift with the projected AR 353  

frequency changes. Barnes and Hartmann [2010a] found that when the Atlantic jet is more 354  

equatorward during the negative phase of the North Atlantic Oscillation (NAO), positive eddy 355  

feedback due to the enhanced baroclinicity and anomalous northward eddy propagation away 356  

from the jet helps maintain the jet in an equatorward position and a persistent negative NAO. 357  

Consistent with the above finding and more generally, Barnes and Hartmann [2010b] and 358  

Barnes and Polvani [2013] found that jet variability decreases as the mean jet is located more 359  

poleward. Hence the eddy feedback mechanism may also explain the over-persistence of the 360  

equatorward biased jet simulated by the CMIP5 models [Gerber et al., 2008], and hence their 361  

possible exaggeration of the poleward shift in response to external forcing [Barnes and Polvani, 362  

2013; Kidston and Gerber, 2010]. As ARs are closely coupled to the extratropical cyclones, a 363  

larger poleward jet shift reduces the likelihood for extratropical storm tracks to tap tropical 364  

moisture or reduces the wind speeds for transporting significant moisture from the lower 365  

latitudes. Given the relationships between ARs and jet stream, improvements in simulating the 366  

jet position and strength may lead to improvements in simulating the number of ARs and reduce 367  

uncertainties in projecting AR changes in the future. 368  

19    

Due primarily to the more abundant moisture but also the poleward jet shift that increases the 369  

wind speeds in the higher latitudes, ARs contribute more importantly to both total and extreme 370  

precipitation in western Europe in the future. However, changes in AR total and extreme 371  

precipitation intensity reveal smaller increases compared to the amount, suggesting that the 372  

increased contributions from AR are mainly a result of increased AR frequency in a warmer 373  

climate. Generally the increase in AR precipitation intensity is larger compared to the increase in 374  

IVT, except in Portugal, Spain, and Morocco where AR precipitation intensity decreases despite 375  

an increase in IVT in the future. 376  

AR extreme precipitation intensity increases much more modestly compared to the AR extreme 377  

IVT intensity or even decreases in some regions. Previous studies found that extreme 378  

precipitation in the extratropics scales approximately with thermodynamics following the CC 379  

relationship as the dynamical effects from changes in vertical velocity are small [Emori and 380  

Brown, 2005; O'Gorman and Schneider, 2009] except potentially for regions influenced by the 381  

poleward jet shift [Lu et al., 2014; O'Gorman and Schneider, 2009]. For non-convective events 382  

in the extratropics, O’Gorman [2015] further showed weak dependence of extreme precipitation 383  

on changes in static stability. With dynamical influence potentially limited, possible reasons for 384  

reduction in AR extreme precipitation include changes in microphysical processes related to 385  

freezing level and hydrometeor fall speed [Singh and O'Gorman, 2014] and changes in 386  

orographic precipitation, with precipitation shifted downwind, reducing the total precipitation 387  

compared to the CC scaling [Siler and Roe, 2014]. The latter may be particularly relevant for the 388  

negative changes in AR extreme precipitation in the Scandinavian mountains and the Alps. 389  

However the simulated changes reported here may be hampered by the relatively coarse spatial 390  

resolution and uncertainties in cloud parameterizations in the CMIP5 models, so further 391  

20    

investigations are warranted to understand the dynamical, thermodynamical, and microphysical 392  

factors that modulate the AR extreme precipitation response to warming. As model resolution 393  

increases with advances in computational resources, potential improvements in simulating the jet 394  

stream [Lu et al., 2015] and orographic effects may improve understanding and lead to more 395  

reliable projections of future changes in AR days and extreme precipitation. 396  

397  

Acknowledgments 398  

This study was supported by the U.S. Department of Energy Office of Science Biological and 399  

Environmental Research (BER) as part of the Regional and Global Climate Modeling program. 400  

PNNL is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830. 401  

We acknowledge the World Climate Research Programme's Working Group on Coupled 402  

