Simulating the Pineapple Express in the Half Degree Community Climate

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Geophysical Research Letters

RESEARCH LETTER Simulating the Pineapple Express in the half degree 10.1002/2016GL069476 Community Climate System Model, CCSM4 Key Points: Christine A. Shields1 and Jeffrey T. Kiehl1 • CCSM4 accurately simulates the fi Paci c atmospheric river called the 1National Center for Atmospheric Research, Boulder, Colorado, USA Pineapple Express • Under greenhouse warming the duration of PE storms is projected to significantly increase Abstract Atmospheric rivers are recognized as major contributors to the poleward transport of water • Under greenhouse warming the vapor. Upon reaching land, these phenomena also play a critical role in extreme precipitation and flooding intensity of PE storms in also projected events. The Pineapple Express (PE) is defined as an atmospheric river extending out of the deep tropics and to increase reaching the west coast of North America. Community Climate System Model (CCSM4) high-resolution ensemble simulations for the twentieth and 21st centuries are diagnosed to identify the PE. Analysis of the twentieth century simulations indicated that the CCSM4 accurately captures the spatial and temporal Correspondence to: fi C. A. Shields, climatology of the PE. Analysis of the end 21st century simulations indicates a signi cant increase in storm [email protected] duration and intensity of precipitation associated with landfall of the PE. Only a modest increase in the number of atmospheric rivers of a few percent is projected for the end of 21st century.

Citation: Shields, C. A., and J. T. Kiehl (2016), Simulating the Pineapple Express in the 1. Introduction half degree Community Climate System ’ Model, CCSM4, Geophys. Res. Lett., 43, The transport of moisture from lower latitudes to middle and high latitudes plays a critical role in Earth s 7767–7773, doi:10.1002/2016GL069476. water and energy cycles. Newell and colleagues [Newell et al., 1992; Newell and Zhu, 1994; Zhu and Newell, 1998] pointed out an important role that transient filaments of water vapor extending out of the Received 10 NOV 2015 subtropics play in the overall transport of vapor. They found that these atmospheric rivers although cover- Accepted 12 JUL 2016 Accepted article online 15 JUL 2016 ing only 10% in area at any given latitude explain 90% of the poleward transport of moisture. Since this Published online 30 JUL 2016 early work, a number of studies [e.g., Ralph et al., 2004, 2005, 2006; Neiman et al., 2008, 2011, 2012; Lavers et al., 2011; Guan et al., 2012; Gimeno et al., 2014] have documented the dynamical and hydrological importance atmospheric rivers play in the climate system. Of equal importance is the role atmospheric rivers (ARs) play in extreme precipitation events [e.g., Dettinger et al., 2011; Lavers et al., 2012; Lavers and Villarini, 2013; Warner et al., 2015]. Thus, understanding the meteorological, climatological, and hydrological dimensions of atmospheric rivers is critical to improved understanding of the water cycle. In addition, given that Earth is warming due to increases in greenhouse gases, it is critical to understand how the characteristics of atmospheric rivers may change in the warming world. Of equal importance is considering how the atmospheric river may change due to greenhouse warming. Dettinger [2011], Dettinger and Ingram [2013], Lavers et al. [2013], Lavers et al. [2015], and Warner et al. [2015] have explored changes in ARs due to future greenhouse warming. The general finding of these studies is that the frequency of ARs will increase under the conditions of warming, wherein most of the simulated change in the models arises from an increase in atmospheric water vapor through the Clausisus-Clapeyron relationship. However, [Warner et al., 2015] finds that although mean precipitation more closely follows Clausius-Clapeyron in Coupled Model Intercomparison Project Phase 5 models, extreme precipitation is projected to surpasses this rate for the 99th percentile events. Here we explore how well the Community Climate System Model (CCSM4) is able to simulate the statistical characteristics of atmospheric rivers and how ARs may change under a greenhouse warming scenario for the future. The CCSM4 is a global fully coupled climate model and, as such, simulates ARs for many regions. The purpose of this study is to focus on the so-called Pineapple Express (PE) form of ARs. A future, more extensive study will explore simulated ARs for other regions of the world. As noted by Dettinger [2004], the Pineapple Express atmospheric river plays an important role in California’s regional hydrologic cycle. One of the unique features of this study is the use of moderately high horizontal resolution simulations to characterize the ARs. We consider simulations carried out at a horizontal resolution of approximately 50 km

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The paper is organized in the following way, section 2 describes the CCSM4, the reanalysis data and the methodology used to identify the PE, section 3 explores the results of the study, and section 4 presents the conclusions.

2. Model Description and Methodology The Community Climate System Model Version 4 (CCSM4) [Gent et al., 2011] is a fully coupled atmospheric, dynamic ocean, sea ice, and land model. The atmospheric component is the Community Atmosphere Model (CAM4) with a finite volume dynamical core run at 0.5° horizontal resolution with 26 vertical layers. The ocean model uses the Parallel Ocean Program (version 2) and includes parameterizations for subgrid ocean eddies and vertical mixing (see Gent et al. for details). The ocean model resolution is nominally 1°. The land model is the Community Land Model version 4, run at 0.5° resolution. The CCSM4 sea ice model is based on the Community Ice Code version 4 described in Hunke and Lipscomb [2008]. The 0.5°, or “half” degree, resolution utilizes a finer grid than typically used for the CCSM4 system and better represents extreme precipitation [Shields et al., 2016; Gent et al., 2009]. Historical and Representative Concentration Pathways (RCP8.5) scenario ensemble suites were used for this study. Of the five ensemble members per suite, three members produced 6-hourly output data required for the AR detection algorithm. Precipitation statistics were computed using daily data. Cataloguing AR events in climate simulations requires an automated AR-identification algorithm that captures the spatial and temporal structures of these events. Algorithms may vary in complexity but most neces- sitate the inclusion of water vapor content or transport. Other techniques, such as self-organizing maps (SOMs), rely on pattern recognition or clustering software. The adoption of observationally based empirical threshold values (i.e., integrated water vapor content > 2 cm applied in Ralph et al. [2004], Neiman et al. [2005], and Wick et al. [2013]) may not translate well to future climate projections as increased temperatures change the background water vapor content; therefore, here we apply a moisture threshold methodology described in Zhu and Newell [1998], (henceforth called ZN), which computes unique threshold values for each 6 h period relative to the background climate state and are thus more applicable to future warmer climate states [Newman et al., 2012]. With the focus on the phenomenon of the PE, we concentrate on grid points from 35 to 41 N for California statistics. To validate the latitude of all land-falling ARs along the entire West Coast, 32°N–52°N was used. Only storms with total vertically integrated precipitable water exceeding or equal to the threshold value determined by the ZN approach were considered. To capture the atmospheric river shape, we search for a grid point ratio (dy/dx) > 2 with four grid points minimum in length and a two grid points minimum in width. We focus on atmospheric rivers approaching from a southwesterly direction [Dettinger, 2004] by requiring the 850 mb wind vectors to have values between 180° and 270° with a minimum speed of 10 m/s. Although we only discuss ARs captured with the ZN method, we have tested a wide variety of threshold algorithms ranging from observationally based values to other methods that use spatial and temporal anomalies values. Our examination shows that the ZN method best captures the observationally based ERA-Interim data (resolution 0.7 × 0.7°) [Dee et al., 2011] in seasonal and latitudinal distribution as well as being able to correctly isolate individual storms.

3. Results The focus of this study is on the CCSM4’s ability to simulate the statistical characteristics of the PE. The climate model simulations are simulations of the twentieth and 21st century climate states assuming time-varying greenhouse gas and aerosol conditions representative of these two centuries. Applying the Pineapple Express (PE) atmospheric river detection algorithm to the 6-hourly output from the three CCSM4 simulations provides a description of the spatial structure of the PE as shown in Figure 1a, where the ensemble and composite mean of the three simulations is presented. The 6-hourly temporal standard deviation of the simulated PEs is shown in Figure 1b, again for the ensemble and composite mean of the three simulations. Applying the same detection algorithm to the ERA-Interim data results in the PE spatial structure shown in Figure 1c and the temporal standard deviation shown in Figure 1d. Comparison between simulated and observed PE structure indicates the CCSM4 is able to realistically capture the PE. The model slightly overestimates the magnitude of column-integrated water vapor near 130°W, 30°N, otherwise the structure is well simulated by the model despite the positive bias over much of the subtropical domain. In

SHIELDS AND KIEHL SIMULATING PINEAPPLE EXPRESS CCSM4 7768 Geophysical Research Letters 10.1002/2016GL069476

Figure 1. The 1980–2005 vertically integrated water vapor (kg/m2) for (a) composite AR events, ensemble average CCSM simulations, (b) 6-hourly variance, (c) ERA-Interim, and (d) ERA-Interim 6-hourly variance. Composites means were computed using all AR events using 6-hourly data. Black dots note the speciﬁc locations used in Figure 4.