Modelling, which is responsible for CMIP, and we thank the climate modeling groups for 403  

producing and making available their model output. 404  

405  

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 515  

List of Figure captions 516  

Figure 1. The grids used to detect atmospheric rivers. The color-coded squares are the grid cells 517  

used to detect ARs, with different colors indicating different latitudinal bins, which are also 518  

separated by the dashed gray lines. 519  

Figure 2. Box and whisker plots of the number of atmospheric river days in each season from 30° 520  

to 70°N for each of the eight bins from CMIP5 models from 1975-2004. The horizontal line 521  

within the box indicates the CMIP5 median, the boundaries of the box indicate the 25th and 75th 522  

percentile, and the whiskers indicate the highest and lowest values of the results from CMIP5. 523  

The “•” marked inside the box indicates the CMIP5 MME mean. The number of atmospheric 524  

rivers from the four reanalysis data sets is marked with triangles, i.e., CFSR (blue), ERA-525  

INTERIM (red), MERRA (green), and NCEP1 (gold) during 1979-2004. 526  

Figure 3. Historical number of AR days versus jet position (top row) and speed (bottom row) in 527  

winter for three latitudinal bins from 40 to 55 degrees north. Each black dot corresponds to one 528  

CMIP5 model whereas the triangles of different colors denote the reanalysis datasets. An asterisk 529  

indicates statistically significant correlations shown in red. 530  

25    

Figure 4 The CMIP5 MME seasonal total numbers of AR days over 8 latitudinal bins (30-70N) 531  

for present (1975-2004, black) and future climate conditions in RCP8.5 (2070-2099, red). Also 532  

shown are the numbers of AR days from rescaling of the future IVT by the present IWV 533  

(V!Q!,  orange) and present IVT by future IWV (V!Q!,    blue) and using the C-C scaling (dashed 534  

black). The shaded areas represent one standard deviation of the CMIP5 inter-model spread. The 535  

numbers on the top row in each panel indicate the percentage changes of AR, calculated based 536  

on (V!Q! − V!Q!)/V!Q! ∗ 100% whereas the bottom row shows the thermodynamical effect 537  

calculated through (  V!Q! − V!Q!)/V!Q! ∗ 100%, with the red numbers indicating statistical 538  

significance at 95% level. 539  

Figure 5 Changes in the number of AR days due to dynamics (black curve), calculated using 540  

ARs detected with V!Q! (orange line in Figure 4) minus that detected with V!Q! (the black line 541  

in Figure 4) as a function of latitude. Also shown are the changes of zonal wind speed (red 542  

curves), calculated by averaging the zonal wind speed for each 10-degree latitudinal bin 543  

extending from the coast to 20 degrees west. The black and red dots indicate statistically 544  

significant changes for AR days and zonal winds, respectively, at 95% level. Shading 545  

corresponds to one standard deviation above and below the multi-model mean. 546  

Figure 6. Correlation between the CMIP5 simulated historical jet position and projected changes 547  

of jet position in the future under RCP 8.5 for winter and spring. The four colored circles 548  

indicate the jet position from the four reanalysis datasets. The multi-model mean historical jet 549  

positions and changes of jet positions are indicated by the grey and red dashed lines, respectively. 550  

Figure 7. Similar to Figure 6 but for correlation between the historical jet position and the 551  

changes of AR days in winter at two latitudinal bins due to dynamical effects. 552  

26    

Figure 8. The fractional contribution of AR induced precipitation to the total precipitation in 553  

each season from ERA-Interim (1979-2004; top row), CMIP5 MME at present (1975-2004; 554  

middle row), and the difference between CMIP5 MME in RCP 8.5 and present (2070-2099 555  

minus 1975-2004; bottom row). 556  

Figure 9. The same as Figure 8 but for extreme precipitation. 557  

Figure 10. Percentage change in winter AR total precipitation (a), extreme precipitation (b), total 558  

IVT (c), and extreme IVT (d) from the CMIP5 MME comparing the present (1975-2004) with 559  

the future (2070-2099). 560  

Figure 11. Same as Figure 10, but for intensity of AR total precipitation (a), extreme 561  

precipitation (b), IVT (c), and extreme IVT (d). 562  

563  

564  

565  

566  

567  

568  

569  

570  

571  

572  

573  

574  

575  

576  

27    

577  

Figure 1. The grids used to detect atmospheric rivers. The color-coded squares are the grid cells 578  

used to detect ARs, with different colors indicating different latitudinal bins, which are also 579  

separated by the dashed gray lines. 580  

 581  

28    

582  

Figure 2. Box and whisker plots of the number of atmospheric river days in each season from 30° 583  

to 70°N for each of the eight bins from CMIP5 models from 1975-2004. The horizontal line 584  

within the box indicates the CMIP5 median, the boundaries of the box indicate the 25th and 75th 585  

percentile, and the whiskers indicate the highest and lowest values of the results from CMIP5. 586  