terms of the intracomposite variability, the CCSM4 displays a broader latitudinal structure in variation around 180° compared to the reanalysis and can be attributed to the variance of the individual AR events impacting the California coast from a variety of southwesterly directions. The CCSM4 realistically captures the seasonal cycle in frequency of occurrence of PEs (Figure 2a) with the overall maximum occurring in December through February. Dettinger [2004] and Neiman et al. [2008] find the peak in PEs reaching the coast of California to occur in December through February, which is supported by our analysis of the ERA-Interim. In general, the CCSM4 overestimates the frequency of the PE for each month, with a maximum bias in April. The projected change in the number of PE storms (Figure 2b) under the RCP8.5 warming scenario indicates modest changes. Results from the three ensemble members indicate a robust increase of a few percent. Given the overestimate of frequency of PEs in April, the simulated decrease at the end of this century is questionable. This relatively small change in PEs may seem surprising given the increase in column-integrated water vapor in the warmer world. Analysis of the CCSM4 simulations indicates a modest (~10%) increase in wind magnitude reaching the California coast from the deep tropics. This modest increase in wind speed coupled with the increase in atmospheric water vapor leads to more integrated water vapor transport along the California coast. Note that this increase in wind speed directed toward the coast is only significant during December through February, which explains why there is no discernable change in PEs during the other months of the year. Finally, we consider the latitudinal of landfall of the PEs (Figure 2c). The model accurately simulates the fraction of PEs landfall position along the coast of California while underestimating the number of atmospheric rivers impacting the Pacific Northwest region. Synoptically, atmospheric rivers make landfall and impact the coast for a specific length of time. [Ralph et al., 2013, 2011; Rutz et al., 2014] We compute storm duration by identifying the number of consecutive 6 h per- iods for each landfalling AR event. The simulated storm duration count for the period 1980 to 2005 is in agreement with than contained in the ERA-Interim analysis product. The propensity of storms (Figure 3a) lasts less than 24 h, with a long tail out to ~ 3 days. There is a significant increase (~80%) in the number of long-lived storms under the RCP8.5 warming scenario (Figure 3b). There is also a significant decrease in the number of short-lived (less than 18 h) storms for the warmer conditions of the end of this century. This result is most likely due to lower zonal wind speeds along the coast, which results in storms persisting for a longer time over these regions. Dettinger et al. [2011] (Figure 6) find that approximately 30 to 45% of total precipitation in California is due to the PE. We find that on average ~36% of the total precipitation arises from the PE for California within our specified region (35°N–41°N). To compute this percentage figure, we sum precipitation across all grid points in our regional box, for the all days between 1960 and 2005 and then repeat this procedure for AR-only days. Only grid points with rate values of greater than 1 mm/d are included to eliminate trace rain events. Thus, the model simulates PE precipitation that lies within the observational estimates. Numerous studies have linked extreme precipitation events to the landfall of atmospheric rivers [Ralph and Dettinger, 2011, 2012; Lavers and Villarini, 2013]. We consider four locations along the California coast (see points denoted on Figure 1).

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Figure 2. (a) Seasonal cycle of the ensemble mean simulated Pineapple Express (solid) and ERA-Interim reanalysis (hatched) 1980–2005. (b) The 21st century to twentieth century changes to the seasonal cycle for simulated PE for three ensemble members (number of storms/5 year period). (c) Latitudinal range of PE landfall for the ensemble mean simulated Pineapple Express (solid) and ERA-Interim reanalysis (hatched). Ensemble spread is shown with black line overlays. Maximum and minimum ensemble members are plotted as stars.

À A comparison of the frequency distribution of precipitation rate (mm d 1) indicates a significant shift in intense rainfall (Figures 4a–4d) for the four landfall locations due to greenhouse warming. There is a clear difference between the three ensemble members at the end twentieth century compared to the ensemble members at the end of this century under the RCP8.5 scenario. Increases by more than a factor of 3 occurs À for precipitation rates higher than 50 mm d 1. The implication of these results is a significant increase in flooding associated with the PE atmospheric rivers.

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Figure 3. Frequency distribution of (a) 1980–2005 CCSM4 ensemble mean PE storm duration (hours) (green solid) and 1980–2005 ERA-Interim reanalysis (hatched), (b) 1960–2005 CCSM4 ensemble mean PE storm duration (hours) (green) and 2055–2100 CCSM4 ensemble mean PE storm duration (hours) (red). Ensemble spread is shown with black line overlays. Maximum and minimum ensemble members are plotted as stars.

We also consider the role of natural variability in modulating the statistical characteristics. We find strong correlation between the model simulated El Niño–Southern Oscillation pattern and AR-associated precipitation events. California precipitation was regressed against the monthly Niño3.4 index for months where ARs occurred and found positive values with 95% statistical significance with the strongest correlations over the mountains. The model is also able to reproduce the observed correlation between the phase of the Pacific North America pattern (PNA) and PE-associated precipitation, in which during the negative phase of the PNA, atmospheric rivers are enhanced [Guan et al., 2013].

4. Conclusions This study presents an analysis of the CCSM4’s ability to accurately simulate the atmospheric river called the Pineapple Express (PE) and how this atmospheric phenomenon may change under the conditions of future greenhouse warming. The CCSM4 is able to realistically simulate the mean spatial structure of the PE, as well as the variance in the structure. The model also captures the seasonal cycle of the PE with peak frequency occurring in December through February. Storm duration statistics for ARs that reach the California coast are well simulated when compared to ERA-Interim data. In terms of changes in the PE due to increased levels of greenhouse gases, we ﬁnd small changes in the frequency of occurrence in the PE of order of a few percent. The most signiﬁcant change occurs in increased storm duration and more intense precipitation associated with these events. Future research will focus on atmospheric rivers that reach the coasts of the United Kingdom and Europe.

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Acknowledgments NCAR and CESM are sponsored by the National Science Foundation (NSF). We would like to thank Adrianne Middleton, Frederic Castruccio, and Joanie Kleypas for their contributions in helping complete the half-degree CCSM4 simulation suite. Data from all À simulations are available through the Figure 4. Frequency of CCSM4 simulated precipitation rate (mm d 1) for the end twentieth century (black) and end 21st Earth System Grid. Please direct Hdeg century (red) 90th percentile California AR precipitation. The horizontal lines inside the boxplots represent maximum, data requests to [email protected]. Portions of this study were supported median, and minimum values. Daily data are used. by the Regional and Global Climate Modeling Program (RGCM) of the U.S. Department of Energy’sOffice of References Biological & Environmental Research Dee, D. P., et al. (2011), The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. R. Meteorol. Soc., (BER) Cooperative Agreement DE-FC02- 137(656), 553–597. 97ER62402, and the National Science Dettinger, M. (2004), Fifty-two years of Pineapple-Express storms across the west coast of North America California Energy Commission PIER Foundation. Computing resources (ark:/ Energy-Related Environmental Research Report CEC-500-2005-004, Sacramento, Calif., 15 pp. 85065/d7wd3xhc) were provided by the Dettinger, M. D. (2011), Climate change, atmospheric rivers, and floods in California—A multimodel analysis of storm frequency and Climate Simulation Laboratory at magnitude changes, J. Am. Water Resour. Assoc., 47(3), 514–523. NCAR’s Computational and Information Dettinger, M. D., and B. L. Ingram (2013), The coming megafloods, Sci. Am., 308,64–71. Systems Laboratory, sponsored by the Dettinger, M. D., F. M. Ralph, T. Das, P. J. Neiman, and D. Cayan (2011), Atmospheric rivers, floods, and the water resources of California, Water, National Science Foundation and other 3, 455–478. agencies. An award of computer time Gent, P. R., S. G. Yeager, R. B. Neale, S. Levis, and D. A. Bailey (2009), Improvements in a half degree atmosphere/land version of the CCSM, was also provided by the Innovative and Clim. Dyn., 34, 819–833, doi:10.1007/s00382-009-0614-8. Novel Computational Impact on Theory Gent, P. R., et al. (2011), The Community Climate System Model version 4, J. Clim., 24, 4973–4991. and Experiment (INCITE) program and Gimeno, L., R. Nieto, M. Vazquez, and D. A. Lavers (2014), Atmospheric rivers: A mini-review, Front. Earth Sci., 2(2), 1–5. used the resources of Oak Ridge Guan, B., D. E. Waliser, N. P. Molotch, E. J. Fetzer, and P. J. Neiman (2012), Does the Madden–Julian Oscillation influence wintertime Leadership Computing Facility located atmospheric rivers and snowpack in the Sierra Nevada?, Mon. Weather Rev., 140, 325–342, doi:10.1175/MWR-D-11-00087.1. in the Oak Ridge National Laboratory, Guan, B., N. P. Molotch, D. E. Waliser, E. J. Fetzer, and P. J. Neiman (2013), The 2010/2011 snow season in California’s Sierra Nevada: Role of which is supported by the Office of atmospheric rivers and modes of large-scale variability, Water Resour. Res., 49, 6731–6743, doi:10.1002/wrcr.20537. Science of the Department of Energy Hunke, E. C., and W. H. Lipscomb (2008), CICE: The Los Alamos sea ice model user’s manual, version 4 Los Alamos Natl. Lab. Tech. Rep. under contract DE-AC05-00OR22725. LA-CC-06-012, 76 pp.

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