The “•” marked inside the box indicates the CMIP5 MME mean. The number of atmospheric 587  

rivers from the four reanalysis data sets is marked with triangles, i.e., CFSR (blue), ERA-588  

INTERIM (red), MERRA (green), and NCEP1 (gold) during 1979-2004. 589  

29    

590  

Figure 3. Historical number of AR days versus jet position (top row) and speed (bottom row) in 591  

winter for three latitudinal bins from 40 to 55 degrees north. Each black dot corresponds to one 592  

CMIP5 model whereas the triangles of different colors denote the reanalysis datasets. An asterisk 593  

indicates statistically significant correlations shown in red. 594  

595  

30    

596  

597  

Figure 4 The CMIP5 MME seasonal total numbers of AR days over 8 latitudinal bins (30-70N) 598  

for present (1975-2004, black) and future climate conditions in RCP8.5 (2070-2099, red). Also 599  

shown are the numbers of AR days from rescaling of the future IVT by the present IWV 600  

(V!Q!,  orange) and present IVT by future IWV (V!Q!,    blue) and using the C-C scaling (dashed 601  

black). The shaded areas represent one standard deviation of the CMIP5 inter-model spread. The 602  

31    

numbers on the top row in each panel indicate the percentage changes of AR, calculated based 603  

on (V!Q! − V!Q!)/V!Q! ∗ 100% whereas the bottom row shows the thermodynamical effect 604  

calculated through (  V!Q! − V!Q!)/V!Q! ∗ 100%, with the red numbers indicating statistical 605  

significance at 95% level. 606  

607  

608  

609  

610  

611  

612  

613  

614  

32    

615  

Figure 5 Changes in the number of AR days due to dynamics (black curve), calculated using 616  

ARs detected with V!Q! (orange line in Figure 4) minus that detected with V!Q! (the black line 617  

in Figure 4) as a function of latitude. Also shown are the changes of zonal wind speed (red 618  

curves), calculated by averaging the zonal wind speed for each 10-degree latitudinal bin 619  

extending from the coast to 20 degrees west. The black and red dots indicate statistically 620  

significant changes for AR days and zonal winds, respectively, at 95% level. Shading 621  

corresponds to one standard deviation above and below the multi-model mean. 622  

623  

624  

33    

625  

Figure 6. Correlation between the CMIP5 simulated historical jet position and projected changes 626  

of jet position in the future under RCP 8.5 for winter and spring. The four colored triangles 627  

indicate the jet position from the four reanalysis datasets. The multi-model mean historical jet 628  

positions and changes of jet positions are indicated by the grey dashed lines. The expected 629  

change of jet position from the four reanalysis datasets are the value at the intersection between 630  

the colored solid lines and the regression line. 631  

632  

633  

 634  

34    

635  

Figure 7. Similar to Figure 6 but for correlation between the historical jet position and the 636  

changes of AR days in winter at two latitudinal bins due to dynamical effects. 637  

638  

639  

640  

35    

641  

Figure 8. The fractional contribution of AR induced precipitation to the total precipitation in 642  

each season from ERA-Interim (1979-2004; top row), CMIP5 MME at present (1975-2004; 643  

middle row), and the difference between CMIP5 MME in RCP 8.5 and present (2070-2099 644  

minus 1975-2004; bottom row). 645  

36    

646  

Figure 9. The same as Figure 8 but for extreme precipitation. 647  

37    

648  

Figure 10. Percentage change in winter AR total precipitation (a), extreme precipitation (b), total 649  

IVT (c), and extreme IVT (d) from the CMIP5 MME comparing the present (1975-2004) with 650  

the future (2070-2099). 651  

 652  

38    

653  

Figure 11. Same as Figure 10, but for intensity of AR total precipitation (a), extreme 654  

precipitation (b), IVT (c), and extreme IVT (d). 655  

 656  

657